Tag Archives: Technical How-to

Run interactive workloads on Amazon EMR Serverless from Amazon EMR Studio

Post Syndicated from Sekar Srinivasan original https://aws.amazon.com/blogs/big-data/run-interactive-workloads-on-amazon-emr-serverless-from-amazon-emr-studio/

Starting from release 6.14, Amazon EMR Studio supports interactive analytics on Amazon EMR Serverless. You can now use EMR Serverless applications as the compute, in addition to Amazon EMR on EC2 clusters and Amazon EMR on EKS virtual clusters, to run JupyterLab notebooks from EMR Studio Workspaces.

EMR Studio is an integrated development environment (IDE) that makes it straightforward for data scientists and data engineers to develop, visualize, and debug analytics applications written in PySpark, Python, and Scala. EMR Serverless is a serverless option for Amazon EMR that makes it straightforward to run open source big data analytics frameworks such as Apache Spark without configuring, managing, and scaling clusters or servers.

In the post, we demonstrate how to do the following:

  • Create an EMR Serverless endpoint for interactive applications
  • Attach the endpoint to an existing EMR Studio environment
  • Create a notebook and run an interactive application
  • Seamlessly diagnose interactive applications from within EMR Studio

Prerequisites

In a typical organization, an AWS account administrator will set up AWS resources such as AWS Identity and Access management (IAM) roles, Amazon Simple Storage Service (Amazon S3) buckets, and Amazon Virtual Private Cloud (Amazon VPC) resources for internet access and access to other resources in the VPC. They assign EMR Studio administrators who manage setting up EMR Studios and assigning users to a specific EMR Studio. Once they’re assigned, EMR Studio developers can use EMR Studio to develop and monitor workloads.

Make sure you set up resources like your S3 bucket, VPC subnets, and EMR Studio in the same AWS Region.

Complete the following steps to deploy these prerequisites:

  1. Launch the following AWS CloudFormation stack.
    Launch Cloudformation Stack
  2. Enter values for AdminPassword and DevPassword and make a note of the passwords you create.
  3. Choose Next.
  4. Keep the settings as default and choose Next again.
  5. Select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  6. Choose Submit.

We have also provided instructions to deploy these resources manually with sample IAM policies in the GitHub repo.

Set up EMR Studio and a serverless interactive application

After the AWS account administrator completes the prerequisites, the EMR Studio administrator can log in to the AWS Management Console to create an EMR Studio, Workspace, and EMR Serverless application.

Create an EMR Studio and Workspace

The EMR Studio administrator should log in to the console using the emrs-interactive-app-admin-user user credentials. If you deployed the prerequisite resources using the provided CloudFormation template, use the password that you provided as an input parameter.

  1. On the Amazon EMR console, choose EMR Serverless in the navigation pane.
  2. Choose Get started.
  3. Select Create and launch EMR Studio.

This creates a Studio with the default name studio_1 and a Workspace with the default name My_First_Workspace. A new browser tab will open for the Studio_1 user interface.

Create and Launch EMR Studio

Create an EMR Serverless application

Complete the following steps to create an EMR Serverless application:

  1. On the EMR Studio console, choose Applications in the navigation pane.
  2. Create a new application.
  3. For Name, enter a name (for example, my-serverless-interactive-application).
  4. For Application setup options, select Use custom settings for interactive workloads.
    Create Serverless Application using custom settings

For interactive applications, as a best practice, we recommend keeping the driver and workers pre-initialized by configuring the pre-initialized capacity at the time of application creation. This effectively creates a warm pool of workers for an application and keeps the resources ready to be consumed, enabling the application to respond in seconds. For further best practices for creating EMR Serverless applications, see Define per-team resource limits for big data workloads using Amazon EMR Serverless.

  1. In the Interactive endpoint section, select Enable Interactive endpoint.
  2. In the Network connections section, choose the VPC, private subnets, and security group you created previously.

If you deployed the CloudFormation stack provided in this post, choose emr-serverless-sg­  as the security group.

A VPC is needed for the workload to be able to access the internet from within the EMR Serverless application in order to download external Python packages. The VPC also allows you to access resources such as Amazon Relational Database Service (Amazon RDS) and Amazon Redshift that are in the VPC from this application. Attaching a serverless application to a VPC can lead to IP exhaustion in the subnet, so make sure there are sufficient IP addresses in your subnet.

  1. Choose Create and start application.

Enable Interactive Endpoints, Choose private subnets and security group

On the applications page, you can verify that the status of your serverless application changes to Started.

  1. Select your application and choose How it works.
  2. Choose View and launch workspaces.
  3. Choose Configure studio.

  1. For Service role¸ provide the EMR Studio service role you created as a prerequisite (emr-studio-service-role).
  2. For Workspace storage, enter the path of the S3 bucket you created as a prerequisite (emrserverless-interactive-blog-<account-id>-<region-name>).
  3. Choose Save changes.

Choose emr-studio-service-role and emrserverless-interactive-blog s3 bucket

14.  Navigate to the Studios console by choosing Studios in the left navigation menu in the EMR Studio section. Note the Studio access URL from the Studios console and provide it to your developers to run their Spark applications.

Run your first Spark application

After the EMR Studio administrator has created the Studio, Workspace, and serverless application, the Studio user can use the Workspace and application to develop and monitor Spark workloads.

Launch the Workspace and attach the serverless application

Complete the following steps:

  1. Using the Studio URL provided by the EMR Studio administrator, log in using the emrs-interactive-app-dev-user user credentials shared by the AWS account admin.

If you deployed the prerequisite resources using the provided CloudFormation template, use the password that you provided as an input parameter.

On the Workspaces page, you can check the status of your Workspace. When the Workspace is launched, you will see the status change to Ready.

  1. Launch the workspace by choosing the workspace name (My_First_Workspace).

This will open a new tab. Make sure your browser allows pop-ups.

  1. In the Workspace, choose Compute (cluster icon) in the navigation pane.
  2. For EMR Serverless application, choose your application (my-serverless-interactive-application).
  3. For Interactive runtime role, choose an interactive runtime role (for this post, we use emr-serverless-runtime-role).
  4. Choose Attach to attach the serverless application as the compute type for all the notebooks in this Workspace.

Choose my-serverless-interactive-application as your app and emr-serverless-runtime-role and attach

Run your Spark application interactively

Complete the following steps:

  1. Choose the Notebook samples (three dots icon) in the navigation pane and open Getting-started-with-emr-serverless notebook.
  2. Choose Save to Workspace.

There are three choices of kernels for our notebook: Python 3, PySpark, and Spark (for Scala).

  1. When prompted, choose PySpark as the kernel.
  2. Choose Select.

Choose PySpark as kernel

Now you can run your Spark application. To do so, use the %%configure Sparkmagic command, which configures the session creation parameters. Interactive applications support Python virtual environments. We use a custom environment in the worker nodes by specifying a path for a different Python runtime for the executor environment using spark.executorEnv.PYSPARK_PYTHON. See the following code:

%%configure -f
{
  "conf": {
    "spark.pyspark.virtualenv.enabled": "true",
    "spark.pyspark.virtualenv.bin.path": "/usr/bin/virtualenv",
    "spark.pyspark.virtualenv.type": "native",
    "spark.pyspark.python": "/usr/bin/python3",
    "spark.executorEnv.PYSPARK_PYTHON": "/usr/bin/python3"
  }
}

Install external packages

Now that you have an independent virtual environment for the workers, EMR Studio notebooks allow you to install external packages from within the serverless application by using the Spark install_pypi_package function through the Spark context. Using this function makes the package available for all the EMR Serverless workers.

First, install matplotlib, a Python package, from PyPi:

sc.install_pypi_package("matplotlib")

If the preceding step doesn’t respond, check your VPC setup and make sure it is configured correctly for internet access.

Now you can use a dataset and visualize your data.

Create visualizations

To create visualizations, we use a public dataset on NYC yellow taxis:

file_name = "s3://athena-examples-us-east-1/notebooks/yellow_tripdata_2016-01.parquet"
taxi_df = (spark.read.format("parquet").option("header", "true") \
.option("inferSchema", "true").load(file_name))

In the preceding code block, you read the Parquet file from a public bucket in Amazon S3. The file has headers, and we want Spark to infer the schema. You then use a Spark dataframe to group and count specific columns from taxi_df:

taxi1_df = taxi_df.groupBy("VendorID", "passenger_count").count()
taxi1_df.show()

Use %%display magic to view the result in table format:

%%display
taxi1_df

Table shows vendor_id, passenger_count and count columns

You can also quickly visualize your data with five types of charts. You can choose the display type and the chart will change accordingly. In the following screenshot, we use a bar chart to visualize our data.

bar chart showing passenger_count against each vendor_id

Interact with EMR Serverless using Spark SQL

You can interact with tables in the AWS Glue Data Catalog using Spark SQL on EMR Serverless. In the sample notebook, we show how you can transform data using a Spark dataframe.

First, create a new temporary view called taxis. This allows you to use Spark SQL to select data from this view. Then create a taxi dataframe for further processing:

taxi_df.createOrReplaceTempView("taxis")
sqlDF = spark.sql(
    "SELECT DOLocationID, sum(total_amount) as sum_total_amount \
     FROM taxis where DOLocationID < 25 Group by DOLocationID ORDER BY DOLocationID"
)
sqlDF.show(5)

Table shows vendor_id, passenger_count and count columns

In each cell in your EMR Studio notebook, you can expand Spark Job Progress to view the various stages of the job submitted to EMR Serverless while running this specific cell. You can see the time taken to complete each stage. In the following example, stage 14 of the job has 12 completed tasks. In addition, if there is any failure, you can see the logs, making troubleshooting a seamless experience. We discuss this more in the next section.

Job[14]: showString at NativeMethodAccessorImpl.java:0 and Job[15]: showString at NativeMethodAccessorImpl.java:0

Use the following code to visualize the processed dataframe using the matplotlib package. You use the maptplotlib library to plot the dropoff location and the total amount as a bar chart.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.clf()
df = sqlDF.toPandas()
plt.bar(df.DOLocationID, df.sum_total_amount)
%matplot plt

Diagnose interactive applications

You can get the session information for your Livy endpoint using the %%info Sparkmagic. This gives you links to access the Spark UI as well as the driver log right in your notebook.

The following screenshot is a driver log snippet for our application, which we opened via the link in our notebook.

driver log screenshot

Similarly, you can choose the link below Spark UI to open the UI. The following screenshot shows the Executors tab, which provides access to the driver and executor logs.

The following screenshot shows stage 14, which corresponds to the Spark SQL step we saw earlier in which we calculated the location wise sum of total taxi collections, which had been broken down into 12 tasks. Through the Spark UI, the interactive application provides fine-grained task-level status, I/O, and shuffle details, as well as links to corresponding logs for each task for this stage right from your notebook, enabling a seamless troubleshooting experience.

Clean up

If you no longer want to keep the resources created in this post, complete the following cleanup steps:

  1. Delete the EMR Serverless application.
  2. Delete the EMR Studio and the associated workspaces and notebooks.
  3. To delete rest of the resources, navigate to CloudFormation console, select the stack, and choose Delete.

All of the resources will be deleted except the S3 bucket, which has its deletion policy set to retain.

Conclusion

The post showed how to run interactive PySpark workloads in EMR Studio using EMR Serverless as the compute. You can also build and monitor Spark applications in an interactive JupyterLab Workspace.

In an upcoming post, we’ll discuss additional capabilities of EMR Serverless Interactive applications, such as:

  • Working with resources such as Amazon RDS and Amazon Redshift in your VPC (for example, for JDBC/ODBC connectivity)
  • Running transactional workloads using serverless endpoints

If this is your first time exploring EMR Studio, we recommend checking out the Amazon EMR workshops and referring to Create an EMR Studio.


About the Authors

Sekar Srinivasan is a Principal Specialist Solutions Architect at AWS focused on Data Analytics and AI. Sekar has over 20 years of experience working with data. He is passionate about helping customers build scalable solutions modernizing their architecture and generating insights from their data. In his spare time he likes to work on non-profit projects, focused on underprivileged Children’s education.

Disha Umarwani is a Sr. Data Architect with Amazon Professional Services within Global Health Care and LifeSciences. She has worked with customers to design, architect and implement Data Strategy at scale. She specializes in architecting Data Mesh architectures for Enterprise platforms.

Using Amazon Verified Permissions to manage authorization for AWS IoT smart home applications

Post Syndicated from Rajat Mathur original https://aws.amazon.com/blogs/security/using-amazon-verified-permissions-to-manage-authorization-for-aws-iot-smart-thermostat-applications/

This blog post introduces how manufacturers and smart appliance consumers can use Amazon Verified Permissions to centrally manage permissions and fine-grained authorizations. Developers can offer more intuitive, user-friendly experiences by designing interfaces that align with user personas and multi-tenancy authorization strategies, which can lead to higher user satisfaction and adoption. Traditionally, implementing authorization logic using role based access control (RBAC) or attribute based access control (ABAC) within IoT applications can become complex as the number of connected devices and associated user roles grows. This often leads to an unmanageable increase in access rules that must be hard-coded into each application, requiring excessive compute power for evaluation. By using Verified Permissions, you can externalize the authorization logic using Cedar policy language, enabling you to define fine-grained permissions that combine RBAC and ABAC models. This decouples permissions from your application’s business logic, providing a centralized and scalable way to manage authorization while reducing development effort.

In this post, we walk you through a reference architecture that outlines an end-to-end smart thermostat application solution using AWS IoT Core, Verified Permissions, and other AWS services. We show you how to use Verified Permissions to build an authorization solution using Cedar policy language to define dynamic policy-based access controls for different user personas. The post includes a link to a GitHub repository that houses the code for the web dashboard and the Verified Permissions logic to control access to the solution APIs.

Solution overview

This solution consists of a smart thermostat IoT device and an AWS hosted web application using Verified Permissions for fine-grained access to various application APIs. For this use case, the AWS IoT Core device is being simulated by an AWS Cloud9 environment and communicates with the IoT service using AWS IoT Device SDK for Python. After being configured, the device connects to AWS IoT Core to receive commands and send messages to various MQTT topics.

As a general practice, when a user-facing IoT solution is implemented, the manufacturer performs administrative tasks such as:

  1. Embedding AWS Private Certificate Authority certificates into each IoT device (in this case a smart thermostat). Usually this is done on the assembly line and the certificates used to verify the IoT endpoints are burned into device memory along with the firmware.
  2. Creating an Amazon Cognito user pool that provides sign-up and sign-in options for web and mobile application users and hosts the authentication process.
  3. Creating policy stores and policy templates in Verified Permissions. Based on who signs up, the manufacturer creates policies with Verified Permissions to link each signed-up user to certain allowed resources or IoT devices.
  4. The mapping of user to device is stored in a datastore. For this solution, you’ll use an Amazon DynamoDB table to record the relationship.

The user who purchases the device (the primary device owner) performs the following tasks:

  1. Signs up on the manufacturer’s web application or mobile app and registers the IoT device by entering a unique serial number. The mapping between user details and the device serial number is stored in the datastore through an automated process that is initiated after sign-up and device claim.
  2. Connects the new device to an existing wireless network, which initiates a registration process to securely connect to AWS IoT Core services within the manufacturer’s account.
  3. Invites other users (such as guests, family members, or the power company) through a referral, invitation link, or a designated OAuth process.
  4. Assign roles to the other users and therefore permissions.
     
Figure 1: Sample smart home application architecture built using AWS services

Figure 1: Sample smart home application architecture built using AWS services

Figure 1 depicts the solution as three logical components:

  1. The first component depicts device operations through AWS IoT Core. The smart thermostat is on site and it communicates with AWS IoT Core and its state is managed through the AWS IoT Device Shadow Service.
  2. The second component depicts the web application, which is the application interface that customers use. It’s a ReactJS-backed single page application deployed using AWS Amplify.
  3. The third component shows the backend application, which is built using Amazon API Gateway, AWS Lambda, and DynamoDB. A Cognito user pool is used to manage application users and their authentication. Authorization is handled by Verified Permissions where you create and manage policies that are evaluated when the web application calls backend APIs. These policies are evaluated against each authorization policy to provide an access decision to deny or allow an action.

The solution flow itself can be broken down into three steps after the device is onboarded and users have signed up:

  1. The smart thermostat device connects and communicates with AWS IoT Core using the MQTT protocol. A classic Device Shadow is created for the AWS IoT thing Thermostat1 when the UpdateThingShadow call is made the first time through the AWS SDK for a new device. AWS IoT Device Shadow service lets the web application query and update the device’s state in case of connectivity issues.
  2. Users sign up or sign in to the Amplify hosted smart home application and authenticate themselves against a Cognito user pool. They’re mapped to a device, which is stored in a DynamoDB table.
  3. After the users sign in, they’re allowed to perform certain tasks and view certain sections of the dashboard based on the different roles and policies managed by Verified Permissions. The underlying Lambda function that’s responsible for handling the API calls queries the DynamoDB table to provide user context to Verified Permissions.

Prerequisites

  1. To deploy this solution, you need access to the AWS Management Console and AWS Command Line Interface (AWS CLI) on your local machine with sufficient permissions to access required services, including Amplify, Verified Permissions, and AWS IoT Core. For this solution, you’ll give the services full access to interact with different underlying services. But in production, we recommend following security best practices with AWS Identity and Access Management (IAM), which involves scoping down policies.
  2. Set up Amplify CLI by following these instructions. We recommend the latest NodeJS stable long-term support (LTS) version. At the time of publishing this post, the LTS version was v20.11.1. Users can manage multiple NodeJS versions on their machines by using a tool such as Node Version Manager (nvm).

Walkthrough

The following table describes the actions, resources, and authorization decisions that will be enforced through Verified Permissions policies to achieve fine-grained access control. In this example, John is the primary device owner and has purchased and provisioned a new smart thermostat device called Thermostat1. He has invited Jane to access his device and has given her restricted permissions. John has full control over the device whereas Jane is only allowed to read the temperature and set the temperature between 72°F and 78°F.

John has also decided to give his local energy provider (Power Company) access to the device so that they can set the optimum temperature during the day to manage grid load and offer him maximum savings on his energy bill. However, they can only do so between 2:00 PM and 5:00 PM.

For security purposes the verified permissions default decision is DENY for unauthorized principals.

Name Principal Action Resource Authorization decision
Any Default Default Default Deny
John john_doe Any Thermostat1 Allow
Jane jane_doe GetTemperature Thermostat1 Allow
Jane jane_doe SetTemperature Thermostat1 Allow only if desired temperature is between 72°F and 78°F.
Power Company powercompany GetTemperature Thermostat1 Allow only if accessed between the hours of 2:00 PM and 5:00 PM
Power Company powercompany SetTemperature Thermostat1 Allow only if the temperature is set between the hours of 2:00 PM and 5:00 PM

Create a Verified Permissions policy store

Verified Permissions is a scalable permissions management and fine-grained authorization service for the applications that you build. The policies are created using Cedar, a dedicated language for defining access permissions in applications. Cedar seamlessly integrates with popular authorization models such as RBAC and ABAC.

A policy is a statement that either permits or forbids a principal to take one or more actions on a resource. A policy store is a logical container that stores your Cedar policies, schema, and principal sources. A schema helps you to validate your policy and identify errors based on the definitions you specify. See Cedar schema to learn about the structure and formal grammar of a Cedar schema.

To create the policy store

  1. Sign in to the Amazon Verified Permissions console and choose Create policy store.
  2. In the Configuration Method section, select Empty Policy Store and choose Create policy store.
     
Figure 2: Create an empty policy store

Figure 2: Create an empty policy store

Note: Make a note of the policy store ID to use when you deploy the solution.

To create a schema for the application

  1. On the Verified Permissions page, select Schema.
  2. In the Schema section, choose Create schema.
     
    Figure 3: Create a schema

    Figure 3: Create a schema

  3. In the Edit schema section, choose JSON mode, paste the following sample schema for your application, and choose Save changes.
    {
        "AwsIotAvpWebApp": {
            "entityTypes": {
                "Device": {
                    "shape": {
                        "attributes": {
                            "primaryOwner": {
                                "name": "User",
                                "required": true,
                                "type": "Entity"
                            }
                        },
                        "type": "Record"
                    },
                    "memberOfTypes": []
                },
                "User": {}
            },
            "actions": {
                "GetTemperature": {
                    "appliesTo": {
                        "context": {
                            "attributes": {
                                "desiredTemperature": {
                                    "type": "Long"
                                },
                                "time": {
                                    "type": "Long"
                                }
                            },
                            "type": "Record"
                        },
                        "resourceTypes": [
                            "Device"
                        ],
                        "principalTypes": [
                            "User"
                        ]
                    }
                },
                "SetTemperature": {
                    "appliesTo": {
                        "resourceTypes": [
                            "Device"
                        ],
                        "principalTypes": [
                            "User"
                        ],
                        "context": {
                            "attributes": {
                                "desiredTemperature": {
                                    "type": "Long"
                                },
                                "time": {
                                    "type": "Long"
                                }
                            },
                            "type": "Record"
                        }
                    }
                }
            }
        }
    }

When creating policies in Cedar, you can define authorization rules using a static policy or a template-linked policy.

Static policies

In scenarios where a policy explicitly defines both the principal and the resource, the policy is categorized as a static policy. These policies are immediately applicable for authorization decisions, as they are fully defined and ready for implementation.

Template-linked policies

On the other hand, there are situations where a single set of authorization rules needs to be applied across a variety of principals and resources. Consider an IoT application where actions such as SetTemperature and GetTemperature must be permitted for specific devices. Using static policies for each unique combination of principal and resource can lead to an excessive number of almost identical policies, differing only in their principal and resource components. This redundancy can be efficiently addressed with policy templates. Policy templates allow for the creation of policies using placeholders for the principal, the resource, or both. After a policy template is established, individual policies can be generated by referencing this template and specifying the desired principal and resource. These template-linked policies function the same as static policies, offering a streamlined and scalable solution for policy management.

To create a policy that allows access to the primary owner of the device using a static policy

  1. In the Verified Permissions console, on the left pane, select Policies, then choose Create policy and select Create static policy from the drop-down menu.
     
    Figure 4: Create static policy

    Figure 4: Create static policy

  2. Define the policy scope:
    1. Select Permit for the Policy effect.
       
      Figure 5: Define policy effect

      Figure 5: Define policy effect

    2. Select All Principals for Principals scope.
    3. Select All Resources for Resource scope.
    4. Select All Actions for Actions scope and choose Next.
       
      Figure 6: Define policy scope

      Figure 6: Define policy scope

  3. On the Details page, under Policy, paste the following full-access policy, which grants the primary owner permission to perform both SetTemperature and GetTemperature actions on the smart thermostat unconditionally. Choose Create policy.
    	permit (principal, action, resource)
    	when { resource.primaryOwner == principal };
    Figure 7: Write and review policy statement

    Figure 7: Write and review policy statement

To create a static policy to allow a guest user to read the temperature

In this example, the guest user is Jane (username: jane_doe).

  1. Create another static policy and specify the policy scope.
    1. Select Permit for the Policy effect.
       
      Figure 8: Define the policy effect

      Figure 8: Define the policy effect

    2. Select Specific principal for the Principals scope.
    3. Select AwsIotAvpWebApp::User and enter jane_doe.
       
      Figure 9: Define the policy scope

      Figure 9: Define the policy scope

    4. Select Specific resource for the Resources scope.
    5. Select AwsIotAvpWebApp::Device and enter Thermostat1.
    6. Select Specific set of actions for the Actions scope.
    7. Select GetTemperature and choose Next.
       
      Figure 10: Define resource and action scopes

      Figure 10: Define resource and action scopes

    8. Enter the Policy description: Allow jane_doe to read thermostat1.
    9. Choose Create policy.

Next, you will create reusable policy templates to manage policies efficiently. To create a policy template for a guest user with restricted temperature settings that limit the temperature range they can set to between 72°F and 78°F. In this case, the guest user is going to be Jane (username: jane_doe)

To create a reusable policy template

  1. Select Policy template and enter Guest user template as the description.
  2. Paste the following sample policy in the Policy body and choose Create policy template.
    permit (
        principal == ?principal,
        action in [AwsIotAvpWebApp::Action::"SetTemperature"],
        resource == ?resource
    )
    when { context.desiredTemperature >= 72 && context.desiredTemperature <= 78 };
Figure 11: Create guest user policy template

Figure 11: Create guest user policy template

As you can see, you don’t specify the principal and resource yet. You enter those when you create an actual policy from the policy template. The context object will be populated with the desiredTemperature property in the application and used to evaluate the decision.

You also need to create a policy template for the Power Company user with restricted time settings. Cedar policies don’t support date/time format, so you must represent 2:00 PM and 5:00 PM as elapsed minutes from midnight.

To create a policy template for the power company

  1. Select Policy template and enter Power company user template as the description.
  2. Paste the following sample policy in the Policy body and choose Create policy template.
    permit (
        principal == ?principal,
        action in [AwsIotAvpWebApp::Action::"SetTemperature", AwsIotAvpWebApp::Action::"GetTemperature"],
        resource == ?resource
    )
    when { context.time >= 840 && context.time < 1020 };

The policy templates accept the user and resource. The next step is to create a template-linked policy for Jane to set and get thermostat readings based on the Guest user template that you created earlier. For simplicity, you will manually create this policy using the Verified Permissions console. In production, application policies can be dynamically created using the Verified Permissions API.

To create a template-linked policy for a guest user

  1. In the Verified Permissions console, on the left pane, select Policies, then choose Create policy and select Create template-linked policy from the drop-down menu.
     
    Figure 12: Create new template-linked policy

    Figure 12: Create new template-linked policy

  2. Select the Guest user template and choose next.
     
    Figure 13: Select Guest user template

    Figure 13: Select Guest user template

  3. Under parameter selection:
    1. For Principal enter AwsIotAvpWebApp::User::”jane_doe”.
    2. For Resource enter AwsIotAvpWebApp::Device::”Thermostat1″.
    3. Choose Create template-linked policy.
       
      Figure 14: Create guest user template-linked policy

      Figure 14: Create guest user template-linked policy

Note that with this policy in place, jane_doe can only set the temperature of the device Thermostat1 to between 72°F and 78°F.

To create a template-linked policy for the power company user

Based on the template that was set up for power company, you now need an actual policy for it.

  1. In the Verified Permissions console, go to the left pane and select Policies, then choose Create policy and select Create template-linked policy from the drop-down menu.
  2. Select the Power company user template and choose next.
  3. Under Parameter selection, for Principal enter AwsIotAvpWebApp::User::”powercompany”, and for Resource enter AwsIotAvpWebApp::Device::”Thermostat1″, and choose Create template-linked policy.

Now that you have a set of policies in a policy store, you need to update the backend codebase to include this information and then deploy the web application using Amplify.

The policy statements in this post intentionally use human-readable values such as jane_doe and powercompany for the principal entity. This is useful when discussing general concepts but in production systems, customers should use unique and immutable values for entities. See Get the best out of Amazon Verified Permissions by using fine-grained authorization methods for more information.

Deploy the solution code from GitHub

Go to the GitHub repository to set up the Amplify web application. The repository Readme file provides detailed instructions on how to set up the web application. You will need your Verified Permissions policy store ID to deploy the application. For convenience, we’ve provided an onboarding script—deploy.sh—which you can use to deploy the application.

To deploy the application

  1. Close the repository.
    git clone https://github.com/aws-samples/amazon-verified-permissions-iot-
    amplify-smart-home-application.git

  2. Deploy the application.
    ./deploy.sh <region> <Verified Permissions Policy Store ID>

After the web dashboard has been deployed, you’ll create an IoT device using AWS IoT Core.

Create an IoT device and connect it to AWS IoT Core

With the users, policies, and templates, and the Amplify smart home application in place, you can now create a device and connect it to AWS IoT Core to complete the solution.

To create Thermostat1” device and connect it to AWS IoT Core

  1. From the left pane in the AWS IoT console, select Connect one device.
     
    Figure 15: Connect device using AWS IoT console

    Figure 15: Connect device using AWS IoT console

  2. Review how IoT Thing works and then choose Next.
     
    Figure 16: Review how IoT Thing works before proceeding

    Figure 16: Review how IoT Thing works before proceeding

  3. Choose Create a new thing and enter Thermostat1 as the Thing name and choose next.
    &bsp;
    Figure 17: Create the new IoT thing

    Figure 17: Create the new IoT thing

  4. Select Linux/macOS as the Device platform operating system and Python as the AWS IoT Core Device SDK and choose next.
     
    Figure 18: Choose the platform and SDK for the device

    Figure 18: Choose the platform and SDK for the device

  5. Choose Download connection kit and choose next.
     
    Figure 19: Download the connection kit to use for creating the Thermostat1 device

    Figure 19: Download the connection kit to use for creating the Thermostat1 device

  6. Review the three steps to display messages from your IoT device. You will use them to verify the thermostat1 IoT device connectivity to the AWS IoT Core platform. They are:
    1. Step 1: Add execution permissions
    2. Step 2: Run the start script
    3. Step 3: Return to the AWS IoT Console to view the device’s message
       
      Figure 20: How to display messages from an IoT device

      Figure 20: How to display messages from an IoT device

Solution validation

With all of the pieces in place, you can now test the solution.

Primary owner signs in to the web application to set Thermostat1 temperature to 82°F

Figure 21: Thermostat1 temperature update by John

Figure 21: Thermostat1 temperature update by John

  1. Sign in to the Amplify web application as John. You should be able to view the Thermostat1 controller on the dashboard.
  2. Set the temperature to 82°F.
  3. The Lambda function processes the request and performs an API call to Verified Permissions to determine whether to ALLOW or DENY the action based on the policies. Verified Permissions sends back an ALLOW, as the policy that was previously set up allows unrestricted access for primary owners.
  4. Upon receiving the response from Verified Permissions, the Lambda function sends ALLOW permission back to the web application and an API call to the AWS IoT Device Shadow service to update the device (Thermostat1) temperature to 82°F.
     
Figure 22: Policy evaluation decision is ALLOW when a primary owner calls SetTemperature

Figure 22: Policy evaluation decision is ALLOW when a primary owner calls SetTemperature

Guest user signs in to the web application to set Thermostat1 temperature to 80°F

Figure 23: Thermostat1 temperature update by Jane

Figure 23: Thermostat1 temperature update by Jane

  1. If you sign in as Jane to the Amplify web application, you can view the Thermostat1 controller on the dashboard.
  2. Set the temperature to 80°F.
  3. The Lambda function validates the actions by sending an API call to Verified Permissions to determine whether to ALLOW or DENY the action based on the established policies. Verified Permissions sends back a DENY, as the policy only permits temperature adjustments between 72°F and 78°F.
  4. Upon receiving the response from Verified Permissions, the Lambda function sends DENY permissions back to the web application and an unauthorized response is returned.
     
    Figure 24: Guest user jane_doe receives a DENY when calling SetTemperature for a desired temperature of 80°F

    Figure 24: Guest user jane_doe receives a DENY when calling SetTemperature for a desired temperature of 80°F

  5. If you repeat the process (still as Jane) but set Thermostat1 to 75°F, the policy will cause the request to be allowed.
     
    Figure 25: Guest user jane_doe receives an ALLOW when calling SetTemperature for a desired temperature of 75°F

    Figure 25: Guest user jane_doe receives an ALLOW when calling SetTemperature for a desired temperature of 75°F

  6. Similarly, jane_doe is allowed run GetTemperature on the device Thermostat1. When the temperature is set to 74°F, the device shadow is updated. The IoT device being simulated by your AWS Cloud9 instance reads desired the temperature field and sets the reported value to 74.
  7. Now, when jane_doe runs GetTemperature, the value of the device is reported as 74 as shown in Figure 26. We encourage you to try different restrictions in the World Settings (outside temperature and time) by adding restrictions to the static policy that allows GetTemperature for guest user.
     
    Figure 26: Guest user jane_doe receives an ALLOW when calling GetTemperature for the reported temperature

    Figure 26: Guest user jane_doe receives an ALLOW when calling GetTemperature for the reported temperature

Power company signs in to the web application to set Thermostat1 to 78°F at 3.30 PM

Figure 27: Thermostat1 temperature set to 78°F by powercompany user at a specified time

Figure 27: Thermostat1 temperature set to 78°F by powercompany user at a specified time

  1. Sign in as the powercompany user to the Amplify web application using an API. You can view the Thermostat1 controller on the dashboard.
  2. To test this scenario, set the current time to 3:30 PM, and try to set the temperature to 78°F.
  3. The Lambda function validates the actions by sending an API call to Verified Permissions to determine whether to ALLOW or DENY the action based on pre-established policies. Verified Permissions returns ALLOW permission, because the policy for powercompany permits device temperature changes between 2:00 PM and 5:00 PM.
  4. Upon receiving the response from Verified Permissions, the Lambda function sends ALLOW permission back to the web application and an API call to the AWS IoT Device Shadow service to update the Thermostat1 temperature to 78°F.
     
    Figure 28: powercompany receives an ALLOW when SetTemperature is called with the desired temperature of 78°F

    Figure 28: powercompany receives an ALLOW when SetTemperature is called with the desired temperature of 78°F

Note: As an optional exercise, we also made jane_doe a device owner for device Thermostat2. This can be observed in the users.json file in the Github repository. We encourage you to create your own policies and restrict functions for Thermostat2 after going through this post. You will need to create separate Verified Permissions policies and update the Lambda functions to interact with these policies.

We encourage you to create policies for guests and the power company and restrict permissions based on the following criteria:

  1. Verify Jane Doe can perform GetTemperature and SetTemperature actions on Thermostat2.
  2. John Doe should not be able to set the temperature on device Thermostat2 outside of the time range of 4:00 PM and 6:00 PM and outside of the temperature range of 68°F and 72°F.
  3. Power Company can only perform the GetTemperature operation, but there are no restrictions on time and outside temperature.

To help you verify the solution, we’ve provided the correct policies under the challenge directory in the GitHub repository.

Clean up

Deploying the Thermostat application in your AWS account will incur costs. To avoid ongoing charges, when you’re done examining the solution, delete the resources that were created. This includes the Amplify hosted web application, API Gateway resource, AWS Cloud 9 environment, the Lambda function, DynamoDB table, Cognito user pool, AWS IoT Core resources, and Verified Permissions policy store.

Amplify resources can be deleted by going to the AWS CloudFormation console and deleting the stacks that were used to provision various services.

Conclusion

In this post, you learned about creating and managing fine-grained permissions using Verified Permissions for different user personas for your smart thermostat IoT device. With Verified Permissions, you can strengthen your security posture and build smart applications aligned with Zero Trust principles for real-time authorization decisions. To learn more, we recommend:

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Author

Rajat Mathur

Rajat is a Principal Solutions Architect at Amazon Web Services. Rajat is a passionate technologist who enjoys building innovative solutions for AWS customers. His core areas of focus are IoT, Networking, and Serverless computing. In his spare time, Rajat enjoys long drives, traveling, and spending time with family.

Pronoy Chopra

Pronoy Chopra

Pronoy is a Senior Solutions Architect with the Startups Generative AI team at AWS. He specializes in architecting and developing IoT and Machine Learning solutions. He has co-founded two startups and enjoys being hands-on with projects in the IoT, AI/ML and Serverless domain. His work in Magnetoencephalography has been cited many times in the effort to build better brain-compute interfaces.

Syed Sanoor

Syed Sanoor

Syed serves as a Solutions Architect, assisting customers in the enterprise sector. With a foundation in software engineering, he takes pleasure in crafting solutions tailored to client needs. His expertise predominantly lies in C# and IoT. During his leisure time, Syed enjoys piloting drones and playing cricket.

Dynamic DAG generation with YAML and DAG Factory in Amazon MWAA

Post Syndicated from Jayesh Shinde original https://aws.amazon.com/blogs/big-data/dynamic-dag-generation-with-yaml-and-dag-factory-in-amazon-mwaa/

Amazon Managed Workflow for Apache Airflow (Amazon MWAA) is a managed service that allows you to use a familiar Apache Airflow environment with improved scalability, availability, and security to enhance and scale your business workflows without the operational burden of managing the underlying infrastructure. In Airflow, Directed Acyclic Graphs (DAGs) are defined as Python code. Dynamic DAGs refer to the ability to generate DAGs on the fly during runtime, typically based on some external conditions, configurations, or parameters. Dynamic DAGs helps you to create, schedule, and run tasks within a DAG based on data and configurations that may change over time.

There are various ways to introduce dynamism in Airflow DAGs (dynamic DAG generation) using environment variables and external files. One of the approaches is to use the DAG Factory YAML based configuration file method. This library aims to facilitate the creation and configuration of new DAGs by using declarative parameters in YAML. It allows default customizations and is open-source, making it simple to create and customize new functionalities.

In this post, we explore the process of creating Dynamic DAGs with YAML files, using the DAG Factory library. Dynamic DAGs offer several benefits:

  1. Enhanced code reusability – By structuring DAGs through YAML files, we promote reusable components, reducing redundancy in your workflow definitions.
  2. Streamlined maintenance – YAML-based DAG generation simplifies the process of modifying and updating workflows, ensuring smoother maintenance procedures.
  3. Flexible parameterization – With YAML, you can parameterize DAG configurations, facilitating dynamic adjustments to workflows based on varying requirements.
  4. Improved scheduler efficiency – Dynamic DAGs enable more efficient scheduling, optimizing resource allocation and enhancing overall workflow runs
  5. Enhanced scalability – YAML-driven DAGs allow for parallel runs, enabling scalable workflows capable of handling increased workloads efficiently.

By harnessing the power of YAML files and the DAG Factory library, we unleash a versatile approach to building and managing DAGs, empowering you to create robust, scalable, and maintainable data pipelines.

Overview of solution

In this post, we will use an example DAG file that is designed to process a COVID-19 data set. The workflow process involves processing an open source data set offered by WHO-COVID-19-Global. After we install the DAG-Factory Python package, we create a YAML file that has definitions of various tasks. We process the country-specific death count by passing Country as a variable, which creates individual country-based DAGs.

The following diagram illustrates the overall solution along with data flows within logical blocks.

Overview of the Solution

Prerequisites

For this walkthrough, you should have the following prerequisites:

Additionally, complete the following steps (run the setup in an AWS Region where Amazon MWAA is available):

  1. Create an Amazon MWAA environment (if you don’t have one already). If this is your first time using Amazon MWAA, refer to Introducing Amazon Managed Workflows for Apache Airflow (MWAA).

Make sure the AWS Identity and Access Management (IAM) user or role used for setting up the environment has IAM policies attached for the following permissions:

The access policies mentioned here are just for the example in this post. In a production environment, provide only the needed granular permissions by exercising least privilege principles.

  1. Create an unique (within an account) Amazon S3 bucket name while creating your Amazon MWAA environment, and create folders called dags and requirements.
    Amazon S3 Bucket
  2. Create and upload a requirements.txt file with the following content to the requirements folder. Replace {environment-version} with your environment’s version number, and {Python-version} with the version of Python that’s compatible with your environment:
    --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-{Airflow-version}/constraints-{Python-version}.txt"
    dag-factory==0.19.0
    pandas==2.1.4

Pandas is needed just for the example use case described in this post, and dag-factory is the only required plug-in. It is recommended to check the compatibility of the latest version of dag-factory with Amazon MWAA. The boto and psycopg2-binary libraries are included with the Apache Airflow v2 base install and don’t need to be specified in your requirements.txt file.

  1. Download the WHO-COVID-19-global data file to your local machine and upload it under the dags prefix of your S3 bucket.

Make sure that you are pointing to the latest AWS S3 bucket version of your requirements.txt file for the additional package installation to happen. This should typically take between 15 – 20 minutes depending on your environment configuration.

Validate the DAGs

When your Amazon MWAA environment shows as Available on the Amazon MWAA console, navigate to the Airflow UI by choosing Open Airflow UI next to your environment.

Validate the DAG

Verify the existing DAGs by navigating to the DAGs tab.

Verify the DAG

Configure your DAGs

Complete the following steps:

  1. Create empty files named dynamic_dags.yml, example_dag_factory.py and process_s3_data.py on your local machine.
  2. Edit the process_s3_data.py file and save it with following code content, then upload the file back to the Amazon S3 bucket dags folder. We are doing some basic data processing in the code:
    1. Read the file from an Amazon S3 location
    2. Rename the Country_code column as appropriate to the country.
    3. Filter data by the given country.
    4. Write the processed final data into CSV format and upload back to S3 prefix.
import boto3
import pandas as pd
import io
   
def process_s3_data(COUNTRY):
### Top level Variables replace S3_BUCKET with your bucket name ###
    s3 = boto3.client('s3')
    S3_BUCKET = "my-mwaa-assets-bucket-sfj33ddkm"
    INPUT_KEY = "dags/WHO-COVID-19-global-data.csv"
    OUTPUT_KEY = "dags/count_death"
### get csv file ###
   response = s3.get_object(Bucket=S3_BUCKET, Key=INPUT_KEY)
   status = response['ResponseMetadata']['HTTPStatusCode']
   if status == 200:
### read csv file and filter based on the country to write back ###
       df = pd.read_csv(response.get("Body"))
       df.rename(columns={"Country_code": "country"}, inplace=True)
       filtered_df = df[df['country'] == COUNTRY]
       with io.StringIO() as csv_buffer:
                   filtered_df.to_csv(csv_buffer, index=False)
                   response = s3.put_object(
                       Bucket=S3_BUCKET, Key=OUTPUT_KEY + '_' + COUNTRY + '.csv', Body=csv_buffer.getvalue()
                   )
       status = response['ResponseMetadata']['HTTPStatusCode']
       if status == 200:
           print(f"Successful S3 put_object response. Status - {status}")
       else:
           print(f"Unsuccessful S3 put_object response. Status - {status}")
   else:
       print(f"Unsuccessful S3 get_object response. Status - {status}")
  1. Edit the dynamic_dags.yml and save it with the following code content, then upload the file back to the dags folder. We are stitching various DAGs based on the country as follows:
    1. Define the default arguments that are passed to all DAGs.
    2. Create a DAG definition for individual countries by passing op_args
    3. Map the process_s3_data function with python_callable_name.
    4. Use Python Operator to process csv file data stored in Amazon S3 bucket.
    5. We have set schedule_interval as 10 minutes, but feel free to adjust this value as needed.
default:
  default_args:
    owner: "airflow"
    start_date: "2024-03-01"
    retries: 1
    retry_delay_sec: 300
  concurrency: 1
  max_active_runs: 1
  dagrun_timeout_sec: 600
  default_view: "tree"
  orientation: "LR"
  schedule_interval: "*/10 * * * *"
 
module3_dynamic_dag_Australia:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Australia"
 
module3_dynamic_dag_Brazil:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Brazil"
 
module3_dynamic_dag_India:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "India"
 
module3_dynamic_dag_Japan:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Japan"
 
module3_dynamic_dag_Mexico:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Mexico"
 
module3_dynamic_dag_Russia:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Russia"
 
module3_dynamic_dag_Spain:
  tasks:
    task_process_s3_data:
      task_id: process_s3_data
      operator: airflow.operators.python.PythonOperator
      python_callable_name: process_s3_data
      python_callable_file: /usr/local/airflow/dags/process_s3_data.py
      op_args:
        - "Spain"
  1. Edit the file example_dag_factory.py and save it with the following code content, then upload the file back to dags folder. The code cleans the existing the DAGs and generates clean_dags() method and the creating new DAGs using the generate_dags() method from the DagFactory instance.
from airflow import DAG
import dagfactory
  
config_file = "/usr/local/airflow/dags/dynamic_dags.yml"
example_dag_factory = dagfactory.DagFactory(config_file)
  
## to clean up or delete any existing DAGs ##
example_dag_factory.clean_dags(globals())
## generate and create new DAGs ##
example_dag_factory.generate_dags(globals())
  1. After you upload the files, go back to the Airflow UI console and navigate to the DAGs tab, where you will find new DAGs.
    List the new DAGs
  2. Once you upload the files, go back to the Airflow UI console and under the DAGs tab you will find new DAGs are appearing as shown below:DAGs

You can enable DAGs by making them active and testing them individually. Upon activation, an additional CSV file named count_death_{COUNTRY_CODE}.csv is generated in the dags folder.

Cleaning up

There may be costs associated with using the various AWS services discussed in this post. To prevent incurring future charges, delete the Amazon MWAA environment after you have completed the tasks outlined in this post, and empty and delete the S3 bucket.

Conclusion

In this blog post we demonstrated how to use the dag-factory library to create dynamic DAGs. Dynamic DAGs are characterized by their ability to generate results with each parsing of the DAG file based on configurations. Consider using dynamic DAGs in the following scenarios:

  • Automating migration from a legacy system to Airflow, where flexibility in DAG generation is crucial
  • Situations where only a parameter changes between different DAGs, streamlining the workflow management process
  • Managing DAGs that are reliant on the evolving structure of a source system, providing adaptability to changes
  • Establishing standardized practices for DAGs across your team or organization by creating these blueprints, promoting consistency and efficiency
  • Embracing YAML-based declarations over complex Python coding, simplifying DAG configuration and maintenance processes
  • Creating data driven workflows that adapt and evolve based on the data inputs, enabling efficient automation

By incorporating dynamic DAGs into your workflow, you can enhance automation, adaptability, and standardization, ultimately improving the efficiency and effectiveness of your data pipeline management.

To learn more about Amazon MWAA DAG Factory, visit Amazon MWAA for Analytics Workshop: DAG Factory. For additional details and code examples on Amazon MWAA, visit the Amazon MWAA User Guide and the Amazon MWAA examples GitHub repository.


About the Authors

 Jayesh Shinde is Sr. Application Architect with AWS ProServe India. He specializes in creating various solutions that are cloud centered using modern software development practices like serverless, DevOps, and analytics.

Harshd Yeola is Sr. Cloud Architect with AWS ProServe India helping customers to migrate and modernize their infrastructure into AWS. He specializes in building DevSecOps and scalable infrastructure using containers, AIOPs, and AWS Developer Tools and services.

Quickly go from Idea to PR with CodeCatalyst using Amazon Q

Post Syndicated from Brendan Jenkins original https://aws.amazon.com/blogs/devops/quickly-go-from-idea-to-pr-with-codecatalyst-using-amazon-q/

Amazon Q feature development enables teams using Amazon CodeCatalyst to scale with AI to assist developers in completing everyday software development tasks. Developers can now go from an idea in an issue to a fully tested, merge-ready, running application code in a Pull Request (PR) with natural language inputs in a few clicks. Developers can also provide feedback to Amazon Q directly on the published pull request and ask it to generate a new revision. If the code change falls short of expectations, a new development environment can be created directly from the pull request, necessary adjustments can be made manually, a new revision published, and proceed with the merge upon approval.

In this blog, we will walk through a use case leveraging the Modern three-tier web application blueprint, and adding a feature to the web application. We’ll leverage Amazon Q feature development to quickly go from Idea to PR. We also suggest following the steps outlined below in this blog in your own application so you can gain a better understanding of how you can use this feature in your daily work.

Solution Overview

Amazon Q feature development is integrated into CodeCatalyst. Figure 1 details how users can assign Amazon Q an issue. When assigning the issue, users answer a few preliminary questions and Amazon Q outputs the proposed approach, where users can either approve or provide additional feedback to Amazon Q. Once approved, Amazon Q will generate a PR where users can review, revise, and merge the PR into the repository.

Figure 1: Amazon Q feature development workflow

Figure 1: Amazon Q feature development workflow

Prerequisites

Although we will walk through a sample use case in this blog using a Blueprint from CodeCatalyst, after, we encourage you to try this with your own application so you can gain hands-on experience with utilizing this feature. If you are using CodeCatalyst for the first time, you’ll need:

Walkthrough

Step 1: Creating the blueprint

In this blog, we’ll leverage the Modern three-tier web application blueprint to walk through a sample use case. This blueprint creates a Mythical Mysfits three-tier web application with modular presentation, application, and data layers.

Figure 2: Creating a new Modern three-tier application blueprint

Figure 2: Creating a new Modern three-tier application blueprint

First, within your space click “Create Project” and select the Modern three-tier web application CodeCatalyst Blueprint as shown above in Figure 2.

Enter a Project name and select: Lambda for the Compute Platform and Amplify Hosting for Frontend Hosting Options. Additionally, ensure your AWS account is selected along with creating a new IAM Role.

Once the project is finished creating, the application will deploy via a CodeCatalyst workflow, assuming the AWS account and IAM role were setup correctly. The deployed application will be similar to the Mythical Mysfits website.

Step 2: Create a new issue

The Product Manager (PM) has asked us to add a feature to the newly created application, which entails creating the ability to add new mythical creatures. The PM has provided a detailed description to get started.

In the Issues section of our new project, click Create Issue

For the Issue title, enter “Ability to add a new mythical creature” and for the Description enter “Users should be able to add a new mythical creature to the website. There should be a new Add button on the UI, when prompted should allow the user to fill in Name, Age, Description, Good/Evil, Lawful/Chaotic, Species, Profile Image URI and thumbnail Image URI for the new creature. When the user clicks save, the application should leverage the existing API in app.py to save the new creature to the DynamoDB table.”

Furthermore, click Assign to Amazon Q as shown below in Figure 3.

Figure 3: Assigning a new issue to Amazon Q

Figure 3: Assigning a new issue to Amazon Q

Lastly, enable the Require Amazon Q to stop after each step and await review of its work. In this use case, we do not anticipate having any changes to our workflow files to support this new feature so we will leave the Allow Amazon Q to modify workflow files disabled as shown below in Figure 4. Click Create Issue and Amazon Q will get started.

Figure 4: Configurations for assigning Amazon Q

Figure 4: Configurations for assigning Amazon Q

Step 3: Review Amazon Qs Approach

After a few minutes, Amazon Q will generate its understanding of the project in the Background section as well as an Approach to make the changes for the issue you created as show in Figure 5 below

(**Note: The Background and Approach generated for you may be different than what is shown in Figure 5 below).

We have the option to proceed as is or can reply to the Approach via a Comment to provide feedback so Amazon Q can refine it to align better with the use case.

Figure 5: Reviewing Amazon Qs Background and Approach

Figure 5: Reviewing Amazon Qs Background and Approach

In the approach, we notice Amazon Q is suggesting it will create a new method to create and save the new item to the table, but we already have an existing method. We decide to leave feedback as show in Figure 6 letting Amazon Q know the existing method should be leveraged.

Figure 6: Provide feedback to Approach

Figure 6: Provide feedback to Approach

Amazon Q will now refine the approach based on the feedback provided. The refined approach generated by Amazon Q meets our requirements, including unit tests, so we decide to click Proceed as shown in Figure 7 below.

Figure 7: Confirm approach and click Proceed

Figure 7: Confirm approach and click Proceed

Now, Amazon Q will generate the code for implementation & create a PR with code changes that can be reviewed.

Step 4: Review the PR

Within our project, under Code on the left panel click on Pull requests. You should see the new PR created by Amazon Q.

The PR description contains the approach that Amazon Q took to generate the code. This is helpful to reviewers who want to gain a high-level understanding of the changes included in the PR before diving into the details. You will also be able to review all changes made to the code as shown below in Figure 8.

Figure 8: Changes within PR

Figure 8: Changes within PR

Step 5 (Optional): Provide feedback on PR

After reviewing the changes in the PR, I leave comments on a few items that can be improved. Notably, all fields on the new input form for creating a new creature should be required. After I complete leaving comments, I hit the Create Revision button. Amazon Q will take my comments, update the code accordingly and create a new revision of the PR as shown in Figure 9 below.

Figure 9: PR Revision created

Figure 9: PR Revision created.

After reviewing the latest revision created by Amazon Q, I am happy with the changes and proceed with testing the changes directly from CodeCatalyst by utilizing Dev Environments. Once I have completed testing of the new feature and everything works as expected, we will let our peers review the PR to provide feedback and approve the pull request.

As part of following the steps in this blog post, if you upgraded your Space to Standard or Enterprise tier, please ensure you downgrade to the Free tier to avoid any unwanted additional charges. Additionally, delete the project and any associated resources deployed in the walkthrough.

Unassign Amazon Q from any issues no longer being worked on. If Amazon Q has finished its work on an issue or could not find a solution, make sure to unassign Amazon Q to avoid reaching the maximum quota for generative AI features. For more information, see Managing generative AI features and Pricing.

Best Practices for using Amazon Q Feature Development

You can follow a few best practices to ensure you experience the best results when using Amazon Q feature development:

  1. When describing your feature or issue, provide as much context as possible to get the best result from Amazon Q. Being too vague or unclear may not produce ideal results for your use case.
  2. Changes and new features should be as focused as possible. You will likely not experience the best results when making large and complex changes in a single issue. Instead, break the changes or feature up into smaller, more manageable issues where you will see better results.
  3. Leverage the feedback feature to practice giving input on approaches Amazon Q takes to ensure it gets to a similar outcome as highlighted in the blog.

Conclusion

In this post, you’ve seen how you can quickly go from Idea to PR using the Amazon Q Feature development capability in CodeCatalyst. You can leverage this new feature to start building new features in your applications. Check out Amazon CodeCatalyst feature development today.

About the authors

Brent Everman

Brent is a Senior Technical Account Manager with AWS, based out of Pittsburgh. He has over 17 years of experience working with enterprise and startup customers. He is passionate about improving the software development experience and specializes in AWS’ Next Generation Developer Experience services.

Brendan Jenkins

Brendan Jenkins is a Solutions Architect at Amazon Web Services (AWS) working with Enterprise AWS customers providing them with technical guidance and helping achieve their business goals. He has an area of specialization in DevOps and Machine Learning technology.

Fahim Sajjad

Fahim is a Solutions Architect at Amazon Web Services. He helps customers transform their business by helping in designing their cloud solutions and offering technical guidance. Fahim graduated from the University of Maryland, College Park with a degree in Computer Science. He has deep interested in AI and Machine learning. Fahim enjoys reading about new advancements in technology and hiking.

Abdullah Khan

Abdullah is a Solutions Architect at AWS. He attended the University of Maryland, Baltimore County where he earned a degree in Information Systems. Abdullah currently helps customers design and implement solutions on the AWS Cloud. He has a strong interest in artificial intelligence and machine learning. In his spare time, Abdullah enjoys hiking and listening to podcasts.

Architecting for Disaster Recovery on AWS Outposts Racks with AWS Elastic Disaster Recovery

Post Syndicated from Macey Neff original https://aws.amazon.com/blogs/compute/architecting-for-disaster-recovery-on-aws-outposts-racks-with-aws-elastic-disaster-recovery/

This blog post is written by Brianna Rosentrater, Hybrid Edge Specialist SA.

AWS Elastic Disaster Recovery Service (AWS DRS) now supports disaster recovery (DR) architectures that include on-premises Windows and Linux workloads running on AWS Outposts. AWS DRS minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery. Both services are billed and managed from your AWS Management Console.

Like workloads running in AWS Regions, it’s critical to plan for failures. Outposts are designed with resiliency in mind, providing redundant power, networking, and are available to order with N+M active compute instance capacity. In other words, for every physical N compute servers, you have the option of including M redundant hosts capable of handling the workload during a failure. When leveraging AWS DRS with Outpost, you can plan for larger-scale failure modes, such as data center outages, by replicating mission-critical workloads to other remote data center locations or the AWS Region.

In this post, you’ll learn how AWS DRS can be used with Outpost rack to architect for high availability in the event of a site failure. The post will examine several different architectures enabled by AWS DRS that provide DR for Outpost, and the benefits of each method described.

Prerequisites

Each of these architectures described below need the following:

Public internet access isn’t needed, AWS PrivateLink and AWS Direct Connect are supported for replication and failback which is a significant security benefit.

Planning for failure

Disasters come in many forms and are often unplanned and unexpected events. Regardless of whether your workload resides on premises, in a colocation facility, or in an AWS Region, it’s critical to define the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) which are often workload-specific. These two metrics profile how long a service can be down during recovery and quantify the acceptable amount of data loss. RTO and RPO guide you in choosing the appropriate strategy such as backup and recovery, pilot light, warm standby, or a multi-site (active-active) approach.

With AWS DRS, while failing back to a test machine (not the original source server), replication of the source server continues. This allows failback drills without impacting RPO, and non-disruptive failback drills are an important part of disaster planning to validate your recovery plan meets your expected RPO/RTO as per your business requirements.

How AWS DRS integrates with Outpost

AWS DRS uses an AWS Replication Agent at the source to capture the workload and transfer it to a lightweight staging area, which resides on an Outpost equipped with Amazon S3 on Outposts. This method also provides the ability to perform low-effort, non-disruptive DR drills before making the final cutover. The AWS Replication Agent doesn’t need a reboot nor does it impact your applications during installation.

When an Outpost’s subnet is selected as the target for replication or launch, all associated AWS DRS components remain within the Outpost, including the AWS DRS server conversion technology. These conversion servers convert source disks of servers being migrated so that they can boot and run in the target infrastructure, Amazon EBS volumes, snapshots, and replication servers. The replication servers replicate the disks to the target infrastructure. With AWS DRS you can control the data replication path using private connectivity options such as a virtual private network (VPN), AWS Direct Connect, VPC peering, or another private connection. Learn more about using a private IP for data replication.

AWS DRS provides nearly continuous replication for mission-critical workloads and supports deployment patterns including on-premises to Outpost, Outpost to Region, Region to Outpost, and between two logical Outposts through local networks. To leverage Outpost with AWS DRS, simply select the Outpost subnet as your target or source for replication when configuring AWS DRS for your workload. If you are currently using CloudEndure DR for disaster recovery with Outpost, see these detailed instructions for migrating to AWS DRS from CloudEndure DR.

DR from on-premises to Outpost

Outpost can be used as a DR target for on-premises workloads. By deploying an Outpost in a remote data center or colocation a significant distance from the source within the same geo-political boundary, you can replicate workloads across great distances and increase resiliency of the data while ensuring adherence to data residency policies or legislation.

DR from on-premises to Outposts

Figure 1 – DR from on-premises to Outposts

In Figure 1, on premises sources replicate traffic from a LAN to a staging area residing in an Outpost subnet via the local gateway. This allows workloads to failover from their on-premises environment to an Outpost in a different physical location during a disaster.

The staging areas and replication servers run on Amazon Elastic Compute Cloud (Amazon EC2) with Amazon EBS volumes and require Amazon S3 on Outposts where the Amazon EBS snapshots reside.

The replication agent is responsible for providing nearly continuous, block-level replication from your LAN using TCP/1500 with traffic routing to Amazon EC2 instances using the Outposts local gateway.

DR from Outpost to Region

Since its initial release, Outpost has supported Amazon EBS snapshots written to Amazon S3 located in the AWS Region. Backup to an AWS Region is one of the most cost-effective and easiest-to-configure DR approaches, enabling data redundancy outside of your Outpost and data center.

This method also offers flexibility for restoration within an AWS Region if the original deployment is irrecoverable. However, depending on the frequency of the snapshots and the timing of the failure, backup, and recovery to the Region has the potential to have an RPO/RTO spanning hours depending on the throughput of the service link.

For critical workloads, AWS DRS can reduce RTO to minutes and RPO in the sub-second range. After creating an initial replication of workloads that reside on the Outpost, AWS DRS provides nearly continuous, block-level replication in the Region. Just like replication from non-AWS virtual machines or bare metal servers, AWS DRS resources, including Replication Servers, Conversion Servers, Amazon EBS Volumes, and Snapshots reside in the Region.

DR from Outpost to Region

Figure 2 – DR from Outpost to Region

In Figure 2, data replication is performed over the service link from Amazon EC2 instances running locally on an Outpost to an AWS Region. The service link traverses either public Region connectivity or AWS Direct Connect.

AWS Direct Connect is the recommended option because it provides low latency and consistent bandwidth for the service link back to a Region, which also improves the reliability of transmission for AWS DRS replication traffic.

The service link is comprised of redundant, encrypted VPN tunnels. Replication traffic can also be sent privately without traversing the public internet by leveraging Private Virtual Interfaces with Direct Connect for the service link.

With this architecture in place, you can mitigate disasters and reduce downtime by failing over to the AWS Region using AWS DRS.

DR from Region to Outpost

AWS provides multiple Availability Zones (AZs) within a Region and isolated AWS Regions globally for the greatest possible fault tolerance and stability. The reliability pillar of AWS’s Well-Architected Framework encourages distributing workloads across AZs and replicating data between Regions when the need for distances exceeds those of AZs.

AWS DRS supports nearly continuous replication of workloads from a Region to an Outpost within your data center or colocation facility for DR. This deployment model provides increased durability from a source AWS Region to an Outpost anchored to a different Region.

In this model, AWS DRS components remain on-premises within the Outpost, but data charges are applicable as data egresses from the Region back to the data center and Amazon S3 on Outposts is required on the destination Outpost.

DR from Region to Outpost

Figure 3 – DR from Region to Outpost

Implementing the preceding architecture diagram enables failover of critical workloads from the Region to on-premises Outposts seamlessly. Keep in mind that AWS Regions provide the management and control plane for Outpost, making it critical to consider probability and frequency of service link interruptions as a part of your DR planning. Scenarios such as warm standby with pre-allocated Amazon EC2 and Amazon EBS resources may prove more resilient during service link disruptions.

DR between two Outposts

Each logical Outpost is comprised of one or more physical racks. Logical Outposts are in independent colocations of one another, and support deployments in disparate data centers or colocation facilities. You can elect to have multiple logical Outposts anchored to different Availability Zones or Regions. AWS DRS unlocks options for replication between two logical Outposts, leading to increased resiliency and reducing the impact of your data center as a single point of failure. In the following architecture, nearly continuous replication captured from a single Outpost source is applied at a second logical Outpost.

DR between two Outposts

Figure 4 – DR between two Outposts

Supporting both directional and bidirectional replication between Outposts can minimize disruption caused by events that take down a data center, Availability Zone, or even the entire Region result in minimal disruption. In the following architecture diagram, bidirectional data replication occurs between the Outposts by routing traffic via the local gateways, minimizing outbound data charges from the Region and allowing for more direct routing between deployment sites that could potentially span significant distances. AWS DRS cannot communicate with resources directly utilizing a customer-owned IP address pool (CoIP pool).

Figure 5 – DR between two Outposts – bidirectional 

Architecture Considerations

When planning an Outpost deployment leveraging AWS DRS, it’s critical to consider the impact on storage. As a general best practice, AWS recommends planning for a 2:1 ratio consisting of EBS volumes used for nearly continuous replication and Amazon EBS snapshots on Amazon S3 for point-in-time recovery. While it’s unlikely that all servers would need recovery simultaneously, it’s also important to allocate a reserve of EBS volume capacity, which will launch at the time of recovery. Amazon S3 on Outpost is needed for each Outpost used as a replication destination, and the recommendation is to plan for a 1:1 ratio consisting of S3 on Outposts storage, plus the rate of data change. For example, if your data change rate is 10%, you’d want to plan for 110% S3 on Outpost use with AWS DRS.

Amazon CloudWatch has integrated metrics for EC2, Amazon EBS, and Amazon S3 capacity on Outposts, making it easy to create custom tailored dashboards and integrate with Simple Notification Service (Amazon SNS) for alerts at defined thresholds. Monitoring these metrics is critical in making sure that proper free space is available for data replication to occur unimpeded. CloudWatch has metrics available for AWS DRS as well. You can also use the AWS DRS service page in the AWS Console to monitor the status of your recovery instances.

Consider taking advantage of Recovery Plans within AWS DRS to make sure that related services are recovered in a particular order. For example, during a disaster, it might be critical to first bring up a database before recovering application tiers. Recovery plans provide the ability to group related services and apply wait times to individual targets.

Conclusion

AWS Outpost enables low latency, data residency, or data gravity-constrained workloads by supplying managed cloud compute and storage services within your data center or colocation. When coupled with AWS DRS, you can decrease RPO and RTO through a variety of flexible deployment models with sources and destinations ranging from on-premises, the Region, or another AWS Outpost.

Serverless IoT email capture, attachment processing, and distribution

Post Syndicated from Stacy Conant original https://aws.amazon.com/blogs/messaging-and-targeting/serverless-iot-email-capture-attachment-processing-and-distribution/

Many customers need to automate email notifications to a broad and diverse set of email recipients, sometimes from a sensor network with a variety of monitoring capabilities. Many sensor monitoring software products include an SMTP client to achieve this goal. However, managing email server infrastructure requires specialty expertise and operating an email server comes with additional cost and inherent risk of breach, spam, and storage management. Organizations also need to manage distribution of attachments, which could be large and potentially contain exploits or viruses. For IoT use cases, diagnostic data relevance quickly expires, necessitating retention policies to regularly delete content.

Solution Overview

This solution uses the Amazon Simple Email Service (SES) SMTP interface to receive SMTP client messages, and processes the message to replace an attachment with a pre-signed URL in the resulting email to its intended recipients. Attachments are stored separately in an Amazon Simple Storage Service (S3) bucket with a lifecycle policy implemented. This reduces the storage requirements of recipient email server receiving notification emails. Additionally, this solution leverages built-in anti-spam and security scanning capabilities to deal with spam and potentially malicious attachments while at the same time providing the mechanism by which pre-signed attachment links can be revoked should the emails be distributed to unintended recipients.

The solution uses:

  • Amazon SES SMTP interface to receive incoming emails.
  • Amazon SES receipt rule on a (sub)domain controlled by administrators, to store raw incoming emails in an Amazon S3 bucket.
  • AWS Lambda function, triggered on S3 ObjectCreated event, to process raw emails, extract attachments, replace each with pre-signed URL with configurable expiry, and send the processed emails to intended recipients.

Solution Flow Details:

  1. SMTP client transmits email content to an email address in a (sub) domain with MX record set to Amazon SES service’s regional endpoint.
  2. Amazon SES SMTP interface receives an email and forwards it to SES Receipt Rule(s) for processing.
  3. A matching Amazon SES Receipt Rule saves incoming email into an Amazon S3 Bucket.
  4. Amazon S3 Bucket emits an S3 ObjectCreated Event, and places the event onto the Amazon Simple Queue Services (SQS) queue.
  5. The AWS Lambda service polls the inbound messages’ SQS queue and feeds events to the Lambda function.
  6. The Lambda function, retrieves email files from the S3 bucket, parses the email sender/subject/body, saves attachments to a separate attachment S3 bucket (7), and replaces attachments with pre-signed URLs in the email body. The Lambda function then extracts intended recipient addresses from the email body. If the body contains properly formatted recipients list, email is then sent using SES API (9), otherwise a notice is posted to a fallback Amazon Simple Notification Service (SNS) Topic (8).
  7. The Lambda function saves extracted attachments, if any, into an attachments bucket.
  8. Malformed email notifications are posted to a fallback Amazon SNS Topic.
  9. The Lambda function invokes Amazon SES API to send the processed email to all intended recipient addresses.
  10. If the Lambda function is unable to process email successfully, the inbound message is placed on to the SQS dead-letter queue (DLQ) queue for later intervention by the operator.
  11. SES delivers an email to each recipients’ mail server.
  12. Intended recipients download emails from their corporate mail servers and retrieve attachments from the S3 pre-signed URL(s) embedded in the email body.
  13. An alarm is triggered and a notification is published to Amazon SNS Alarms Topic whenever:
    • More than 50 failed messages are in the DLQ.
    • Oldest message on incoming SQS queue is older than 3 minutes – unable to keep up with inbound messages (flooding).
    • The incoming SQS queue contains over 180 messages (configurable) over 5 minutes old.

Setting up Amazon SES

For this solution you will need an email account where you can receive emails. You’ll also need a (sub)domain for which you control the mail exchanger (MX) record. You can obtain your (sub)domain either from Amazon Route53 or another domain hosting provider.

Verify the sender email address

You’ll need to follow the instructions to Verify an email address for all identities that you use as “From”, “Source”, ” Sender”, or “Return-Path” addresses. You’ll also need to follow these instructions for any identities you wish to send emails to during initial testing while your SES account is in the “Sandbox” (see next “Moving out of the SES Sandbox” section).

Moving out of the SES Sandbox

Amazon SES accounts are “in the Sandbox” by default, limiting email sending only to verified identities. AWS does this to prevent fraud and abuse as well as protecting your reputation as an email sender. When your account leaves the Sandbox, SES can send email to any recipient, regardless of whether the recipient’s address or domain is verified by SES. However, you still have to verify all identities that you use as “From”, “Source”, “Sender”, or “Return-Path” addresses.
Follow the Moving out of the SES Sandbox instructions in the SES Developer Guide. Approval is usually within 24 hours.

Set up the SES SMTP interface

Follow the workshop lab instructions to set up email sending from your SMTP client using the SES SMTP interface. Once you’ve completed this step, your SMTP client can open authenticated sessions with the SES SMTP interface and send emails. The workshop will guide you through the following steps:

  1. Create SMTP credentials for your SES account.
    • IMPORTANT: Never share SMTP credentials with unauthorized individuals. Anyone with these credentials can send as many SMTP requests and in whatever format/content they choose. This may result in end-users receiving emails with malicious content, administrative/operations overload, and unbounded AWS charges.
  2. Test your connection to ensure you can send emails.
  3. Authenticate using the SMTP credentials generated in step 1 and then send a test email from an SMTP client.

Verify your email domain and bounce notifications with Amazon SES

In order to replace email attachments with a pre-signed URL and other application logic, you’ll need to set up SES to receive emails on a domain or subdomain you control.

  1. Verify the domain that you want to use for receiving emails.
  2. Publish a mail exchanger record (MX record) and include the Amazon SES inbound receiving endpoint for your AWS region ( e.g. inbound-smtp.us-east-1.amazonaws.com for US East Northern Virginia) in the domain DNS configuration.
  3. Amazon SES automatically manages the bounce notifications whenever recipient email is not deliverable. Follow the Set up notifications for bounces and complaints guide to setup bounce notifications.

Deploying the solution

The solution is implemented using AWS CDK with Python. First clone the solution repository to your local machine or Cloud9 development environment. Then deploy the solution by entering the following commands into your terminal:

python -m venv .venv
. ./venv/bin/activate
pip install -r requirements.txt

cdk deploy \
--context SenderEmail=<verified sender email> \
 --context RecipientEmail=<recipient email address> \
 --context ConfigurationSetName=<configuration set name>

Note:

The RecipientEmail CDK context parameter in the cdk deploy command above can be any email address in the domain you verified as part of the Verify the domain step. In other words, if the verified domain is acme-corp.com, then the emails can be [email protected], [email protected], etc.

The ConfigurationSetName CDK context can be obtained by navigating to Identities in Amazon SES console, selecting the verified domain (same as above), switching to “Configuration set” tab and selecting name of the “Default configuration set”

After deploying the solution, please, navigate to Amazon SES Email receiving in AWS console, edit the rule set and set it to Active.

Testing the solution end-to-end

Create a small file and generate a base64 encoding so that you can attach it to an SMTP message:

echo content >> demo.txt
cat demo.txt | base64 > demo64.txt
cat demo64.txt

Install openssl (which includes an SMTP client capability) using the following command:

sudo yum install openssl

Now run the SMTP client (openssl is used for the proof of concept, be sure to complete the steps in the workshop lab instructions first):

openssl s_client -crlf -quiet -starttls smtp -connect email-smtp.<aws-region>.amazonaws.com:587

and feed in the commands (replacing the brackets [] and everything between them) to send the SMTP message with the attachment you created.

EHLO amazonses.com
AUTH LOGIN
[base64 encoded SMTP user name]
[base64 encoded SMTP password]
MAIL FROM:[VERIFIED EMAIL IN SES]
RCPT TO:[VERIFIED EMAIL WITH SES RECEIPT RULE]
DATA
Subject: Demo from openssl
MIME-Version: 1.0
Content-Type: multipart/mixed;
 boundary="XXXXboundary text"

This is a multipart message in MIME format.

--XXXXboundary text
Content-Type: text/plain

Line1:This is a Test email sent to coded list of email addresses using the Amazon SES SMTP interface from openssl SMTP client.
Line2:Email_Rxers_Code:[ANYUSER1@DOMAIN_A,ANYUSER2@DOMAIN_B,ANYUSERX@DOMAIN_Y]:Email_Rxers_Code:
Line3:Last line.

--XXXXboundary text
Content-Type: text/plain;
Content-Transfer-Encoding: Base64
Content-Disposition: attachment; filename="demo64.txt"
Y29udGVudAo=
--XXXXboundary text
.
QUIT

Note: For base64 SMTP username and password above, use values obtained in Set up the SES SMTP interface, step 1. So for example, if the username is AKZB3LJAF5TQQRRPQZO1, then you can obtain base64 encoded value using following command:

echo -n AKZB3LJAF5TQQRRPQZO1 |base64
QUtaQjNMSkFGNVRRUVJSUFFaTzE=

This makes base64 encoded value QUtaQjNMSkFGNVRRUVJSUFFaTzE= Repeat same process for SMTP username and password values in the example above.

The openssl command should result in successful SMTP authentication and send. You should receive an email that looks like this:

Optimizing Security of the Solution

  1. Do not share DNS credentials. Unauthorized access can lead to domain control, potential denial of service, and AWS charges. Restrict access to authorized personnel only.
  2. Do not set the SENDER_EMAIL environment variable to the email address associated with the receipt rule. This address is a closely guarded secret, known only to administrators, and should be changed frequently.
  3. Review access to your code repository regularly to ensure there are no unauthorized changes to your code base.
  4. Utilize Permissions Boundaries to restrict the actions permitted by an IAM user or role.

Cleanup

To cleanup, start by navigating to Amazon SES Email receiving in AWS console, and setting the rule set to Inactive.

Once completed, delete the stack:

cdk destroy

Cleanup AWS SES Access Credentials

In Amazon SES Console, select Manage existing SMTP credentials, select the username for which credentials were created in Set up the SES SMTP interface above, navigate to the Security credentials tab and in the Access keys section, select Action -> Delete to delete AWS SES access credentials.

Troubleshooting

If you are not receiving the email or email is not being sent correctly there are a number of common causes of these errors:

  • HTTP Error 554 Message rejected email address is not verified. The following identities failed the check in region :
    • This means that you have attempted to send an email from address that has not been verified.
    • Please, ensure that the “MAIL FROM:[VERIFIED EMAIL IN SES]” email address sent via openssl matches the SenderEmail=<verified sender email> email address used in cdk deploy.
    • Also make sure this email address was used in Verify the sender email address step.
  • Email is not being delivered/forwarded
    • The incoming S3 bucket under the incoming prefix, contains file called AMAZON_SES_SETUP_NOTIFICATION. This means that MX record of the domain setup is missing. Please, validate that the MX record (step 2) of Verify your email domain with Amazon SES to receive emails section is fully configured.
    • Please ensure after deploying the Amazon SES solution, the created rule set was made active by navigating to Amazon SES Email receiving in AWS console, and set it to Active.
    • This may mean that the destination email address has bounced. Please, navigate to Amazon SES Suppression list in AWS console ensure that recipient’s email is not in the suppression list. If it is listed, you can see the reason in the “Suppression reason” column. There you may either manually remove from the suppression list or if the recipient email is not valid, consider using a different recipient email address.
AWS Legal Disclaimer: Sample code, software libraries, command line tools, proofs of concept, templates, or other related technology are provided as AWS Content or Third-Party Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content or Third-Party Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content or Third-Party Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content or Third-Party Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

About the Authors

Tarek Soliman

Tarek Soliman

Tarek is a Senior Solutions Architect at AWS. His background is in Software Engineering with a focus on distributed systems. He is passionate about diving into customer problems and solving them. He also enjoys building things using software, woodworking, and hobby electronics.

Dave Spencer

Dave Spencer

Dave is a Senior Solutions Architect at AWS. His background is in cloud solutions architecture, Infrastructure as Code (Iac), systems engineering, and embedded systems programming. Dave’s passion is developing partnerships with Department of Defense customers to maximize technology investments and realize their strategic vision.

Ayman Ishimwe

Ayman Ishimwe

Ayman is a Solutions Architect at AWS based in Seattle, Washington. He holds a Master’s degree in Software Engineering and IT from Oakland University. With prior experience in software development, specifically in building microservices for distributed web applications, he is passionate about helping customers build robust and scalable solutions on AWS cloud services following best practices.

Dmytro Protsiv

Dmytro Protsiv

Dmytro is a Cloud Applications Architect for with Amazon Web Services. He is passionate about helping customers to solve their business challenges around application modernization.

Stacy Conant

Stacy Conant

Stacy is a Solutions Architect working with DoD and US Navy customers. She enjoys helping customers understand how to harness big data and working on data analytics solutions. On the weekends, you can find Stacy crocheting, reading Harry Potter (again), playing with her dogs and cooking with her husband.

Power analytics as a service capabilities using Amazon Redshift

Post Syndicated from Sandipan Bhaumik original https://aws.amazon.com/blogs/big-data/power-analytics-as-a-service-capabilities-using-amazon-redshift/

Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. The AaaS model accelerates data-driven decision-making through advanced analytics, enabling organizations to swiftly adapt to changing market trends and make informed strategic choices.

Amazon Redshift is a cloud data warehouse service that offers real-time insights and predictive analytics capabilities for analyzing data from terabytes to petabytes. It offers features like data sharing, Amazon Redshift ML, Amazon Redshift Spectrum, and Amazon Redshift Serverless, which simplify application building and make it effortless for AaaS companies to embed rich data analytics capabilities. Amazon Redshift delivers up to 4.9 times lower cost per user and up to 7.9 times better price-performance than other cloud data warehouses.

The Powered by Amazon Redshift program helps AWS Partners operating an AaaS model quickly build analytics applications using Amazon Redshift and successfully scale their business. For example, you can build visualizations on top of Amazon Redshift and embed them within applications to provide outstanding analytics experiences for end-users. In this post, we explore how AaaS providers scale their processes with Amazon Redshift to deliver insights to their customers.

AaaS delivery models

While serving analytics at scale, AaaS providers and customers can choose where to store the data and where to process the data.

AaaS providers could choose to ingest and process all the customer data into their own account and deliver insights to the customer account. Alternatively, they could choose to directly process data in-place within the customer’s account.

The choice of these delivery models depends on many factors, and each has their own benefits. Because AaaS providers service multiple customers, they could mix these models in a hybrid fashion, meeting each customer’s preference. The following diagram illustrates the two delivery models.

We explore the technical details of each model in the next sections.

Build AaaS on Amazon Redshift

Amazon Redshift has features that allow AaaS providers the flexibility to deploy three unique delivery models:

  • Managed model – Processing data within the Redshift data warehouse the AaaS provider manages
  • Bring-your-own-Redshift (BYOR) model – Processing data directly within the customer’s Redshift data warehouse
  • Hybrid model – Using a mix of both models depending on customer needs

These delivery models give AaaS providers the flexibility to deliver insights to their customers no matter where the data warehouse is located.

Let’s look at how each of these delivery models work in practice.

Managed model

In this model, the AaaS provider ingests customer data in their own account, and engages their own Redshift data warehouse for processing. Then they use one or more methods to deliver the generated insights to their customers. Amazon Redshift enables companies to securely build multi-tenant applications, ensuring data isolation, integrity, and confidentiality. It provides features like row-level security (RLS), column-level security (CLS) for fine-grained access control, role-based access control (RBAC), and assigning permissions at the database and schema level.

The following diagram illustrates the managed delivery model and the various methods AaaS providers can use to deliver insights to their customers.

The workflow includes the following steps:

  1. The AaaS provider pulls data from customer data sources like operational databases, files, and APIs, and ingests them into the Redshift data warehouse hosted in their account.
  2. Data processing jobs enrich the data in Amazon Redshift. This could be an application the AaaS provider has built to process data, or they could use a data processing service like Amazon EMR or AWS Glue to run Spark applications.
  3. Now the AaaS provider has multiple methods to deliver insights to their customers:
    1. Option 1 – The enriched data with insights is shared directly with the customer’s Redshift instance using the Amazon Redshift data sharing feature. End-users consume data using business intelligence (BI) tools and analytics applications.
    2. Option 2 – If AaaS providers are publishing generic insights to AWS Data Exchange to reach millions of AWS customers and monetize those insights, their customers can use AWS Data Exchange for Amazon Redshift. With this feature, customers get instant insights in their Redshift data warehouse without having to write extract, transform, and load (ETL) pipelines to ingest the data. AWS Data Exchange provides their customers a secure and compliant way to subscribe to the data with consolidated billing and subscription management.
    3. Option 3 – The AaaS provider exposes insights on a web application using the Amazon Redshift Data API. Customers access the web application directly from the internet. The gives the AaaS provider the flexibility to expose insights outside an AWS account.
    4. Option 4 – Customers connect to the AaaS provider’s Redshift instance using Amazon QuickSight or other third-party BI tools through a JDBC connection.

In this model, the customer shifts the responsibility of data management and governance to the AaaS providers, with light services to consume insights. This leads to improved decision-making as customers focus on core activities and save time from tedious data management tasks. Because AaaS providers move data from the customer accounts, there could be associated data transfer costs depending on how they move the data. However, because they deliver this service at scale to multiple customers, they can offer cost-efficient services using economies of scale.

BYOR model

In cases where the customer hosts a Redshift data warehouse and wants to run analytics in their own data platform without moving data out, you use the BYOR model.

The following diagram illustrates the BYOR model, where AaaS providers process data to add insights directly in their customer’s data warehouse so the data never leaves the customer account.

The solution includes the following steps:

  1. The customer ingests all the data from various data sources into their Redshift data warehouse.
  2. The data undergoes processing:
    1. The AaaS provider uses a secure channel, AWS PrivateLink for the Redshift Data API, to push data processing logic directly in the customer’s Redshift data warehouse.
    2. They use the same channel to process data at scale with multiple customers. The diagram illustrates a second customer, but this can scale to hundreds or thousands of customers. AaaS providers can tailor data processing logic per customer by isolating scripts for each customer and deploying them according to the customer’s identity, providing a customized and efficient service.
  3. The customer’s end-users consume data from their own account using BI tools and analytics applications.
  4. The customer has control over how to expose insights to their end-users.

This delivery model allows customers to manage their own data, reducing dependency on AaaS providers and cutting data transfer costs. By keeping data in their own environment, customers can reduce the risk of data breach while benefiting from insights for better decision-making.

Hybrid model

Customers have diverse needs influenced by factors like data security, compliance, and technical expertise. To cover a broader range of customers, AaaS providers can choose a hybrid approach that delivers both the managed model and the BYOR model depending on the customer, offering flexibility and the ability to serve multiple customers.

The following diagram illustrates the AaaS provider delivering insights through the BYOR model for Customer 1 and 4, the managed model for Customer 2 and 3, and so on.

Conclusion

In this post, we talked about the rising demand of analytics as a service and how providers can use the capabilities of Amazon Redshift to deliver insights to their customers. We examined two primary delivery models: the managed model, where AaaS providers process data on their own accounts, and the BYOR model, where AaaS providers process and enrich data directly in their customer’s account. Each method offers unique benefits, such as cost-efficiency, enhanced control, and personalized insights. The flexibility of the AWS Cloud facilitates a hybrid model, accommodating diverse customer needs and allowing AaaS providers to scale. We also introduced the Powered by Amazon Redshift program, which supports AaaS businesses in building effective analytics applications, fostering improved user engagement and business growth.

We take this opportunity to invite our ISV partners to reach out to us and learn more about the Powered by Amazon Redshift program.


About the Authors

Sandipan Bhaumik is a Senior Analytics Specialist Solutions Architect based in London, UK. He helps customers modernize their traditional data platforms using the modern data architecture in the cloud to perform analytics at scale.

Sain Das is a Senior Product Manager on the Amazon Redshift team and leads Amazon Redshift GTM for partner programs, including the Powered by Amazon Redshift and Redshift Ready programs.

Accelerate security automation using Amazon CodeWhisperer

Post Syndicated from Brendan Jenkins original https://aws.amazon.com/blogs/security/accelerate-security-automation-using-amazon-codewhisperer/

In an ever-changing security landscape, teams must be able to quickly remediate security risks. Many organizations look for ways to automate the remediation of security findings that are currently handled manually. Amazon CodeWhisperer is an artificial intelligence (AI) coding companion that generates real-time, single-line or full-function code suggestions in your integrated development environment (IDE) to help you quickly build software. By using CodeWhisperer, security teams can expedite the process of writing security automation scripts for various types of findings that are aggregated in AWS Security Hub, a cloud security posture management (CSPM) service.

In this post, we present some of the current challenges with security automation and walk you through how to use CodeWhisperer, together with Amazon EventBridge and AWS Lambda, to automate the remediation of Security Hub findings. Before reading further, please read the AWS Responsible AI Policy.

Current challenges with security automation

Many approaches to security automation, including Lambda and AWS Systems Manager Automation, require software development skills. Furthermore, the process of manually writing code for remediation can be a time-consuming process for security professionals. To help overcome these challenges, CodeWhisperer serves as a force multiplier for qualified security professionals with development experience to quickly and effectively generate code to help remediate security findings.

Security professionals should still cultivate software development skills to implement robust solutions. Engineers should thoroughly review and validate any generated code, as manual oversight remains critical for security.

Solution overview

Figure 1 shows how the findings that Security Hub produces are ingested by EventBridge, which then invokes Lambda functions for processing. The Lambda code is generated with the help of CodeWhisperer.

Figure 1: Diagram of the solution

Security Hub integrates with EventBridge so you can automatically process findings with other services such as Lambda. To begin remediating the findings automatically, you can configure rules to determine where to send findings. This solution will do the following:

  1. Ingest an Amazon Security Hub finding into EventBridge.
  2. Use an EventBridge rule to invoke a Lambda function for processing.
  3. Use CodeWhisperer to generate the Lambda function code.

It is important to note that there are two types of automation for Security Hub finding remediation:

  • Partial automation, which is initiated when a human worker selects the Security Hub findings manually and applies the automated remediation workflow to the selected findings.
  • End-to-end automation, which means that when a finding is generated within Security Hub, this initiates an automated workflow to immediately remediate without human intervention.

Important: When you use end-to-end automation, we highly recommend that you thoroughly test the efficiency and impact of the workflow in a non-production environment first before moving forward with implementation in a production environment.

Prerequisites

To follow along with this walkthrough, make sure that you have the following prerequisites in place:

Implement security automation

In this scenario, you have been tasked with making sure that versioning is enabled across all Amazon Simple Storage Service (Amazon S3) buckets in your AWS account. Additionally, you want to do this in a way that is programmatic and automated so that it can be reused in different AWS accounts in the future.

To do this, you will perform the following steps:

  1. Generate the remediation script with CodeWhisperer
  2. Create the Lambda function
  3. Integrate the Lambda function with Security Hub by using EventBridge
  4. Create a custom action in Security Hub
  5. Create an EventBridge rule to target the Lambda function
  6. Run the remediation

Generate a remediation script with CodeWhisperer

The first step is to use VS Code to create a script so that CodeWhisperer generates the code for your Lambda function in Python. You will use this Lambda function to remediate the Security Hub findings generated by the [S3.14] S3 buckets should use versioning control.

Note: The underlying model of CodeWhisperer is powered by generative AI, and the output of CodeWhisperer is nondeterministic. As such, the code recommended by the service can vary by user. By modifying the initial code comment to prompt CodeWhisperer for a response, customers can change the corresponding output to help meet their needs. Customers should subject all code generated by CodeWhisperer to typical testing and review protocols to verify that it is free of errors and is in line with applicable organizational security policies. To learn about best practices on prompt engineering with CodeWhisperer, see this AWS blog post.

To generate the remediation script

  1. Open a new VS Code window, and then open or create a new folder for your file to reside in.
  2. Create a Python file called cw-blog-remediation.py as shown in Figure 2.
     
    Figure 2: New VS Code file created called cw-blog-remediation.py

    Figure 2: New VS Code file created called cw-blog-remediation.py

  3. Add the following imports to the Python file.
    import json
    import boto3

  4. Because you have the context added to your file, you can now prompt CodeWhisperer by using a natural language comment. In your file, below the import statements, enter the following comment and then press Enter.
    # Create lambda function that turns on versioning for an S3 bucket after the function is triggered from Amazon EventBridge

  5. Accept the first recommendation that CodeWhisperer provides by pressing Tab to use the Lambda function handler, as shown in Figure 3.
    &ngsp;
    Figure 3: Generation of Lambda handler

    Figure 3: Generation of Lambda handler

  6. To get the recommendation for the function from CodeWhisperer, press Enter. Make sure that the recommendation you receive looks similar to the following. CodeWhisperer is nondeterministic, so its recommendations can vary.
    import json
    import boto3
    
    # Create lambda function that turns on versioning for an S3 bucket after function is triggered from Amazon EventBridge
    def lambda_handler(event, context):
        s3 = boto3.client('s3')
        bucket = event['detail']['requestParameters']['bucketName']
        response = s3.put_bucket_versioning(
            Bucket=bucket,
            VersioningConfiguration={
                'Status': 'Enabled'
            }
        )
        print(response)
        return {
            'statusCode': 200,
            'body': json.dumps('Versioning enabled for bucket ' + bucket)
        }
    

  7. Take a moment to review the user actions and keyboard shortcut keys. Press Tab to accept the recommendation.
  8. You can change the function body to fit your use case. To get the Amazon Resource Name (ARN) of the S3 bucket from the EventBridge event, replace the bucket variable with the following line:
    bucket = event['detail']['findings'][0]['Resources'][0]['Id']

  9. To prompt CodeWhisperer to extract the bucket name from the bucket ARN, use the following comment:
    # Take the S3 bucket name from the ARN of the S3 bucket

    Your function code should look similar to the following:

    import json
    import boto3
    
    # Create lambda function that turns on versioning for an S3 bucket after function is triggered from Amazon EventBridge
    def lambda_handler(event, context):
        s3 = boto3.client('s3')
       bucket = event['detail']['findings'][0]['Resources'][0]['Id']
             # Take the S3 bucket name from the ARN of the S3 bucket
       bucket = bucket.split(':')[5]
    
        response = s3.put_bucket_versioning(
            Bucket=bucket,
            VersioningConfiguration={
                'Status': 'Enabled'
            }
        )
        print(response)
        return {
            'statusCode': 200,
            'body': json.dumps('Versioning enabled for bucket ' + bucket)
        }
    

  10. Create a .zip file for cw-blog-remediation.py. Find the file in your local file manager, right-click the file, and select compress/zip. You will use this .zip file in the next section of the post.

Create the Lambda function

The next step is to use the automation script that you generated to create the Lambda function that will enable versioning on applicable S3 buckets.

To create the Lambda function

  1. Open the AWS Lambda console.
  2. In the left navigation pane, choose Functions, and then choose Create function.
  3. Select Author from Scratch and provide the following configurations for the function:
    1. For Function name, select sec_remediation_function.
    2. For Runtime, select Python 3.12.
    3. For Architecture, select x86_64.
    4. For Permissions, select Create a new role with basic Lambda permissions.
  4. Choose Create function.
  5. To upload your local code to Lambda, select Upload from and then .zip file, and then upload the file that you zipped.
  6. Verify that you created the Lambda function successfully. In the Code source section of Lambda, you should see the code from the automation script displayed in a new tab, as shown in Figure 4.
     
    Figure 4: Source code that was successfully uploaded

    Figure 4: Source code that was successfully uploaded

  7. Choose the Code tab.
  8. Scroll down to the Runtime settings pane and choose Edit.
  9. For Handler, enter cw-blog-remediation.lambda_handler for your function handler, and then choose Save, as shown in Figure 5.
     
    Figure 5: Updated Lambda handler

    Figure 5: Updated Lambda handler

  10. For security purposes, and to follow the principle of least privilege, you should also add an inline policy to the Lambda function’s role to perform the tasks necessary to enable versioning on S3 buckets.
    1. In the Lambda console, navigate to the Configuration tab and then, in the left navigation pane, choose Permissions. Choose the Role name, as shown in Figure 6.
       
      Figure 6: Lambda role in the AWS console

      Figure 6: Lambda role in the AWS console

    2. In the Add permissions dropdown, select Create inline policy.
       
      Figure 7: Create inline policy

      Figure 7: Create inline policy

    3. Choose JSON, add the following policy to the policy editor, and then choose Next.
      {
          "Version": "2012-10-17",
          "Statement": [
              {
                  "Sid": "VisualEditor0",
                  "Effect": "Allow",
                  "Action": "s3:PutBucketVersioning",
                  "Resource": "*"
              }
          ]
      }

    4. Name the policy PutBucketVersioning and choose Create policy.

Create a custom action in Security Hub

In this step, you will create a custom action in Security Hub.

To create the custom action

  1. Open the Security Hub console.
  2. In the left navigation pane, choose Settings, and then choose Custom actions.
  3. Choose Create custom action.
  4. Provide the following information, as shown in Figure 8:
    • For Name, enter TurnOnS3Versioning.
    • For Description, enter Action that will turn on versioning for a specific S3 bucket.
    • For Custom action ID, enter TurnOnS3Versioning.
       
      Figure 8: Create a custom action in Security Hub

      Figure 8: Create a custom action in Security Hub

  5. Choose Create custom action.
  6. Make a note of the Custom action ARN. You will need this ARN when you create a rule to associate with the custom action in EventBridge.

Create an EventBridge rule to target the Lambda function

The next step is to create an EventBridge rule to capture the custom action. You will define an EventBridge rule that matches events (in this case, findings) from Security Hub that were forwarded by the custom action that you defined previously.

To create the EventBridge rule

  1. Navigate to the EventBridge console.
  2. On the right side, choose Create rule.
  3. On the Define rule detail page, give your rule a name and description that represents the rule’s purpose—for example, you could use the same name and description that you used for the custom action. Then choose Next.
  4. Scroll down to Event pattern, and then do the following:
    1. For Event source, make sure that AWS services is selected.
    2. For AWS service, select Security Hub.
    3. For Event type, select Security Hub Findings – Custom Action.
    4. Select Specific custom action ARN(s) and enter the ARN for the custom action that you created earlier.
       
    Figure 9: Specify the EventBridge event pattern for the Security Hub custom action workflow

    Figure 9: Specify the EventBridge event pattern for the Security Hub custom action workflow

    As you provide this information, the Event pattern updates.

  5. Choose Next.
  6. On the Select target(s) step, in the Select a target dropdown, select Lambda function. Then from the Function dropdown, select sec_remediation_function.
  7. Choose Next.
  8. On the Configure tags step, choose Next.
  9. On the Review and create step, choose Create rule.

Run the automation

Your automation is set up and you can now test the automation. This test covers a partial automation workflow, since you will manually select the finding and apply the remediation workflow to one or more selected findings.

Important: As we mentioned earlier, if you decide to make the automation end-to-end, you should assess the impact of the workflow in a non-production environment. Additionally, you may want to consider creating preventative controls if you want to minimize the risk of event occurrence across an entire environment.

To run the automation

  1. In the Security Hub console, on the Findings tab, add a filter by entering Title in the search box and selecting that filter. Select IS and enter S3 general purpose buckets should have versioning enabled (case sensitive). Choose Apply.
  2. In the filtered list, choose the Title of an active finding.
  3. Before you start the automation, check the current configuration of the S3 bucket to confirm that your automation works. Expand the Resources section of the finding.
  4. Under Resource ID, choose the link for the S3 bucket. This opens a new tab on the S3 console that shows only this S3 bucket.
  5. In your browser, go back to the Security Hub tab (don’t close the S3 tab—you will need to return to it), and on the left side, select this same finding, as shown in Figure 10.
     
    Figure 10: Filter out Security Hub findings to list only S3 bucket-related findings

    Figure 10: Filter out Security Hub findings to list only S3 bucket-related findings

  6. In the Actions dropdown list, choose the name of your custom action.
     
    Figure 11: Choose the custom action that you created to start the remediation workflow

    Figure 11: Choose the custom action that you created to start the remediation workflow

  7. When you see a banner that displays Successfully started action…, go back to the S3 browser tab and refresh it. Verify that the S3 versioning configuration on the bucket has been enabled as shown in figure 12.
     
    Figure 12: Versioning successfully enabled

    Figure 12: Versioning successfully enabled

Conclusion

In this post, you learned how to use CodeWhisperer to produce AI-generated code for custom remediations for a security use case. We encourage you to experiment with CodeWhisperer to create Lambda functions that remediate other Security Hub findings that might exist in your account, such as the enforcement of lifecycle policies on S3 buckets with versioning enabled, or using automation to remove multiple unused Amazon EC2 elastic IP addresses. The ability to automatically set public S3 buckets to private is just one of many use cases where CodeWhisperer can generate code to help you remediate Security Hub findings.

To sum up, CodeWhisperer acts as a tool that can help boost the productivity of security experts who have coding abilities, assisting them to swiftly write code to address security issues. However, security specialists should continue building their software development capabilities to implement robust solutions. Engineers should carefully review and test any generated code, since human oversight is still vital for security.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Brendan Jenkins

Brendan Jenkins

Brendan is a Solutions Architect at AWS who works with enterprise customers, providing them with technical guidance and helping them achieve their business goals. He specializes in DevOps and machine learning (ML) technology.

Chris Shea

Chris Shea

Chris is an AWS Solutions Architect serving enterprise customers in the PropTech and AdTech industry verticals, providing guidance and the tools that customers need for success. His areas of interest include AI for DevOps and AI/ML technology.

Tim Manik

Tim Manik

Tim is a Solutions Architect at AWS working with enterprise customers on migrations and modernizations. He specializes in cybersecurity and AI/ML and is passionate about bridging the gap between the two fields.

Angel Tolson

Angel Tolson

Angel is a Solutions Architect at AWS working with small to medium size businesses, providing them with technical guidance and helping them achieve their business goals. She is particularly interested in cloud operations and networking.

Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

Post Syndicated from Raks Khare original https://aws.amazon.com/blogs/big-data/achieve-near-real-time-operational-analytics-using-amazon-aurora-postgresql-zero-etl-integration-with-amazon-redshift/

“Data is at the center of every application, process, and business decision. When data is used to improve customer experiences and drive innovation, it can lead to business growth,”

– Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS in With a zero-ETL approach, AWS is helping builders realize near-real-time analytics.

Customers across industries are becoming more data driven and looking to increase revenue, reduce cost, and optimize their business operations by implementing near real time analytics on transactional data, thereby enhancing agility. Based on customer needs and their feedback, AWS is investing and steadily progressing towards bringing our zero-ETL vision to life so that builders can focus more on creating value from data, instead of preparing data for analysis.

Our zero-ETL integration with Amazon Redshift facilitates point-to-point data movement to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) using Amazon Redshift on petabytes of data. Within seconds of transactional data being written into supported AWS databases, zero-ETL seamlessly makes the data available in Amazon Redshift, removing the need to build and maintain complex data pipelines that perform extract, transform, and load (ETL) operations.

To help you focus on creating value from data instead of investing undifferentiated time and resources in building and managing ETL pipelines between transactional databases and data warehouses, we announced four AWS database zero-ETL integrations with Amazon Redshift at AWS re:Invent 2023:

In this post, we provide step-by-step guidance on how to get started with near real time operational analytics using the Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

Solution overview

To create a zero-ETL integration, you specify an Amazon Aurora PostgreSQL-Compatible Edition cluster (compatible with PostgreSQL 15.4 and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse.

You must create Aurora PostgreSQL DB provisioned clusters within the Amazon RDS Database Preview Environment and a Redshift provisioned preview cluster or serverless preview workgroup, in the US East (Ohio) AWS Region. For Amazon Redshift, make sure that you choose the preview_2023 track in order to use zero-ETL integrations.

The following diagram illustrates the architecture implemented in this post.

The following are the steps needed to set up the zero-ETL integration for this solution. For complete getting started guides, refer to Working with Aurora zero-ETL integrations with Amazon Redshift and Working with zero-ETL integrations.

bdb-3883-image001

After Step1, you can also skip Steps 2–4 and directly start creating your zero-ETL integration from Step 5, in which case Amazon RDS will show a message about missing configurations and you can choose Fix it for me to let Amazon RDS automatically configure the steps.

  1. Configure the Aurora PostgreSQL source with a customized DB cluster parameter group.
  2. Configure the Amazon Redshift Serverless destination with the required resource policy for its namespace.
  3. Update the Redshift Serverless workgroup to enable case-sensitive identifiers.
  4. Configure the required permissions.
  5. Create the zero-ETL integration.
  6. Create a database from the integration in Amazon Redshift.
  7. Start analyzing the near real time transactional data.

Configure the Aurora PostgreSQL source with a customized DB cluster parameter group

For Aurora PostgreSQL DB clusters, you must create the custom parameter group within the Amazon RDS Database Preview Environment, in the US East (Ohio) Region. You can directly access the Amazon RDS Preview Environment.

To create an Aurora PostgreSQL database, complete the following steps:

  1. On the Amazon RDS console, choose Parameter groups in the navigation pane.
  2. Choose Create parameter group.
  3. For Parameter group family, choose aurora-postgresql15.
  4. For Type, choose DB Cluster Parameter Group.
  5. For Group name, enter a name (for example, zero-etl-custom-pg-postgres).
  6. Choose Create.bdb-3883-image002

Aurora PostgreSQL zero-ETL integrations with Amazon Redshift require specific values for the Aurora DB cluster parameters, which requires enhanced logical replication (aurora.enhanced_logical_replication).

  1. On the Parameter groups page, select the newly created parameter group.
  2. On the Actions menu, choose Edit.
  3. Set the following Aurora PostgreSQL (aurora-postgresql15 family) cluster parameter settings:
    • rds.logical_replication=1
    • aurora.enhanced_logical_replication=1
    • aurora.logical_replication_backup=0
    • aurora.logical_replication_globaldb=0

Enabling enhanced logical replication (aurora.enhanced_logical_replication) automatically sets the REPLICA IDENTITY parameter to FULL, which means that all column values are written to the write ahead log (WAL).

  1. Choose Save Changes.bdb-3883-image003
  2. Choose Databases in the navigation pane, then choose Create database.
    bdb-3883-image004
  3. For Engine type, select Amazon Aurora.
  4. For Edition, select Amazon Aurora PostgreSQL-Compatible Edition.
  5. For Available versions, choose Aurora PostgreSQL (compatible with PostgreSQL 15.4 and Zero-ETL Support).bdb-3883-image006
  6. For Templates, select Production.
  7. For DB cluster identifier, enter zero-etl-source-pg.bdb-3883-image007
  8. Under Credentials Settings, enter a password for Master password or use the option to automatically generate a password for you.
  9. In the Instance configuration section, select Memory optimized classes.
  10. Choose a suitable instance size (the default is db.r5.2xlarge).bdb-3883-image008
  11. Under Additional configuration, for DB cluster parameter group, choose the parameter group you created earlier (zero-etl-custom-pg-postgres).bdb-3883-image009
  12. Leave the default settings for the remaining configurations.
  13. Choose Create database.

In a few minutes, this should spin up an Aurora PostgreSQL cluster, with one writer and one reader instance, with the status changing from Creating to Available. The newly created Aurora PostgreSQL cluster will be the source for the zero-ETL integration.

bdb-3883-image010

The next step is to create a named database in Amazon Aurora PostgreSQL for the zero-ETL integration.

The PostgreSQL resource model allows you to create multiple databases within a cluster. Therefore, during the zero-ETL integration creation step, you need to specify which database you want to use as the source for your integration.

When setting up PostgreSQL, you get three standard databases out of the box: template0, template1, and postgres. Whenever you create a new database in PostgreSQL, you are actually basing it off one of these three databases in your cluster. The database created during Aurora PostgreSQL cluster creation is based on template0. The CREATE DATABASE command works by copying an existing database, and if not explicitly specified, by default, it copies the standard system database template1. For the named database for zero-ETL integration, the database is required to be created using template1 and not template0. Therefore, if an initial database name is added under Additional configuration, that would be created using template0 and cannot be used for zero-ETL integration.

  1. To create a new named database using CREATE DATABASE within the new Aurora PostgreSQL cluster zero-etl-source-pg, first get the endpoint of the writer instance of the PostgreSQL cluster.bdb-3883-image011
  2. From a terminal or using AWS CloudShell, SSH into the PostgreSQL cluster and run the following commands to install psql and create a new database zeroetl_db:
    sudo dnf install postgresql15
    psql –version
    psql -h <RDS Write Instance Endpoint> -p 5432 -U postgres
    create database zeroetl_db template template1;

Adding template template1 is optional, because by default, if not mentioned, CREATE DATABASE will use template1.

You can also connect via a client and create the database. Refer to Connect to an Aurora PostgreSQL DB cluster for the options to connect to the PostgreSQL cluster.

Configure Redshift Serverless as destination

After you create your Aurora PostgreSQL source database cluster, you configure a Redshift target data warehouse. The data warehouse must comply with the following requirements:

  • Created in preview (for Aurora PostgreSQL sources only)
  • Uses an RA3 node type (ra3.16xlarge, ra3.4xlarge, or ra3.xlplus) with at least two nodes, or Redshift Serverless
  • Encrypted (if using a provisioned cluster)

For this post, we create and configure a Redshift Serverless workgroup and namespace as the target data warehouse, following these steps:

  1. On the Amazon Redshift console, choose Serverless dashboard in the navigation pane.

Because the zero-ETL integration for Amazon Aurora PostgreSQL to Amazon Redshift has been launched in preview (not for production purposes), you need to create the target data warehouse in a preview environment.

  1. Choose Create preview workgroup.

The first step is to configure the Redshift Serverless workgroup.

  1. For Workgroup name, enter a name (for example, zero-etl-target-rs-wg).bdb-3883-image014
  2. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. The capacity can be configured in increments of 8, from 8–512 RPUs. For this post, set this to 8 RPUs.
  3. Choose Next.bdb-3883-image016

Next, you need to configure the namespace of the data warehouse.

  1. Select Create a new namespace.
  2. For Namespace, enter a name (for example, zero-etl-target-rs-ns).
  3. Choose Next.bdb-3883-image017
  4. Choose Create workgroup.
  5. After the workgroup and namespace are created, choose Namespace configurations in the navigation pane and open the namespace configuration.
  6. On the Resource policy tab, choose Add authorized principals.

An authorized principal identifies the user or role that can create zero-ETL integrations into the data warehouse.

bdb-3883-image018

  1. For IAM principal ARN or AWS account ID, you can enter either the ARN of the AWS user or role, or the ID of the AWS account that you want to grant access to create zero-ETL integrations. (An account ID is stored as an ARN.)
  2. Choose Save changes.bdb-3883-image019

After the Authorized principal is configured, you need to allow the source database to update your Redshift data warehouse. Therefore, you must add the source database as an authorized integration source to the namespace.

  1. Choose Add authorized integration source.bdb-3883-image020
  2. For Authorized source ARN, enter the ARN of the Aurora PostgreSQL cluster, because it’s the source of the zero-ETL integration.

You can obtain the ARN of the Aurora PostgreSQL cluster on the Amazon RDS console, the Configuration tab under Amazon Resource Name.

  1. Choose Save changes.bdb-3883-image021

Update the Redshift Serverless workgroup to enable case-sensitive identifiers

Amazon Aurora PostgreSQL is case sensitive by default, and case sensitivity is disabled on all provisioned clusters and Redshift Serverless workgroups. For the integration to be successful, the case sensitivity parameter enable_case_sensitive_identifier must be enabled for the data warehouse.

In order to modify the enable_case_sensitive_identifier parameter in a Redshift Serverless workgroup, you need to use the AWS Command Line Interface (AWS CLI), because the Amazon Redshift console doesn’t currently support modifying Redshift Serverless parameter values. Run the following command to update the parameter:

aws redshift-serverless update-workgroup --workgroup-name zero-etl-target-rs-wg --config-parameters parameterKey=enable_case_sensitive_identifier,parameterValue=true --region us-east-2

A simple way to connect to the AWS CLI is to use CloudShell, which is a browser-based shell that provides command line access to the AWS resources and tools directly from a browser. The following screenshot illustrates how to run the command in the CloudShell.

bdb-3883-image022

Configure required permissions

To create a zero-ETL integration, your user or role must have an attached identity-based policy with the appropriate AWS Identity and Access Management (IAM) permissions. An AWS account owner can configure required permissions for user or roles who may create zero-ETL integrations. The sample policy allows the associated principal to perform following actions:

  • Create zero-ETL integrations for the source Aurora DB cluster.
  • View and delete all zero-ETL integrations.
  • Create inbound integrations into the target data warehouse. Amazon Redshift has a different ARN format for provisioned and serverless:
  • Provisioned cluster – arn:aws:redshift:{region}:{account-id}:namespace:namespace-uuid
  • Serverless – arn:aws:redshift-serverless:{region}:{account-id}:namespace/namespace-uuid

This permission is not required if the same account owns the Redshift data warehouse and this account is an authorized principal for that data warehouse.

Complete the following steps to configure the permissions:

  1. On the IAM console, choose Policies in the navigation pane.
  2. Choose Create policy.
  3. Create a new policy called rds-integrations using the following JSON. For the Amazon Aurora PostgreSQL preview, all ARNs and actions within the Amazon RDS Database Preview Environment have -preview appended to the service namespace. Therefore, in the following policy, instead of rds, you need to use rds-preview. For example, rds-preview:CreateIntegration.
{
    "Version": "2012-10-17",
    "Statement": [{
        "Effect": "Allow",
        "Action": [
            "rds:CreateIntegration"
        ],
        "Resource": [
            "arn:aws:rds:{region}:{account-id}:cluster:source-cluster",
            "arn:aws:rds:{region}:{account-id}:integration:*"
        ]
    },
    {
        "Effect": "Allow",
        "Action": [
            "rds:DescribeIntegration"
        ],
        "Resource": ["*"]
    },
    {
        "Effect": "Allow",
        "Action": [
            "rds:DeleteIntegration"
        ],
        "Resource": [
            "arn:aws:rds:{region}:{account-id}:integration:*"
        ]
    },
    {
        "Effect": "Allow",
        "Action": [
            "redshift:CreateInboundIntegration"
        ],
        "Resource": [
            "arn:aws:redshift:{region}:{account-id}:cluster:namespace-uuid"
        ]
    }]
}
  1. Attach the policy you created to your IAM user or role permissions.

Create the zero-ETL integration

To create the zero-ETL integration, complete the following steps:

  1. On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane.
  2. Choose Create zero-ETL integration.bdb-3883-image023
  3. For Integration identifier, enter a name, for example zero-etl-demo.
  4. Choose Next.bdb-3883-image025
  5. For Source database, choose Browse RDS databases.bdb-3883-image026
  6. Select the source database zero-etl-source-pg and choose Choose.
  7. For Named database, enter the name of the new database created in the Amazon Aurora PostgreSQL (zeroetl-db).
  8. Choose Next.bdb-3883-image028
  9. In the Target section, for AWS account, select Use the current account.
  10. For Amazon Redshift data warehouse, choose Browse Redshift data warehouses.bdb-3883-image029

We discuss the Specify a different account option later in this section.

  1. Select the Redshift Serverless destination namespace (zero-etl-target-rs-ns), and choose Choose.bdb-3883-image031
  2. Add tags and encryption, if applicable, and choose Next.bdb-3883-image032
  3. Verify the integration name, source, target, and other settings, and choose Create zero-ETL integration.

You can choose the integration on the Amazon RDS console to view the details and monitor its progress. It takes about 30 minutes to change the status from Creating to Active, depending on size of the dataset already available in the source.

bdb-3883-image033

bdb-3883-image034

To specify a target Redshift data warehouse that’s in another AWS account, you must create a role that allows users in the current account to access resources in the target account. For more information, refer to Providing access to an IAM user in another AWS account that you own.

Create a role in the target account with the following permissions:

{
   "Version":"2012-10-17",
   "Statement":[
      {
         "Effect":"Allow",
         "Action":[
            "redshift:DescribeClusters",
            "redshift-serverless:ListNamespaces"
         ],
         "Resource":[
            "*"
         ]
      }
   ]
}

The role must have the following trust policy, which specifies the target account ID. You can do this by creating a role with a trusted entity as an AWS account ID in another account.

{
   "Version":"2012-10-17",
   "Statement":[
      {
         "Effect":"Allow",
         "Principal":{
            "AWS": "arn:aws:iam::{external-account-id}:root"
         },
         "Action":"sts:AssumeRole"
      }
   ]
}

The following screenshot illustrates creating this on the IAM console.

bdb-3883-image035

Then, while creating the zero-ETL integration, for Specify a different account, choose the destination account ID and the name of the role you created.

Create a database from the integration in Amazon Redshift

To create your database, complete the following steps:

  1. On the Redshift Serverless dashboard, navigate to the zero-etl-target-rs-ns namespace.
  2. Choose Query data to open the query editor v2.
    bdb-3883-image036
  3. Connect to the Redshift Serverless data warehouse by choosing Create connection.
    bdb-3883-image037
  4. Obtain the integration_id from the svv_integration system table:
    SELECT integration_id FROM svv_integration; -- copy this result, use in the next sql

  5. Use the integration_id from the previous step to create a new database from the integration. You must also include a reference to the named database within the cluster that you specified when you created the integration.
    CREATE DATABASE aurora_pg_zetl FROM INTEGRATION '<result from above>' DATABASE zeroetl_db;

bdb-3883-image038

The integration is now complete, and an entire snapshot of the source will reflect as is in the destination. Ongoing changes will be synced in near real time.

Analyze the near real time transactional data

Now you can start analyzing the near real time data from the Amazon Aurora PostgreSQL source to the Amazon Redshift target:

  1. Connect to your source Aurora PostgreSQL database. In this demo, we use psql to connect to Amazon Aurora PostgreSQL:
    psql -h <amazon_aurora_postgres_writer_endpoint> -p 5432 -d zeroetl_db -U postgres

bdb-3883-image039

  1. Create a sample table with a primary key. Make sure that all tables to be replicated from source to target have a primary key. Tables without a primary key can’t be replicated to the target.
CREATE TABLE NATION  ( 
N_NATIONKEY  INTEGER NOT NULL PRIMARY KEY, 
N_NAME       CHAR(25) NOT NULL,
N_REGIONKEY  INTEGER NOT NULL,
N_COMMENT    VARCHAR(152));
  1. Insert dummy data into the nation table and verify if the data is properly loaded:
INSERT INTO nation VALUES (1, 'USA', 1 , 'united states of america');
SELECT * FROM nation;

bdb-3883-image040

This sample data should now be replicated in Amazon Redshift.

Analyze the source data in the destination

On the Redshift Serverless dashboard, open query editor v2 and connect to the database aurora_pg_zetl you created earlier.

Run the following query to validate the successful replication of the source data into Amazon Redshift:

SELECT * FROM aurora_pg_etl.public.nation;

bdb-3883-image041

You can also use the following query to validate the initial snapshot or ongoing change data capture (CDC) activity:

SELECT * FROM sys_integration_activity ORDER BY last_commit_timestamp desc;

bdb-3883-image042

Monitoring

There are several options to obtain metrics on the performance and status of the Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

If you navigate to the Amazon Redshift console, you can choose Zero-ETL integrations in the navigation pane. You can choose the zero-ETL integration you want and display Amazon CloudWatch metrics related to the integration. These metrics are also directly available in CloudWatch.

bdb-3883-image043

For each integration, there are two tabs with information available:

  • Integration metrics – Shows metrics such as the number of tables successfully replicated and lag details
    bdb-3883-image044
  • Table statistics – Shows details about each table replicated from Amazon Aurora PostgreSQL to Amazon Redshift
    bdb-3883-image045

In addition to the CloudWatch metrics, you can query the following system views, which provide information about the integrations:

Clean up

When you delete a zero-ETL integration, your transactional data isn’t deleted from Aurora or Amazon Redshift, but Aurora doesn’t send new data to Amazon Redshift.

To delete a zero-ETL integration, complete the following steps:

  1. On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane.
  2. Select the zero-ETL integration that you want to delete and choose Delete.
    bdb-3883-image046
  3. To confirm the deletion, enter confirm and choose Delete.
    bdb-3883-image048

Conclusion

In this post, we explained how you can set up the zero-ETL integration from Amazon Aurora PostgreSQL to Amazon Redshift, a feature that reduces the effort of maintaining data pipelines and enables near real time analytics on transactional and operational data.

To learn more about zero-ETL integration, refer to Working with Aurora zero-ETL integrations with Amazon Redshift and Limitations.


About the Authors

Raks KhareRaks Khare is an Analytics Specialist Solutions Architect at AWS based out of Pennsylvania. He helps customers architect data analytics solutions at scale on the AWS platform.

Juan Luis Polo Garzon is an Associate Specialist Solutions Architect at AWS, specialized in analytics workloads. He has experience helping customers design, build and modernize their cloud-based analytics solutions. Outside of work, he enjoys travelling, outdoors and hiking, and attending to live music events.

Sushmita Barthakur is a Senior Solutions Architect at Amazon Web Services, supporting Enterprise customers architect their workloads on AWS. With a strong background in Data Analytics and Data Management, she has extensive experience helping customers architect and build Business Intelligence and Analytics Solutions, both on-premises and the cloud. Sushmita is based out of Tampa, FL and enjoys traveling, reading and playing tennis.

Detecting and remediating inactive user accounts with Amazon Cognito

Post Syndicated from Harun Abdi original https://aws.amazon.com/blogs/security/detecting-and-remediating-inactive-user-accounts-with-amazon-cognito/

For businesses, particularly those in highly regulated industries, managing user accounts isn’t just a matter of security but also a compliance necessity. In sectors such as finance, healthcare, and government, where regulations often mandate strict control over user access, disabling stale user accounts is a key compliance activity. In this post, we show you a solution that uses serverless technologies to track and disable inactive user accounts. While this process is particularly relevant for those in regulated industries, it can also be beneficial for other organizations looking to maintain a clean and secure user base.

The solution focuses on identifying inactive user accounts in Amazon Cognito and automatically disabling them. Disabling a user account in Cognito effectively restricts the user’s access to applications and services linked with the Amazon Cognito user pool. After their account is disabled, the user cannot sign in, access tokens are revoked for their account and they are unable to perform API operations that require user authentication. However, the user’s data and profile within the Cognito user pool remain intact. If necessary, the account can be re-enabled, allowing the user to regain access and functionality.

While the solution focuses on the example of a single Amazon Cognito user pool in a single account, you also learn considerations for multi-user pool and multi-account strategies.

Solution overview

In this section, you learn how to configure an AWS Lambda function that captures the latest sign-in records of users authenticated by Amazon Cognito and write this data to an Amazon DynamoDB table. A time-to-live (TTL) indicator is set on each of these records based on the user inactivity threshold parameter defined when deploying the solution. This TTL represents the maximum period a user can go without signing in before their account is disabled. As these items reach their TTL expiry in DynamoDB, a second Lambda function is invoked to process the expired items and disable the corresponding user accounts in Cognito. For example, if the user inactivity threshold is configured to be 7 days, the accounts of users who don’t sign in within 7 days of their last sign-in will be disabled. Figure 1 shows an overview of the process.

Note: This solution functions as a background process and doesn’t disable user accounts in real time. This is because DynamoDB Time to Live (TTL) is designed for efficiency and to remain within the constraints of the Amazon Cognito quotas. Set your users’ and administrators’ expectations accordingly, acknowledging that there might be a delay in the reflection of changes and updates.

Figure 1: Architecture diagram for tracking user activity and disabling inactive Amazon Cognito users

Figure 1: Architecture diagram for tracking user activity and disabling inactive Amazon Cognito users

As shown in Figure 1, this process involves the following steps:

  1. An application user signs in by authenticating to Amazon Cognito.
  2. Upon successful user authentication, Cognito initiates a post authentication Lambda trigger invoking the PostAuthProcessorLambda function.
  3. The PostAuthProcessorLambda function puts an item in the LatestPostAuthRecordsDDB DynamoDB table with the following attributes:
    1. sub: A unique identifier for the authenticated user within the Amazon Cognito user pool.
    2. timestamp: The time of the user’s latest sign-in, formatted in UTC ISO standard.
    3. username: The authenticated user’s Cognito username.
    4. userpool_id: The identifier of the user pool to which the user authenticated.
    5. ttl: The TTL value, in seconds, after which a user’s inactivity will initiate account deactivation.
  4. Items in the LatestPostAuthRecordsDDB DynamoDB table are automatically purged upon reaching their TTL expiry, launching events in DynamoDB Streams.
  5. DynamoDB Streams events are filtered to allow invocation of the DDBStreamProcessorLambda function only for TTL deleted items.
  6. The DDBStreamProcessorLambda function runs to disable the corresponding user accounts in Cognito.

Implementation details

In this section, you’re guided through deploying the solution, demonstrating how to integrate it with your existing Amazon Cognito user pool and exploring the solution in more detail.

Note: This solution begins tracking user activity from the moment of its deployment. It can’t retroactively track or manage user activities that occurred prior to its implementation. To make sure the solution disables currently inactive users in the first TTL period after deploying the solution, you should do a one-time preload of those users into the DynamoDB table. If this isn’t done, the currently inactive users won’t be detected because users are detected as they sign in. For the same reason, users who create accounts but never sign in won’t be detected either. To detect user accounts that sign up but never sign in, implement a post confirmation Lambda trigger to invoke a Lambda function that processes user sign-up records and writes them to the DynamoDB table.

Prerequisites

Before deploying this solution, you must have the following prerequisites in place:

  • An existing Amazon Cognito user pool. This user pool is the foundation upon which the solution operates. If you don’t have a Cognito user pool set up, you must create one before proceeding. See Creating a user pool.
  • The ability to launch a CloudFormation template. The second prerequisite is the capability to launch an AWS CloudFormation template in your AWS environment. The template provisions the necessary AWS services, including Lambda functions, a DynamoDB table, and AWS Identity and Access Management (IAM) roles that are integral to the solution. The template simplifies the deployment process, allowing you to set up the entire solution with minimal manual configuration. You must have the necessary permissions in your AWS account to launch CloudFormation stacks and provision these services.

To deploy the solution

  1. Choose the following Launch Stack button to deploy the solution’s CloudFormation template:

    Launch Stack

    The solution deploys in the AWS US East (N. Virginia) Region (us-east-1) by default. To deploy the solution in a different Region, use the Region selector in the console navigation bar and make sure that the services required for this walkthrough are supported in your newly selected Region. For service availability by Region, see AWS Services by Region.

  2. On the Quick Create Stack screen, do the following:
    1. Specify the stack details.
      1. Stack name: The stack name is an identifier that helps you find a particular stack from a list of stacks. A stack name can contain only alphanumeric characters (case sensitive) and hyphens. It must start with an alphabetic character and can’t be longer than 128 characters.
      2. CognitoUserPoolARNs: A comma-separated list of Amazon Cognito user pool Amazon Resource Names (ARNs) to monitor for inactive users.
      3. UserInactiveThresholdDays: Time (in days) that the user account is allowed to be inactive before it’s disabled.
    2. Scroll to the bottom, and in the Capabilities section, select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
    3. Choose Create Stack.

Integrate with your existing user pool

With the CloudFormation template deployed, you can set up Lambda triggers in your existing user pool. This is a key step for tracking user activity.

Note: This walkthrough is using the new AWS Management Console experience. Alternatively, These steps could also be done using CloudFormation.

To integrate with your existing user pool

  1. Navigate to the Amazon Cognito console and select your user pool.
  2. Navigate to User pool properties.
  3. Under Lambda triggers, choose Add Lambda trigger. Select the Authentication radio button, then add a Post authentication trigger and assign the PostAuthProcessorLambda function.

Note: Amazon Cognito allows you to set up one Lambda trigger per event. If you already have a configured post authentication Lambda trigger, you can refactor the existing Lambda function, adding new features directly to minimize the cold starts associated with invoking additional functions (for more information, see Anti-patterns in Lambda-based applications). Keep in mind that when Cognito calls your Lambda function, the function must respond within 5 seconds. If it doesn’t and if the call can be retried, Cognito retries the call. After three unsuccessful attempts, the function times out. You can’t change this 5-second timeout value.

Figure 2: Add a post-authentication Lambda trigger and assign a Lambda function

Figure 2: Add a post-authentication Lambda trigger and assign a Lambda function

When you add a Lambda trigger in the Amazon Cognito console, Cognito adds a resource-based policy to your function that permits your user pool to invoke the function. When you create a Lambda trigger outside of the Cognito console, including a cross-account function, you must add permissions to the resource-based policy of the Lambda function. Your added permissions must allow Cognito to invoke the function on behalf of your user pool. You can add permissions from the Lambda console or use the Lambda AddPermission API operation. To configure this in CloudFormation, you can use the AWS::Lambda::Permission resource.

Explore the solution

The solution should now be operational. It’s configured to begin monitoring user sign-in activities and automatically disable inactive user accounts according to the user inactivity threshold. Use the following procedures to test the solution:

Note: When testing the solution, you can set the UserInactiveThresholdDays CloudFormation parameter to 0. This minimizes the time it takes for user accounts to be disabled.

Step 1: User authentication

  1. Create a user account (if one doesn’t exist) in the Amazon Cognito user pool integrated with the solution.
  2. Authenticate to the Cognito user pool integrated with the solution.
     
    Figure 3: Example user signing in to the Amazon Cognito hosted UI

    Figure 3: Example user signing in to the Amazon Cognito hosted UI

Step 2: Verify the sign-in record in DynamoDB

Confirm the sign-in record was successfully put in the LatestPostAuthRecordsDDB DynamoDB table.

  1. Navigate to the DynamoDB console.
  2. Select the LatestPostAuthRecordsDDB table.
  3. Select Explore Table Items.
  4. Locate the sign-in record associated with your user.
     
Figure 4: Locating the sign-in record associated with the signed-in user

Figure 4: Locating the sign-in record associated with the signed-in user

Step 3: Confirm user deactivation in Amazon Cognito

After the TTL expires, validate that the user account is disabled in Amazon Cognito.

  1. Navigate to the Amazon Cognito console.
  2. Select the relevant Cognito user pool.
  3. Under Users, select the specific user.
  4. Verify the Account status in the User information section.
     
Figure 5: Screenshot of the user that signed in with their account status set to disabled

Figure 5: Screenshot of the user that signed in with their account status set to disabled

Note: TTL typically deletes expired items within a few days. Depending on the size and activity level of a table, the actual delete operation of an expired item can vary. TTL deletes items on a best effort basis, and deletion might take longer in some cases.

The user’s account is now disabled. A disabled user account can’t be used to sign in, but still appears in the responses to GetUser and ListUsers API requests.

Design considerations

In this section, you dive deeper into the key components of this solution.

DynamoDB schema configuration:

The DynamoDB schema has the Amazon Cognito sub attribute as the partition key. The Cognito sub is a globally unique user identifier within Cognito user pools that cannot be changed. This configuration ensures each user has a single entry in the table, even if the solution is configured to track multiple user pools. See Other considerations for more about tracking multiple user pools.

Using DynamoDB Streams and Lambda to disable TTL deleted users

This solution uses DynamoDB TTL and DynamoDB Streams alongside Lambda to process user sign-in records. The TTL feature automatically deletes items past their expiration time without write throughput consumption. The deleted items are captured by DynamoDB Streams and processed using Lambda. You also apply event filtering within the Lambda event source mapping, ensuring that the DDBStreamProcessorLambda function is invoked exclusively for TTL-deleted items (see the following code example for the JSON filter pattern). This approach reduces invocations of the Lambda functions, simplifies code, and reduces overall cost.

{
    "Filters": [
        {
            "Pattern": { "userIdentity": { "type": ["Service"], "principalId": ["dynamodb.amazonaws.com"] } }
        }
    ]
}

Handling API quotas:

The DDBStreamProcessorLambda function is configured to comply with the AdminDisableUser API’s quota limits. It processes messages in batches of 25, with a parallelization factor of 1. This makes sure that the solution remains within the nonadjustable 25 requests per second (RPS) limit for AdminDisableUser, avoiding potential API throttling. For more details on these limits, see Quotas in Amazon Cognito.

Dead-letter queues:

Throughout the architecture, dead-letter queues (DLQs) are used to handle message processing failures gracefully. They make sure that unprocessed records aren’t lost but instead are queued for further inspection and retry.

Other considerations

The following considerations are important for scaling the solution in complex environments and maintaining its integrity. The ability to scale and manage the increased complexity is crucial for successful adoption of the solution.

Multi-user pool and multi-account deployment

While this solution discussed a single Amazon Cognito user pool in a single AWS account, this solution can also function in environments with multiple user pools. This involves deploying the solution and integrating with each user pool as described in Integrating with your existing user pool. Because of the AdminDisableUser API’s quota limit for the maximum volume of requests in one AWS Region in one AWS account, consider deploying the solution separately in each Region in each AWS account to stay within the API limits.

Efficient processing with Amazon SQS:

Consider using Amazon Simple Queue Service (Amazon SQS) to add a queue between the PostAuthProcessorLambda function and the LatestPostAuthRecordsDDB DynamoDB table to optimize processing. This approach decouples user sign-in actions from DynamoDB writes, and allows for batching writes to DynamoDB, reducing the number of write requests.

Clean up

Avoid unwanted charges by cleaning up the resources you’ve created. To decommission the solution, follow these steps:

  1. Remove the Lambda trigger from the Amazon Cognito user pool:
    1. Navigate to the Amazon Cognito console.
    2. Select the user pool you have been working with.
    3. Go to the Triggers section within the user pool settings.
    4. Manually remove the association of the Lambda function with the user pool events.
  2. Remove the CloudFormation stack:
    1. Open the CloudFormation console.
    2. Locate and select the CloudFormation stack that was used to deploy the solution.
    3. Delete the stack.
    4. CloudFormation will automatically remove the resources created by this stack, including Lambda functions, Amazon SQS queues, and DynamoDB tables.

Conclusion

In this post, we walked you through a solution to identify and disable stale user accounts based on periods of inactivity. While the example focuses on a single Amazon Cognito user pool, the approach can be adapted for more complex environments with multiple user pools across multiple accounts. For examples of Amazon Cognito architectures, see the AWS Architecture Blog.

Proper planning is essential for seamless integration with your existing infrastructure. Carefully consider factors such as your security environment, compliance needs, and user pool configurations. You can modify this solution to suit your specific use case.

Maintaining clean and active user pools is an ongoing journey. Continue monitoring your systems, optimizing configurations, and keeping up-to-date on new features. Combined with well-architected preventive measures, automated user management systems provide strong defenses for your applications and data.

For further reading, see the AWS Well-Architected Security Pillar and more posts like this one on the AWS Security Blog.

If you have feedback about this post, submit comments in the Comments section. If you have questions about this post, start a new thread on the Amazon Cognito re:Post forum or contact AWS Support.

Harun Abdi

Harun Abdi

Harun is a Startup Solutions Architect based in Toronto, Canada. Harun loves working with customers across different sectors, supporting them to architect reliable and scalable solutions. In his spare time, he enjoys playing soccer and spending time with friends and family.

Dylan Souvage

Dylan Souvage

Dylan is a Partner Solutions Architect based in Austin, Texas. Dylan loves working with customers to understand their business needs and enable them in their cloud journey. In his spare time, he enjoys going out in nature and going on long road trips.

Automate large-scale data validation using Amazon EMR and Apache Griffin

Post Syndicated from Dipal Mahajan original https://aws.amazon.com/blogs/big-data/automate-large-scale-data-validation-using-amazon-emr-and-apache-griffin/

Many enterprises are migrating their on-premises data stores to the AWS Cloud. During data migration, a key requirement is to validate all the data that has been moved from source to target. This data validation is a critical step, and if not done correctly, may result in the failure of the entire project. However, developing custom solutions to determine migration accuracy by comparing the data between the source and target can often be time-consuming.

In this post, we walk through a step-by-step process to validate large datasets after migration using a configuration-based tool using Amazon EMR and the Apache Griffin open source library. Griffin is an open source data quality solution for big data, which supports both batch and streaming mode.

In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical. Manual validation processes are not only time-consuming but also prone to errors, especially when dealing with vast volumes of data. Automated data validation frameworks offer a streamlined solution by efficiently comparing large datasets, identifying discrepancies, and ensuring data accuracy at scale. With such frameworks, organizations can save valuable time and resources while maintaining confidence in the integrity of their data, thereby enabling informed decision-making and enhancing overall operational efficiency.

The following are standout features for this framework:

  • Utilizes a configuration-driven framework
  • Offers plug-and-play functionality for seamless integration
  • Conducts count comparison to identify any disparities
  • Implements robust data validation procedures
  • Ensures data quality through systematic checks
  • Provides access to a file containing mismatched records for in-depth analysis
  • Generates comprehensive reports for insights and tracking purposes

Solution overview

This solution uses the following services:

  • Amazon Simple Storage Service (Amazon S3) or Hadoop Distributed File System (HDFS) as the source and target.
  • Amazon EMR to run the PySpark script. We use a Python wrapper on top of Griffin to validate data between Hadoop tables created over HDFS or Amazon S3.
  • AWS Glue to catalog the technical table, which stores the results of the Griffin job.
  • Amazon Athena to query the output table to verify the results.

We use tables that store the count for each source and target table and also create files that show the difference of records between source and target.

The following diagram illustrates the solution architecture.

Architecture_Diagram

In the depicted architecture and our typical data lake use case, our data either resides n Amazon S3 or is migrated from on premises to Amazon S3 using replication tools such as AWS DataSync or AWS Database Migration Service (AWS DMS). Although this solution is designed to seamlessly interact with both Hive Metastore and the AWS Glue Data Catalog, we use the Data Catalog as our example in this post.

This framework operates within Amazon EMR, automatically running scheduled tasks on a daily basis, as per the defined frequency. It generates and publishes reports in Amazon S3, which are then accessible via Athena. A notable feature of this framework is its capability to detect count mismatches and data discrepancies, in addition to generating a file in Amazon S3 containing full records that didn’t match, facilitating further analysis.

In this example, we use three tables in an on-premises database to validate between source and target : balance_sheet, covid, and survery_financial_report.

Prerequisites

Before getting started, make sure you have the following prerequisites:

Deploy the solution

To make it straightforward for you to get started, we have created a CloudFormation template that automatically configures and deploys the solution for you. Complete the following steps:

  1. Create an S3 bucket in your AWS account called bdb-3070-griffin-datavalidation-blog-${AWS::AccountId}-${AWS::Region} (provide your AWS account ID and AWS Region).
  2. Unzip the following file to your local system.
  3. After unzipping the file to your local system, change <bucket name> to the one you created in your account (bdb-3070-griffin-datavalidation-blog-${AWS::AccountId}-${AWS::Region}) in the following files:
    1. bootstrap-bdb-3070-datavalidation.sh
    2. Validation_Metrics_Athena_tables.hql
    3. datavalidation/totalcount/totalcount_input.txt
    4. datavalidation/accuracy/accuracy_input.txt
  4. Upload all the folders and files in your local folder to your S3 bucket:
    aws s3 cp . s3://<bucket_name>/ --recursive

  5. Run the following CloudFormation template in your account.

The CloudFormation template creates a database called griffin_datavalidation_blog and an AWS Glue crawler called griffin_data_validation_blog on top of the data folder in the .zip file.

  1. Choose Next.
    Cloudformation_template_1
  2. Choose Next again.
  3. On the Review page, select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  4. Choose Create stack.

You can view the stack outputs on the AWS Management Console or by using the following AWS CLI command:

aws cloudformation describe-stacks --stack-name <stack-name> --region us-east-1 --query Stacks[0].Outputs
  1. Run the AWS Glue crawler and verify that six tables have been created in the Data Catalog.
  2. Run the following CloudFormation template in your account.

This template creates an EMR cluster with a bootstrap script to copy Griffin-related JARs and artifacts. It also runs three EMR steps:

  • Create two Athena tables and two Athena views to see the validation matrix produced by the Griffin framework
  • Run count validation for all three tables to compare the source and target table
  • Run record-level and column-level validations for all three tables to compare between the source and target table
  1. For SubnetID, enter your subnet ID.
  2. Choose Next.
    Cloudformation_template_2
  3. Choose Next again.
  4. On the Review page, select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  5. Choose Create stack.

You can view the stack outputs on the console or by using the following AWS CLI command:

aws cloudformation describe-stacks --stack-name <stack-name> --region us-east-1 --query Stacks[0].Outputs

It takes approximately 5 minutes for the deployment to complete. When the stack is complete, you should see the EMRCluster resource launched and available in your account.

When the EMR cluster is launched, it runs the following steps as part of the post-cluster launch:

  • Bootstrap action – It installs the Griffin JAR file and directories for this framework. It also downloads sample data files to use in the next step.
  • Athena_Table_Creation – It creates tables in Athena to read the result reports.
  • Count_Validation – It runs the job to compare the data count between source and target data from the Data Catalog table and stores the results in an S3 bucket, which will be read via an Athena table.
  • Accuracy – It runs the job to compare the data rows between the source and target data from the Data Catalog table and store the results in an S3 bucket, which will be read via the Athena table.

Athena_table

When the EMR steps are complete, your table comparison is done and ready to view in Athena automatically. No manual intervention is needed for validation.

Validate data with Python Griffin

When your EMR cluster is ready and all the jobs are complete, it means the count validation and data validation are complete. The results have been stored in Amazon S3 and the Athena table is already created on top of that. You can query the Athena tables to view the results, as shown in the following screenshot.

The following screenshot shows the count results for all tables.

Summary_table

The following screenshot shows the data accuracy results for all tables.

Detailed_view

The following screenshot shows the files created for each table with mismatched records. Individual folders are generated for each table directly from the job.

mismatched_records

Every table folder contains a directory for each day the job is run.

S3_path_mismatched

Within that specific date, a file named __missRecords contains records that do not match.

S3_path_mismatched_2

The following screenshot shows the contents of the __missRecords file.

__missRecords

Clean up

To avoid incurring additional charges, complete the following steps to clean up your resources when you’re done with the solution:

  1. Delete the AWS Glue database griffin_datavalidation_blog and drop the database griffin_datavalidation_blog cascade.
  2. Delete the prefixes and objects you created from the bucket bdb-3070-griffin-datavalidation-blog-${AWS::AccountId}-${AWS::Region}.
  3. Delete the CloudFormation stack, which removes your additional resources.

Conclusion

This post showed how you can use Python Griffin to accelerate the post-migration data validation process. Python Griffin helps you calculate count and row- and column-level validation, identifying mismatched records without writing any code.

For more information about data quality use cases, refer to Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog and AWS Glue Data Quality.


About the Authors

Dipal Mahajan serves as a Lead Consultant at Amazon Web Services, providing expert guidance to global clients in developing highly secure, scalable, reliable, and cost-efficient cloud applications. With a wealth of experience in software development, architecture, and analytics across diverse sectors such as finance, telecom, retail, and healthcare, he brings invaluable insights to his role. Beyond the professional sphere, Dipal enjoys exploring new destinations, having already visited 14 out of 30 countries on his wish list.

Akhil is a Lead Consultant at AWS Professional Services. He helps customers design & build scalable data analytics solutions and migrate data pipelines and data warehouses to AWS. In his spare time, he loves travelling, playing games and watching movies.

Ramesh Raghupathy is a Senior Data Architect with WWCO ProServe at AWS. He works with AWS customers to architect, deploy, and migrate to data warehouses and data lakes on the AWS Cloud. While not at work, Ramesh enjoys traveling, spending time with family, and yoga.

Simplify your query management with search templates in Amazon OpenSearch Service

Post Syndicated from Arun Lakshmanan original https://aws.amazon.com/blogs/big-data/simplify-your-query-management-with-search-templates-in-amazon-opensearch-service/

Amazon OpenSearch Service is an Apache-2.0-licensed distributed search and analytics suite offered by AWS. This fully managed service allows organizations to secure data, perform keyword and semantic search, analyze logs, alert on anomalies, explore interactive log analytics, implement real-time application monitoring, and gain a more profound understanding of their information landscape. OpenSearch Service provides the tools and resources needed to unlock the full potential of your data. With its scalability, reliability, and ease of use, it’s a valuable solution for businesses seeking to optimize their data-driven decision-making processes and improve overall operational efficiency.

This post delves into the transformative world of search templates. We unravel the power of search templates in revolutionizing the way you handle queries, providing a comprehensive guide to help you navigate through the intricacies of this innovative solution. From optimizing search processes to saving time and reducing complexities, discover how incorporating search templates can elevate your query management game.

Search templates

Search templates empower developers to articulate intricate queries within OpenSearch, enabling their reuse across various application scenarios, eliminating the complexity of query generation in the code. This flexibility also grants you the ability to modify your queries without requiring application recompilation. Search templates in OpenSearch use the mustache template, which is a logic-free templating language. Search templates can be reused by their name. A search template that is based on mustache has a query structure and placeholders for the variable values. You use the _search API to query, specifying the actual values that OpenSearch should use. You can create placeholders for variables that will be changed to their true values at runtime. Double curly braces ({{}}) serve as placeholders in templates.

Mustache enables you to generate dynamic filters or queries based on the values passed in the search request, making your search requests more flexible and powerful.

In the following example, the search template runs the query in the “source” block by passing in the values for the field and value parameters from the “params” block:

GET /myindex/_search/template
 { 
      "source": {   
         "query": { 
             "bool": {
               "must": [
                 {
                   "match": {
                    "{{field}}": "{{value}}"
                 }
             }
        ]
     }
    }
  },
 "params": {
    "field": "place",
    "value": "sweethome"
  }
}

You can store templates in the cluster with a name and refer to them in a search instead of attaching the template in each request. You use the PUT _scripts API to publish a template to the cluster. Let’s say you have an index of books, and you want to search for books with publication date, ratings, and price. You could create and publish a search template as follows:

PUT /_scripts/find_book
{
  "script": {
    "lang": "mustache",
    "source": {
      "query": {
        "bool": {
          "must": [
            {
              "range": {
                "publish_date": {
                  "gte": "{{gte_date}}"
                }
              }
            },
            {
              "range": {
                "rating": {
                  "gte": "{{gte_rating}}"
                }
              }
            },
            {
              "range": {
                "price": {
                  "lte": "{{lte_price}}"
                }
              }
            }
          ]
        }
      }
    }
  }
}

In this example, you define a search template called find_book that uses the mustache template language with defined placeholders for the gte_date, gte_rating, and lte_price parameters.

To use the search template stored in the cluster, you can send a request to OpenSearch with the appropriate parameters. For example, you can search for products that have been published in the last year with ratings greater than 4.0, and priced less than $20:

POST /books/_search/template
{
  "id": "find_book",
  "params": {
    "gte_date": "now-1y",
    "gte_rating": 4.0,
    "lte_price": 20
  }
}

This query will return all books that have been published in the last year, with a rating of at least 4.0, and a price less than $20 from the books index.

Default values in search templates

Default values are values that are used for search parameters when the query that engages the template doesn’t specify values for them. In the context of the find_book example, you can set default values for the from, size, and gte_date parameters in case they are not provided in the search request. To set default values, you can use the following mustache template:

PUT /_scripts/find_book
{
  "script": {
    "lang": "mustache",
    "source": {
      "query": {
        "bool": {
          "filter": [
            {
              "range": {
                "publish_date": {
                  "gte": "{{gte_date}}{{^gte_date}}now-1y{{/gte_date}}"
                }
              }
            },
            {
              "range": {
                "rating": {
                  "gte": "{{gte_rating}}"
                }
              }
            },
            {
              "range": {
                "price": {
                  "lte": "{{lte_price}}"
                }
              }
            }
          ]
        },
        "from": "{{from}}{{^from}}0{{/from}}",
        "size": "{{size}}{{^size}}2{{/size}}"
      }
    }
  }
}

In this template, the {{from}}, {{size}}, and {{gte_date}} parameters are placeholders that can be filled in with specific values when the template is used in a search. If no value is specified for {{from}}, {{size}}, and {{gte_date}}, OpenSearch uses the default values of 0, 2, and now-1y, respectively. This means that if a user searches for products without specifying from, size, and gte_date, the search will return just two products matching the search criteria for 1 year.

You can also use the render API as follows if you have a stored template and want to validate it:

POST _render/template
{
  "id": "find_book",
  "params": {
    "gte_date": "now-1y",
    "gte_rating": 4.0,
    "lte_price": 20
  }
}

Conditions in search templates

The conditional statement that allows you to control the flow of your search template based on certain conditions. It’s often used to include or exclude certain parts of the search request based on certain parameters. The syntax as follows:

{{#Any condition}}
  ... code to execute if the condition is true ...
{{/Any}}

The following example searches for books based on the gte_date, gte_rating, and lte_price parameters and an optional stock parameter. The if condition is used to include the condition_block/term query only if the stock parameter is present in the search request. If the is_available parameter is not present, the condition_block/term query will be skipped.

GET /books/_search/template
{
  "source": """{
    "query": {
      "bool": {
        "must": [
        {{#is_available}}
        {
          "term": {
            "in_stock": "{{is_available}}"
          }
        },
        {{/is_available}}
          {
            "range": {
              "publish_date": {
                "gte": "{{gte_date}}"
              }
            }
          },
          {
            "range": {
              "rating": {
                "gte": "{{gte_rating}}"
              }
            }
          },
          {
            "range": {
              "price": {
                "lte": "{{lte_price}}"
              }
            }
          }
        ]
      }
    }
  }""",
  "params": {
    "gte_date": "now-3y",
    "gte_rating": 4.0,
    "lte_price": 20,
    "is_available": true
  }
}

By using a conditional statement in this way, you can make your search requests more flexible and efficient by only including the necessary filters when they are needed.

To make the query valid inside the JSON, it needs to be escaped with triple quotes (""") in the payload.

Loops in search templates

A loop is a feature of mustache templates that allows you to iterate over an array of values and run the same code block for each item in the array. It’s often used to generate a dynamic list of filters or queries based on the values passed in the search request. The syntax is as follows:

{{#list item in array}}
  ... code to execute for each item ...
{{/list}}

The following example searches for books based on a query string ({{query}}) and an array of categories to filter the search results. The mustache loop is used to generate a match filter for each item in the categories array.

GET books/_search/template
{
  "source": """{
    "query": {
      "bool": {
        "must": [
        {{#list}}
        {
          "match": {
            "category": "{{list}}"
          }
        }
        {{/list}}
          {
          "match": {
            "title": "{{name}}"
          }
        }
        ]
      }
    }
  }""",
  "params": {
    "name": "killer",
    "list": ["Classics", "comics", "Horror"]
  }
}

The search request is rendered as follows:

{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "title": "killer"
          }
        },
        {
          "match": {
            "category": "Classics"
          }
        },
        {
          "match": {
            "category": "comics"
          }
        },
        {
          "match": {
            "category": "Horror"
          }
        }
      ]
    }
  }
}

The loop has generated a match filter for each item in the categories array, resulting in a more flexible and efficient search request that filters by multiple categories. By using the loops, you can generate dynamic filters or queries based on the values passed in the search request, making your search requests more flexible and powerful.

Advantages of using search templates

The following are key advantages of using search templates:

  • Maintainability – By separating the query definition from the application code, search templates make it straightforward to manage changes to the query or tune search relevancy. You don’t have to compile and redeploy your application.
  • Consistency – You can construct search templates that allow you to design standardized query patterns and reuse them throughout your application, which can help maintain consistency across your queries.
  • Readability – Because templates can be constructed using a more terse and expressive syntax, complicated queries are straightforward to test and debug.
  • Testing – Search templates can be tested and debugged independently of the application code, facilitating simpler problem-solving and relevancy tuning without having to re-deploy the application. You can easily create A/B testing with different templates for the same search.
  • Flexibility – Search templates can be quickly updated or adjusted to account for modifications to the data or search specifications.

Best practices

Consider the following best practices when using search templates:

  •  Before deploying your template to production, make sure it is fully tested. You can test the effectiveness and correctness of your template with example data. It is highly recommended to run the application tests that use these templates before publishing.
  • Search templates allow for the addition of input parameters, which you can use to modify the query to suit the needs of a particular use case. Reusing the same template with varied inputs is made simpler by parameterizing the inputs.
  • Manage the templates in an external source control system.
  • Avoid hard-coding values inside the query—instead, use defaults.

Conclusion

In this post, you learned the basics of search templates, a powerful feature of OpenSearch, and how templates help streamline search queries and improve performance. With search templates, you can build more robust search applications in less time.

If you have feedback about this post, submit it in the comments section. If you have questions about this post, start a new thread on the Amazon OpenSearch Service forum or contact AWS Support.

Stay tuned for more exciting updates and new features in OpenSearch Service.


About the authors

Arun Lakshmanan is a Search Specialist with Amazon OpenSearch Service based out of Chicago, IL. He has over 20 years of experience working with enterprise customers and startups. He loves to travel and spend quality time with his family.

Madhan Kumar Baskaran works as a Search Engineer at AWS, specializing in Amazon OpenSearch Service. His primary focus involves assisting customers in constructing scalable search applications and analytics solutions. Based in Bengaluru, India, Madhan has a keen interest in data engineering and DevOps.

Terraform CI/CD and testing on AWS with the new Terraform Test Framework

Post Syndicated from Kevon Mayers original https://aws.amazon.com/blogs/devops/terraform-ci-cd-and-testing-on-aws-with-the-new-terraform-test-framework/

Image of HashiCorp Terraform logo and Amazon Web Services (AWS) Logo. Underneath the AWS Logo are the service logos for AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, and Amazon S3. Graphic created by Kevon Mayers

Graphic created by Kevon Mayers

 Introduction

Organizations often use Terraform Modules to orchestrate complex resource provisioning and provide a simple interface for developers to enter the required parameters to deploy the desired infrastructure. Modules enable code reuse and provide a method for organizations to standardize deployment of common workloads such as a three-tier web application, a cloud networking environment, or a data analytics pipeline. When building Terraform modules, it is common for the module author to start with manual testing. Manual testing is performed using commands such as terraform validate for syntax validation, terraform plan to preview the execution plan, and terraform apply followed by manual inspection of resource configuration in the AWS Management Console. Manual testing is prone to human error, not scalable, and can result in unintended issues. Because modules are used by multiple teams in the organization, it is important to ensure that any changes to the modules are extensively tested before the release. In this blog post, we will show you how to validate Terraform modules and how to automate the process using a Continuous Integration/Continuous Deployment (CI/CD) pipeline.

Terraform Test

Terraform test is a new testing framework for module authors to perform unit and integration tests for Terraform modules. Terraform test can create infrastructure as declared in the module, run validation against the infrastructure, and destroy the test resources regardless if the test passes or fails. Terraform test will also provide warnings if there are any resources that cannot be destroyed. Terraform test uses the same HashiCorp Configuration Language (HCL) syntax used to write Terraform modules. This reduces the burden for modules authors to learn other tools or programming languages. Module authors run the tests using the command terraform test which is available on Terraform CLI version 1.6 or higher.

Module authors create test files with the extension *.tftest.hcl. These test files are placed in the root of the Terraform module or in a dedicated tests directory. The following elements are typically present in a Terraform tests file:

  • Provider block: optional, used to override the provider configuration, such as selecting AWS region where the tests run.
  • Variables block: the input variables passed into the module during the test, used to supply non-default values or to override default values for variables.
  • Run block: used to run a specific test scenario. There can be multiple run blocks per test file, Terraform executes run blocks in order. In each run block you specify the command Terraform (plan or apply), and the test assertions. Module authors can specify the conditions such as: length(var.items) != 0. A full list of condition expressions can be found in the HashiCorp documentation.

Terraform tests are performed in sequential order and at the end of the Terraform test execution, any failed assertions are displayed.

Basic test to validate resource creation

Now that we understand the basic anatomy of a Terraform tests file, let’s create basic tests to validate the functionality of the following Terraform configuration. This Terraform configuration will create an AWS CodeCommit repository with prefix name repo-.

# main.tf

variable "repository_name" {
  type = string
}
resource "aws_codecommit_repository" "test" {
  repository_name = format("repo-%s", var.repository_name)
  description     = "Test repository."
}

Now we create a Terraform test file in the tests directory. See the following directory structure as an example:

├── main.tf 
└── tests 
└── basic.tftest.hcl

For this first test, we will not perform any assertion except for validating that Terraform execution plan runs successfully. In the tests file, we create a variable block to set the value for the variable repository_name. We also added the run block with command = plan to instruct Terraform test to run Terraform plan. The completed test should look like the following:

# basic.tftest.hcl

variables {
  repository_name = "MyRepo"
}

run "test_resource_creation" {
  command = plan
}

Now we will run this test locally. First ensure that you are authenticated into an AWS account, and run the terraform init command in the root directory of the Terraform module. After the provider is initialized, start the test using the terraform test command.

❯ terraform test
tests/basic.tftest.hcl... in progress
run "test_resource_creation"... pass
tests/basic.tftest.hcl... tearing down
tests/basic.tftest.hcl... pass

Our first test is complete, we have validated that the Terraform configuration is valid and the resource can be provisioned successfully. Next, let’s learn how to perform inspection of the resource state.

Create resource and validate resource name

Re-using the previous test file, we add the assertion block to checks if the CodeCommit repository name starts with a string repo- and provide error message if the condition fails. For the assertion, we use the startswith function. See the following example:

# basic.tftest.hcl

variables {
  repository_name = "MyRepo"
}

run "test_resource_creation" {
  command = plan

  assert {
    condition = startswith(aws_codecommit_repository.test.repository_name, "repo-")
    error_message = "CodeCommit repository name ${var.repository_name} did not start with the expected value of ‘repo-****’."
  }
}

Now, let’s assume that another module author made changes to the module by modifying the prefix from repo- to my-repo-. Here is the modified Terraform module.

# main.tf

variable "repository_name" {
  type = string
}
resource "aws_codecommit_repository" "test" {
  repository_name = format("my-repo-%s", var.repository_name)
  description = "Test repository."
}

We can catch this mistake by running the the terraform test command again.

❯ terraform test
tests/basic.tftest.hcl... in progress
run "test_resource_creation"... fail
╷
│ Error: Test assertion failed
│
│ on tests/basic.tftest.hcl line 9, in run "test_resource_creation":
│ 9: condition = startswith(aws_codecommit_repository.test.repository_name, "repo-")
│ ├────────────────
│ │ aws_codecommit_repository.test.repository_name is "my-repo-MyRepo"
│
│ CodeCommit repository name MyRepo did not start with the expected value 'repo-***'.
╵
tests/basic.tftest.hcl... tearing down
tests/basic.tftest.hcl... fail

Failure! 0 passed, 1 failed.

We have successfully created a unit test using assertions that validates the resource name matches the expected value. For more examples of using assertions see the Terraform Tests Docs. Before we proceed to the next section, don’t forget to fix the repository name in the module (revert the name back to repo- instead of my-repo-) and re-run your Terraform test.

Testing variable input validation

When developing Terraform modules, it is common to use variable validation as a contract test to validate any dependencies / restrictions. For example, AWS CodeCommit limits the repository name to 100 characters. A module author can use the length function to check the length of the input variable value. We are going to use Terraform test to ensure that the variable validation works effectively. First, we modify the module to use variable validation.

# main.tf

variable "repository_name" {
  type = string
  validation {
    condition = length(var.repository_name) <= 100
    error_message = "The repository name must be less than or equal to 100 characters."
  }
}

resource "aws_codecommit_repository" "test" {
  repository_name = format("repo-%s", var.repository_name)
  description = "Test repository."
}

By default, when variable validation fails during the execution of Terraform test, the Terraform test also fails. To simulate this, create a new test file and insert the repository_name variable with a value longer than 100 characters.

# var_validation.tftest.hcl

variables {
  repository_name = “this_is_a_repository_name_longer_than_100_characters_7rfD86rGwuqhF3TH9d3Y99r7vq6JZBZJkhw5h4eGEawBntZmvy”
}

run “test_invalid_var” {
  command = plan
}

Notice on this new test file, we also set the command to Terraform plan, why is that? Because variable validation runs prior to Terraform apply, thus we can save time and cost by skipping the entire resource provisioning. If we run this Terraform test, it will fail as expected.

❯ terraform test
tests/basic.tftest.hcl… in progress
run “test_resource_creation”… pass
tests/basic.tftest.hcl… tearing down
tests/basic.tftest.hcl… pass
tests/var_validation.tftest.hcl… in progress
run “test_invalid_var”… fail
╷
│ Error: Invalid value for variable
│
│ on main.tf line 1:
│ 1: variable “repository_name” {
│ ├────────────────
│ │ var.repository_name is “this_is_a_repository_name_longer_than_100_characters_7rfD86rGwuqhF3TH9d3Y99r7vq6JZBZJkhw5h4eGEawBntZmvy”
│
│ The repository name must be less than or equal to 100 characters.
│
│ This was checked by the validation rule at main.tf:3,3-13.
╵
tests/var_validation.tftest.hcl… tearing down
tests/var_validation.tftest.hcl… fail

Failure! 1 passed, 1 failed.

For other module authors who might iterate on the module, we need to ensure that the validation condition is correct and will catch any problems with input values. In other words, we expect the validation condition to fail with the wrong input. This is especially important when we want to incorporate the contract test in a CI/CD pipeline. To prevent our test from failing due introducing an intentional error in the test, we can use the expect_failures attribute. Here is the modified test file:

# var_validation.tftest.hcl

variables {
  repository_name = “this_is_a_repository_name_longer_than_100_characters_7rfD86rGwuqhF3TH9d3Y99r7vq6JZBZJkhw5h4eGEawBntZmvy”
}

run “test_invalid_var” {
  command = plan

  expect_failures = [
    var.repository_name
  ]
}

Now if we run the Terraform test, we will get a successful result.

❯ terraform test
tests/basic.tftest.hcl… in progress
run “test_resource_creation”… pass
tests/basic.tftest.hcl… tearing down
tests/basic.tftest.hcl… pass
tests/var_validation.tftest.hcl… in progress
run “test_invalid_var”… pass
tests/var_validation.tftest.hcl… tearing down
tests/var_validation.tftest.hcl… pass

Success! 2 passed, 0 failed.

As you can see, the expect_failures attribute is used to test negative paths (the inputs that would cause failures when passed into a module). Assertions tend to focus on positive paths (the ideal inputs). For an additional example of a test that validates functionality of a completed module with multiple interconnected resources, see this example in the Terraform CI/CD and Testing on AWS Workshop.

Orchestrating supporting resources

In practice, end-users utilize Terraform modules in conjunction with other supporting resources. For example, a CodeCommit repository is usually encrypted using an AWS Key Management Service (KMS) key. The KMS key is provided by end-users to the module using a variable called kms_key_id. To simulate this test, we need to orchestrate the creation of the KMS key outside of the module. In this section we will learn how to do that. First, update the Terraform module to add the optional variable for the KMS key.

# main.tf

variable "repository_name" {
  type = string
  validation {
    condition = length(var.repository_name) <= 100
    error_message = "The repository name must be less than or equal to 100 characters."
  }
}

variable "kms_key_id" {
  type = string
  default = ""
}

resource "aws_codecommit_repository" "test" {
  repository_name = format("repo-%s", var.repository_name)
  description = "Test repository."
  kms_key_id = var.kms_key_id != "" ? var.kms_key_id : null
}

In a Terraform test, you can instruct the run block to execute another helper module. The helper module is used by the test to create the supporting resources. We will create a sub-directory called setup under the tests directory with a single kms.tf file. We also create a new test file for KMS scenario. See the updated directory structure:

├── main.tf
└── tests
├── setup
│ └── kms.tf
├── basic.tftest.hcl
├── var_validation.tftest.hcl
└── with_kms.tftest.hcl

The kms.tf file is a helper module to create a KMS key and provide its ARN as the output value.

# kms.tf

resource "aws_kms_key" "test" {
  description = "test KMS key for CodeCommit repo"
  deletion_window_in_days = 7
}

output "kms_key_id" {
  value = aws_kms_key.test.arn
}

The new test will use two separate run blocks. The first run block (setup) executes the helper module to generate a KMS key. This is done by assigning the command apply which will run terraform apply to generate the KMS key. The second run block (codecommit_with_kms) will then use the KMS key ARN output of the first run as the input variable passed to the main module.

# with_kms.tftest.hcl

run "setup" {
  command = apply
  module {
    source = "./tests/setup"
  }
}

run "codecommit_with_kms" {
  command = apply

  variables {
    repository_name = "MyRepo"
    kms_key_id = run.setup.kms_key_id
  }

  assert {
    condition = aws_codecommit_repository.test.kms_key_id != null
    error_message = "KMS key ID attribute value is null"
  }
}

Go ahead and run the Terraform init, followed by Terraform test. You should get the successful result like below.

❯ terraform test
tests/basic.tftest.hcl... in progress
run "test_resource_creation"... pass
tests/basic.tftest.hcl... tearing down
tests/basic.tftest.hcl... pass
tests/var_validation.tftest.hcl... in progress
run "test_invalid_var"... pass
tests/var_validation.tftest.hcl... tearing down
tests/var_validation.tftest.hcl... pass
tests/with_kms.tftest.hcl... in progress
run "create_kms_key"... pass
run "codecommit_with_kms"... pass
tests/with_kms.tftest.hcl... tearing down
tests/with_kms.tftest.hcl... pass

Success! 4 passed, 0 failed.

We have learned how to run Terraform test and develop various test scenarios. In the next section we will see how to incorporate all the tests into a CI/CD pipeline.

Terraform Tests in CI/CD Pipelines

Now that we have seen how Terraform Test works locally, let’s see how the Terraform test can be leveraged to create a Terraform module validation pipeline on AWS. The following AWS services are used:

  • AWS CodeCommit – a secure, highly scalable, fully managed source control service that hosts private Git repositories.
  • AWS CodeBuild – a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages.
  • AWS CodePipeline – a fully managed continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updates.
  • Amazon Simple Storage Service (Amazon S3) – an object storage service offering industry-leading scalability, data availability, security, and performance.
Terraform module validation pipeline Architecture. Multiple interconnected AWS services such as AWS CodeCommit, CodeBuild, CodePipeline, and Amazon S3 used to build a Terraform module validation pipeline.

Terraform module validation pipeline

In the above architecture for a Terraform module validation pipeline, the following takes place:

  • A developer pushes Terraform module configuration files to a git repository (AWS CodeCommit).
  • AWS CodePipeline begins running the pipeline. The pipeline clones the git repo and stores the artifacts to an Amazon S3 bucket.
  • An AWS CodeBuild project configures a compute/build environment with Checkov installed from an image fetched from Docker Hub. CodePipeline passes the artifacts (Terraform module) and CodeBuild executes Checkov to run static analysis of the Terraform configuration files.
  • Another CodeBuild project configured with Terraform from an image fetched from Docker Hub. CodePipeline passes the artifacts (repo contents) and CodeBuild runs Terraform command to execute the tests.

CodeBuild uses a buildspec file to declare the build commands and relevant settings. Here is an example of the buildspec files for both CodeBuild Projects:

# Checkov
version: 0.1
phases:
  pre_build:
    commands:
      - echo pre_build starting

  build:
    commands:
      - echo build starting
      - echo starting checkov
      - ls
      - checkov -d .
      - echo saving checkov output
      - checkov -s -d ./ > checkov.result.txt

In the above buildspec, Checkov is run against the root directory of the cloned CodeCommit repository. This directory contains the configuration files for the Terraform module. Checkov also saves the output to a file named checkov.result.txt for further review or handling if needed. If Checkov fails, the pipeline will fail.

# Terraform Test
version: 0.1
phases:
  pre_build:
    commands:
      - terraform init
      - terraform validate

  build:
    commands:
      - terraform test

In the above buildspec, the terraform init and terraform validate commands are used to initialize Terraform, then check if the configuration is valid. Finally, the terraform test command is used to run the configured tests. If any of the Terraform tests fails, the pipeline will fail.

For a full example of the CI/CD pipeline configuration, please refer to the Terraform CI/CD and Testing on AWS workshop. The module validation pipeline mentioned above is meant as a starting point. In a production environment, you might want to customize it further by adding Checkov allow-list rules, linting, checks for Terraform docs, or pre-requisites such as building the code used in AWS Lambda.

Choosing various testing strategies

At this point you may be wondering when you should use Terraform tests or other tools such as Preconditions and Postconditions, Check blocks or policy as code. The answer depends on your test type and use-cases. Terraform test is suitable for unit tests, such as validating resources are created according to the naming specification. Variable validations and Pre/Post conditions are useful for contract tests of Terraform modules, for example by providing error warning when input variables value do not meet the specification. As shown in the previous section, you can also use Terraform test to ensure your contract tests are running properly. Terraform test is also suitable for integration tests where you need to create supporting resources to properly test the module functionality. Lastly, Check blocks are suitable for end to end tests where you want to validate the infrastructure state after all resources are generated, for example to test if a website is running after an S3 bucket configured for static web hosting is created.

When developing Terraform modules, you can run Terraform test in command = plan mode for unit and contract tests. This allows the unit and contract tests to run quicker and cheaper since there are no resources created. You should also consider the time and cost to execute Terraform test for complex / large Terraform configurations, especially if you have multiple test scenarios. Terraform test maintains one or many state files within the memory for each test file. Consider how to re-use the module’s state when appropriate. Terraform test also provides test mocking, which allows you to test your module without creating the real infrastructure.

Conclusion

In this post, you learned how to use Terraform test and develop various test scenarios. You also learned how to incorporate Terraform test in a CI/CD pipeline. Lastly, we also discussed various testing strategies for Terraform configurations and modules. For more information about Terraform test, we recommend the Terraform test documentation and tutorial. To get hands on practice building a Terraform module validation pipeline and Terraform deployment pipeline, check out the Terraform CI/CD and Testing on AWS Workshop.

Authors

Kevon Mayers

Kevon Mayers is a Solutions Architect at AWS. Kevon is a Terraform Contributor and has led multiple Terraform initiatives within AWS. Prior to joining AWS he was working as a DevOps Engineer and Developer, and before that was working with the GRAMMYs/The Recording Academy as a Studio Manager, Music Producer, and Audio Engineer. He also owns a professional production company, MM Productions.

Welly Siauw

Welly Siauw is a Principal Partner Solution Architect at Amazon Web Services (AWS). He spends his day working with customers and partners, solving architectural challenges. He is passionate about service integration and orchestration, serverless and artificial intelligence (AI) and machine learning (ML). He has authored several AWS blog posts and actively leads AWS Immersion Days and Activation Days. Welly spends his free time tinkering with espresso machines and outdoor hiking.

How to generate security findings to help your security team with incident response simulations

Post Syndicated from Jonathan Nguyen original https://aws.amazon.com/blogs/security/how-to-generate-security-findings-to-help-your-security-team-with-incident-response-simulations/

Continually reviewing your organization’s incident response capabilities can be challenging without a mechanism to create security findings with actual Amazon Web Services (AWS) resources within your AWS estate. As prescribed within the AWS Security Incident Response whitepaper, it’s important to periodically review your incident response capabilities to make sure your security team is continually maturing internal processes and assessing capabilities within AWS. Generating sample security findings is useful to understand the finding format so you can enrich the finding with additional metadata or create and prioritize detections within your security information event management (SIEM) solution. However, if you want to conduct an end-to-end incident response simulation, including the creation of real detections, sample findings might not create actionable detections that will start your incident response process because of alerting suppressions you might have configured, or imaginary metadata (such as synthetic Amazon Elastic Compute Cloud (Amazon EC2) instance IDs), which might confuse your remediation tooling.

In this post, we walk through how to deploy a solution that provisions resources to generate simulated security findings for actual provisioned resources within your AWS account. Generating simulated security findings in your AWS account gives your security team an opportunity to validate their cyber capabilities, investigation workflow and playbooks, escalation paths across teams, and exercise any response automation currently in place.

Important: It’s strongly recommended that the solution be deployed in an isolated AWS account with no additional workloads or sensitive data. No resources deployed within the solution should be used for any purpose outside of generating the security findings for incident response simulations. Although the security findings are non-destructive to existing resources, they should still be done in isolation. For any AWS solution deployed within your AWS environment, your security team should review the resources and configurations within the code.

Conducting incident response simulations

Before deploying the solution, it’s important that you know what your goal is and what type of simulation to conduct. If you’re primarily curious about the format that active Amazon GuardDuty findings will create, you should generate sample findings with GuardDuty. At the time of this writing, Amazon Inspector doesn’t currently generate sample findings.

If you want to validate your incident response playbooks, make sure you have playbooks for the security findings the solution generates. If those playbooks don’t exist, it might be a good idea to start with a high-level tabletop exercise to identify which playbooks you need to create.

Because you’re running this sample in an AWS account with no workloads, it’s recommended to run the sample solution as a purple team exercise. Purple team exercises should be periodically run to support training for new analysts, validate existing playbooks, and identify areas of improvement to reduce the mean time to respond or identify areas where processes can be optimized with automation.

Now that you have a good understanding of the different simulation types, you can create security findings in an isolated AWS account.

Prerequisites

  1. [Recommended] A separate AWS account containing no customer data or running workloads
  2. GuardDuty, along with GuardDuty Kubernetes Protection
  3. Amazon Inspector must be enabled
  4. [Optional] AWS Security Hub can be enabled to show a consolidated view of security findings generated by GuardDuty and Inspector

Solution architecture

The architecture of the solution can be found in Figure 1.

Figure 1: Sample solution architecture diagram

Figure 1: Sample solution architecture diagram

  1. A user specifies the type of security findings to generate by passing an AWS CloudFormation parameter.
  2. An Amazon Simple Notification Service (Amazon SNS) topic is created to subscribe to findings for notifications. Subscribed users are notified of the finding through the deployed SNS topic.
  3. Upon user selection of the CloudFormation parameter, EC2 instances are provisioned to run commands to generate security findings.

    Note: If the parameter inspector is provided during deployment, then only one EC2 instance is deployed. If the parameter guardduty is provided during deployment, then two EC2 instances are deployed.

  4. For Amazon Inspector findings:
    1. The Amazon EC2 user data creates a .txt file with vulnerable images, pulls down Docker images from open source vulhub, and creates an Amazon Elastic Container Registry (Amazon ECR) repository with the vulnerable images.
    2. The EC2 user data pushes and tags the images in the ECR repository which results in Amazon Inspector findings being generated.
    3. An Amazon EventBridge cron-style trigger rule, inspector_remediation_ecr, invokes an AWS Lambda function.
    4. The Lambda function, ecr_cleanup_function, cleans up the vulnerable images in the deployed Amazon ECR repository based on applied tags and sends a notification to the Amazon SNS topic.

      Note: The ecr_cleanup_function Lambda function is also invoked as a custom resource to clean up vulnerable images during deployment. If there are issues with cleanup, the EventBridge rule continually attempts to clean up vulnerable images.

  5. For GuardDuty, the following actions are taken and resources are deployed:
    1. An AWS Identity and Access Management (IAM) user named guardduty-demo-user is created with an IAM access key that is INACTIVE.
    2. An AWS Systems Manager parameter stores the IAM access key for guardduty-demo-user.
    3. An AWS Secrets Manager secret stores the inactive IAM secret access key for guardduty-demo-user.
    4. An Amazon DynamoDB table is created, and the table name is stored in a Systems Manager parameter to be referenced within the EC2 user data.
    5. An Amazon Simple Storage Service (Amazon S3) bucket is created, and the bucket name is stored in a Systems Manager parameter to be referenced within the EC2 user data.
    6. A Lambda function adds a threat list to GuardDuty that includes the IP addresses of the EC2 instances deployed as part of the sample.
    7. EC2 user data generates GuardDuty findings for the following:
      1. Amazon Elastic Kubernetes Service (Amazon EKS)
        1. Installs eksctl from GitHub.
        2. Creates an EC2 key pair.
        3. Creates an EKS cluster (dependent on availability zone capacity).
        4. Updates EKS cluster configuration to make a dashboard public.
      2. DynamoDB
        1. Adds an item to the DynamoDB table for Joshua Tree.
      3. EC2
        1. Creates an AWS CloudTrail trail named guardduty-demo-trail-<GUID> and subsequently deletes the same CloudTrail trail. The <GUID> is randomly generated by using the $RANDOM function
        2. Runs portscan on 172.31.37.171 (an RFC 1918 private IP address) and private IP of the EKS Deployment EC2 instance provisioned as part of the sample. Port scans are primarily used by bad actors to search for potential vulnerabilities. The target of the port scans are internal IP addresses and do not leave the sample VPC deployed.
        3. Curls DNS domains that are labeled for bitcoin, command and control, and other domains associated with known threats.
      4. Amazon S3
        1. Disables Block Public Access and server access logging for the S3 bucket provisioned as part of the solution.
      5. IAM
        1. Deletes the existing account password policy and creates a new password policy with a minimum length of six characters.
  6. The following Amazon EventBridge rules are created:
    1. guardduty_remediation_eks_rule – When a GuardDuty finding for EKS is created, a Lambda function attempts to delete the EKS resources. Subscribed users are notified of the finding through the deployed SNS topic.
    2. guardduty_remediation_credexfil_rule – When a GuardDuty finding for InstanceCredentialExfiltration is created, a Lambda function is used to revoke the IAM role’s temporary security credentials and AWS permissions. Subscribed users are notified of the finding through the deployed SNS topic.
    3. guardduty_respond_IAMUser_rule – When a GuardDuty finding for IAM is created, subscribed users are notified through the deployed SNS topic. There is no remediation activity triggered by this rule.
    4. Guardduty_notify_S3_rule – When a GuardDuty finding for Amazon S3 is created, subscribed users are notified through the deployed Amazon SNS topic. This rule doesn’t invoke any remediation activity.
  7. The following Lambda functions are created:
    1. guardduty_iam_remediation_function – This function revokes active sessions and sends a notification to the SNS topic.
    2. eks_cleanup_function – This function deletes the EKS resources in the EKS CloudFormation template.

      Note: Upon attempts to delete the overall sample CloudFormation stack, this runs to delete the EKS CloudFormation template.

  8. An S3 bucket stores EC2 user data scripts run from the EC2 instances

Solution deployment

You can deploy the SecurityFindingGeneratorStack solution by using either the AWS Management Console or the AWS Cloud Development Kit (AWS CDK).

Option 1: Deploy the solution with AWS CloudFormation using the console

Use the console to sign in to your chosen AWS account and then choose the Launch Stack button to open the AWS CloudFormation console pre-loaded with the template for this solution. It takes approximately 10 minutes for the CloudFormation stack to complete.

Launch Stack

Option 2: Deploy the solution by using the AWS CDK

You can find the latest code for the SecurityFindingGeneratorStack solution in the SecurityFindingGeneratorStack GitHub repository, where you can also contribute to the sample code. For instructions and more information on using the AWS Cloud Development Kit (AWS CDK), see Get Started with AWS CDK.

To deploy the solution by using the AWS CDK

  1. To build the app when navigating to the project’s root folder, use the following commands:
    npm install -g aws-cdk-lib
    npm install

  2. Run the following command in your terminal while authenticated in your separate deployment AWS account to bootstrap your environment. Be sure to replace <INSERT_AWS_ACCOUNT> with your account number and replace <INSERT_REGION> with the AWS Region that you want the solution deployed to.
    cdk bootstrap aws://<INSERT_AWS_ACCOUNT>/<INSERT_REGION>

  3. Deploy the stack to generate findings based on a specific parameter that is passed. The following parameters are available:
    1. inspector
    2. guardduty
    cdk deploy SecurityFindingGeneratorStack –parameters securityserviceuserdata=inspector

Reviewing security findings

After the solution successfully deploys, security findings should start appearing in your AWS account’s GuardDuty console within a couple of minutes.

Amazon GuardDuty findings

In order to create a diverse set of GuardDuty findings, the solution uses Amazon EC2 user data to run scripts. Those scripts can be found in the sample repository. You can also review and change scripts as needed to fit your use case or to remove specific actions if you don’t want specific resources to be altered or security findings to be generated.

A comprehensive list of active GuardDuty finding types and details for each finding can be found in the Amazon GuardDuty user guide. In this solution, activities which cause the following GuardDuty findings to be generated, are performed:

To generate the EKS security findings, the EKS Deployment EC2 instance is running eksctl commands that deploy CloudFormation templates. If the EKS cluster doesn’t deploy, it might be because of capacity restraints in a specific Availability Zone. If this occurs, manually delete the failed EKS CloudFormation templates.

If you want to create the EKS cluster and security findings manually, you can do the following:

  1. Sign in to the Amazon EC2 console.
  2. Connect to the EKS Deployment EC2 instance using an IAM role that has access to start a session through Systems Manager. After connecting to the ssm-user, issue the following commands in the Session Manager session:
    1. sudo chmod 744 /home/ec2-user/guardduty-script.sh
    2. chown ec2-user /home/ec2-user/guardduty-script.sh
    3. sudo /home/ec2-user/guardduty-script.sh

It’s important that your security analysts have an incident response playbook. If playbooks don’t exist, you can refer to the GuardDuty remediation recommendations or AWS sample incident response playbooks to get started building playbooks.

Amazon Inspector findings

The findings for Amazon Inspector are generated by using the open source Vulhub collection. The open source collection has pre-built vulnerable Docker environments that pull images into Amazon ECR.

The Amazon Inspector findings that are created vary depending on what exists within the open source library at deployment time. The following are examples of findings you will see in the console:

For Amazon Inspector findings, you can refer to parts 1 and 2 of Automate vulnerability management and remediation in AWS using Amazon Inspector and AWS Systems Manager.

Clean up

If you deployed the security finding generator solution by using the Launch Stack button in the console or the CloudFormation template security_finding_generator_cfn, do the following to clean up:

  1. In the CloudFormation console for the account and Region where you deployed the solution, choose the SecurityFindingGeneratorStack stack.
  2. Choose the option to Delete the stack.

If you deployed the solution by using the AWS CDK, run the command cdk destroy.

Important: The solution uses eksctl to provision EKS resources, which deploys additional CloudFormation templates. There are custom resources within the solution that will attempt to delete the provisioned CloudFormation templates for EKS. If there are any issues, you should verify and manually delete the following CloudFormation templates:

  • eksctl-GuardDuty-Finding-Demo-cluster
  • eksctl-GuardDuty-Finding-Demo-addon-iamserviceaccount-kube-system-aws-node
  • eksctl-GuardDuty-Finding-Demo-nodegroup-ng-<GUID>

Conclusion

In this blog post, I showed you how to deploy a solution to provision resources in an AWS account to generate security findings. This solution provides a technical framework to conduct periodic simulations within your AWS environment. By having real, rather than simulated, security findings, you can enable your security teams to interact with actual resources and validate existing incident response processes. Having a repeatable mechanism to create security findings also provides your security team the opportunity to develop and test automated incident response capabilities in your AWS environment.

AWS has multiple services to assist with increasing your organization’s security posture. Security Hub provides native integration with AWS security services as well as partner services. From Security Hub, you can also implement automation to respond to findings using custom actions as seen in Use Security Hub custom actions to remediate S3 resources based on Amazon Macie discovery results. In part two of a two-part series, you can learn how to use Amazon Detective to investigate security findings in EKS clusters. Amazon Security Lake automatically normalizes and centralizes your data from AWS services such as Security Hub, AWS CloudTrail, VPC Flow Logs, and Amazon Route 53, as well as custom sources to provide a mechanism for comprehensive analysis and visualizations.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the Incident Response re:Post or contact AWS Support.

Author

Jonathan Nguyen

Jonathan is a Principal Security Architect at AWS. His background is in AWS security with a focus on threat detection and incident response. He helps enterprise customers develop a comprehensive AWS security strategy and deploy security solutions at scale, and trains customers on AWS security best practices.

Improve healthcare services through patient 360: A zero-ETL approach to enable near real-time data analytics

Post Syndicated from Saeed Barghi original https://aws.amazon.com/blogs/big-data/improve-healthcare-services-through-patient-360-a-zero-etl-approach-to-enable-near-real-time-data-analytics/

Healthcare providers have an opportunity to improve the patient experience by collecting and analyzing broader and more diverse datasets. This includes patient medical history, allergies, immunizations, family disease history, and individuals’ lifestyle data such as workout habits. Having access to those datasets and forming a 360-degree view of patients allows healthcare providers such as claim analysts to see a broader context about each patient and personalize the care they provide for every individual. This is underpinned by building a complete patient profile that enables claim analysts to identify patterns, trends, potential gaps in care, and adherence to care plans. They can then use the result of their analysis to understand a patient’s health status, treatment history, and past or upcoming doctor consultations to make more informed decisions, streamline the claim management process, and improve operational outcomes. Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process.

AWS has invested in a zero-ETL (extract, transform, and load) future so that builders can focus more on creating value from data, instead of having to spend time preparing data for analysis. The solution proposed in this post follows a zero-ETL approach to data integration to facilitate near real-time analytics and deliver a more personalized patient experience. The solution uses AWS services such as AWS HealthLake, Amazon Redshift, Amazon Kinesis Data Streams, and AWS Lake Formation to build a 360 view of patients. These services enable you to collect and analyze data in near real time and put a comprehensive data governance framework in place that uses granular access control to secure sensitive data from unauthorized users.

Zero-ETL refers to a set of features on the AWS Cloud that enable integrating different data sources with Amazon Redshift:

Solution overview

Organizations in the healthcare industry are currently spending a significant amount of time and money on building complex ETL pipelines for data movement and integration. This means data will be replicated across multiple data stores via bespoke and in some cases hand-written ETL jobs, resulting in data inconsistency, latency, and potential security and privacy breaches.

With support for querying cross-account Apache Iceberg tables via Amazon Redshift, you can now build a more comprehensive patient-360 analysis by querying all patient data from one place. This means you can seamlessly combine information such as clinical data stored in HealthLake with data stored in operational databases such as a patient relationship management system, together with data produced from wearable devices in near real-time. Having access to all this data enables healthcare organizations to form a holistic view of patients, improve care coordination across multiple organizations, and provide highly personalized care for each individual.

The following diagram depicts the high-level solution we build to achieve these outcomes.

Deploy the solution

You can use the following AWS CloudFormation template to deploy the solution components:

This stack creates the following resources and necessary permissions to integrate the services:

AWS Solution setup

AWS HealthLake

AWS HealthLake enables organizations in the health industry to securely store, transform, transact, and analyze health data. It stores data in HL7 FHIR format, which is an interoperability standard designed for quick and efficient exchange of health data. When you create a HealthLake data store, a Fast Healthcare Interoperability Resources (FHIR) data repository is made available via a RESTful API endpoint. Simultaneously and as part of AWS HealthLake managed service, the nested JSON FHIR data undergoes an ETL process and is stored in Apache Iceberg open table format in Amazon S3.

To create an AWS HealthLake data store, refer to Getting started with AWS HealthLake. Make sure to select the option Preload sample data when creating your data store.

In real-world scenarios and when you use AWS HealthLake in production environments, you don’t need to load sample data into your AWS HealthLake data store. Instead, you can use FHIR REST API operations to manage and search resources in your AWS HealthLake data store.

We use two tables from the sample data stored in HealthLake: patient and allergyintolerance.

Query AWS HealthLake tables with Redshift Serverless

Amazon Redshift is the data warehousing service available on the AWS Cloud that provides up to six times better price-performance than any other cloud data warehouses in the market, with a fully managed, AI-powered, massively parallel processing (MPP) data warehouse built for performance, scale, and availability. With continuous innovations added to Amazon Redshift, it is now more than just a data warehouse. It enables organizations of different sizes and in different industries to access all the data they have in their AWS environments and analyze it from one single location with a set of features under the zero-ETL umbrella. Amazon Redshift integrates with AWS HealthLake and data lakes through Redshift Spectrum and Amazon S3 auto-copy features, enabling you to query data directly from files on Amazon S3.

Query AWS HealthLake data with Amazon Redshift

Amazon Redshift makes it straightforward to query the data stored in S3-based data lakes with automatic mounting of an AWS Glue Data Catalog in the Redshift query editor v2. This means you no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog. To get started with this feature, see Querying the AWS Glue Data Catalog. After it is set up and you’re connected to the Redshift query editor v2, complete the following steps:

  1. Validate that your tables are visible in the query editor V2. The Data Catalog objects are listed under the awsdatacatalog database.

FHIR data stored in AWS HealthLake is highly nested. To learn about how to un-nest semi-structured data with Amazon Redshift, see Tutorial: Querying nested data with Amazon Redshift Spectrum.

  1. Use the following query to un-nest the allergyintolerance and patient tables, join them together, and get patient details and their allergies:
    WITH patient_allergy AS 
    (
        SELECT
            resourcetype, 
            c AS allery_category,
            a."patient"."reference",
            SUBSTRING(a."patient"."reference", 9, LEN(a."patient"."reference")) AS patient_id,
            a.recordeddate AS allergy_record_date,
            NVL(cd."code", 'NA') AS allergy_code,
            NVL(cd.display, 'NA') AS allergy_description
    
        FROM "awsdatacatalog"."datastore_01_179674d36391d68926a8d74c12599306_healthlake_view"."allergyintolerance" a
                LEFT JOIN a.category c ON TRUE
                LEFT JOIN a.reaction r ON TRUE
                LEFT JOIN r.manifestation m ON TRUE
                LEFT JOIN m.coding cd ON TRUE
    ), patinet_info AS
    (
        SELECT id,
                gender,
                g as given_name,
                n.family as family_name,
                pr as prefix
    
        FROM "awsdatacatalog"."datastore_01_179674d36391d68926a8d74c12599306_healthlake_view"."patient" p
                LEFT JOIN p.name n ON TRUE
                LEFT JOIN n.given g ON TRUE
                LEFT JOIN n.prefix pr ON TRUE
    )
    SELECT DISTINCT p.id, 
            p.gender, 
            p.prefix,
            p.given_name,
            p.family_name,
            pa.allery_category,
            pa.allergy_code,
            pa.allergy_description
    from patient_allergy pa
        JOIN patinet_info p
            ON pa.patient_id = p.id
    ORDER BY p.id, pa.allergy_code
    ;
    

To eliminate the need for Amazon Redshift to un-nest data every time a query is run, you can create a materialized view to hold un-nested and flattened data. Materialized views are an effective mechanism to deal with complex and repeating queries. They contain a precomputed result set, based on a SQL query over one or more base tables. You can issue SELECT statements to query a materialized view, in the same way that you can query other tables or views in the database.

  1. Use the following SQL to create a materialized view. You use it later to build a complete view of patients:
    CREATE MATERIALIZED VIEW patient_allergy_info AUTO REFRESH YES AS
    WITH patient_allergy AS 
    (
        SELECT
            resourcetype, 
            c AS allery_category,
            a."patient"."reference",
            SUBSTRING(a."patient"."reference", 9, LEN(a."patient"."reference")) AS patient_id,
            a.recordeddate AS allergy_record_date,
            NVL(cd."code", 'NA') AS allergy_code,
            NVL(cd.display, 'NA') AS allergy_description
    
        FROM
            "awsdatacatalog"."datastore_01_179674d36391d68926a8d74c12599306_healthlake_view"."allergyintolerance" a
                LEFT JOIN a.category c ON TRUE
                LEFT JOIN a.reaction r ON TRUE
                LEFT JOIN r.manifestation m ON TRUE
                LEFT JOIN m.coding cd ON TRUE
    ), patinet_info AS
    (
        SELECT id,
                gender,
                g as given_name,
                n.family as family_name,
                pr as prefix
    
        FROM "awsdatacatalog"."datastore_01_179674d36391d68926a8d74c12599306_healthlake_view"."patient" p
                LEFT JOIN p.name n ON TRUE
                LEFT JOIN n.given g ON TRUE
                LEFT JOIN n.prefix pr ON TRUE
    )
    SELECT DISTINCT p.id, 
            p.gender, 
            p.prefix,
            p.given_name,
            p.family_name,
            pa.allery_category,
            pa.allergy_code,
            pa.allergy_description
    from patient_allergy pa
        JOIN patinet_info p
            ON pa.patient_id = p.id
    ORDER BY p.id, pa.allergy_code
    ;
    

You have confirmed you can query data in AWS HealthLake via Amazon Redshift. Next, you set up zero-ETL integration between Amazon Redshift and Amazon Aurora MySQL.

Set up zero-ETL integration between Amazon Aurora MySQL and Redshift Serverless

Applications such as front-desk software, which are used to schedule appointments and register new patients, store data in OLTP databases such as Aurora. To get data out of OLTP databases and have them ready for analytics use cases, data teams might have to spend a considerable amount of time to build, test, and deploy ETL jobs that are complex to maintain and scale.

With the Amazon Redshift zero-ETL integration with Amazon Aurora MySQL, you can run analytics on the data stored in OLTP databases and combine them with the rest of the data in Amazon Redshift and AWS HealthLake in near real time. In the next steps in this section, we connect to a MySQL database and set up zero-ETL integration with Amazon Redshift.

Connect to an Aurora MySQL database and set up data

Connect to your Aurora MySQL database using your editor of choice using AdminUsername and AdminPassword that you entered when running the CloudFormation stack. (For simplicity, it is the same for Amazon Redshift and Aurora.)

When you’re connected to your database, complete the following steps:

  1. Create a new database by running the following command:
    CREATE DATABASE front_desk_app_db;

  2. Create a new table. This table simulates storing patient information as they visit clinics and other healthcare centers. For simplicity and to demonstrate specific capabilities, we assume that patient IDs are the same in AWS HealthLake and the front-of-office application. In real-world scenarios, this can be a hashed version of a national health care number:
    CREATE TABLE patient_appointment ( 
          patient_id varchar(250), 
          gender varchar(1), 
          date_of_birth date, 
          appointment_datetime datetime, 
          phone_number varchar(15), 
          PRIMARY KEY (patient_id, appointment_datetime) 
    );

Having a primary key in the table is mandatory for zero-ETL integration to work.

  1. Insert new records into the source table in the Aurora MySQL database. To demonstrate the required functionalities, make sure the patient_id of the sample records inserted into the MySQL database match the ones in AWS HealthLake. Replace [patient_id_1] and [patient_id_2] in the following query with the ones from the Redshift query you ran previously (the query that joined allergyintolerance and patient):
    INSERT INTO front_desk_app_db.patient_appointment (patient_id, gender, date_of_birth, appointment_datetime, phone_number)
    
    VALUES([PATIENT_ID_1], 'F', '1988-7-04', '2023-12-19 10:15:00', '0401401401'),
    ([PATIENT_ID_1], 'F', '1988-7-04', '2023-09-19 11:00:00', '0401401401'),
    ([PATIENT_ID_1], 'F', '1988-7-04', '2023-06-06 14:30:00', '0401401401'),
    ([PATIENT_ID_2], 'F', '1972-11-14', '2023-12-19 08:15:00', '0401401402'),
    ([PATIENT_ID_2], 'F', '1972-11-14', '2023-01-09 12:15:00', '0401401402');

Now that your source table is populated with sample records, you can set up zero-ETL and have data ingested into Amazon Redshift.

Set up zero-ETL integration between Amazon Aurora MySQL and Amazon Redshift

Complete the following steps to create your zero-ETL integration:

  1. On the Amazon RDS console, choose Databases in the navigation pane.
  2. Choose the DB identifier of your cluster (not the instance).
  3. On the Zero-ETL Integration tab, choose Create zero-ETL integration.
  4. Follow the steps to create your integration.

Create a Redshift database from the integration

Next, you create a target database from the integration. You can do this by running a couple of simple SQL commands on Amazon Redshift. Log in to the query editor V2 and run the following commands:

  1. Get the integration ID of the zero-ETL you set up between your source database and Amazon Redshift:
    SELECT * FROM svv_integration;

  2. Create a database using the integration ID:
    CREATE DATABASE ztl_demo FROM INTEGRATION '[INTEGRATION_ID ';

  3. Query the database and validate that a new table is created and populated with data from your source MySQL database:
    SELECT * FROM ztl_demo.front_desk_app_db.patient_appointment;

It might take a few seconds for the first set of records to appear in Amazon Redshift.

This shows that the integration is working as expected. To validate it further, you can insert a new record in your Aurora MySQL database, and it will be available in Amazon Redshift for querying in near real time within a few seconds.

Set up streaming ingestion for Amazon Redshift

Another aspect of zero-ETL on AWS, for real-time and streaming data, is realized through Amazon Redshift Streaming Ingestion. It provides low-latency, high-speed ingestion of streaming data from Kinesis Data Streams and Amazon MSK. It lowers the effort required to have data ready for analytics workloads, lowers the cost of running such workloads on the cloud, and decreases the operational burden of maintaining the solution.

In the context of healthcare, understanding an individual’s exercise and movement patterns can help with overall health assessment and better treatment planning. In this section, you send simulated data from wearable devices to Kinesis Data Streams and integrate it with the rest of the data you already have access to from your Redshift Serverless data warehouse.

For step-by-step instructions, refer to Real-time analytics with Amazon Redshift streaming ingestion. Note the following steps when you set up streaming ingestion for Amazon Redshift:

  1. Select wearables_stream and use the following template when sending data to Amazon Kinesis Data Streams via Kinesis Data Generator, to simulate data generated by wearable devices. Replace [PATIENT_ID_1] and [PATIENT_ID_2] with the patient IDs you earlier when inserting new records into your Aurora MySQL table:
    {
       "patient_id": "{{random.arrayElement(["[PATIENT_ID_1]"," [PATIENT_ID_2]"])}}",
       "steps_increment": "{{random.arrayElement(
          [0,1]
       )}}",
       "heart_rate": {{random.number( 
          {
             "min":45,
             "max":120}
       )}}
    }

  2. Create an external schema called from_kds by running the following query and replacing [IAM_ROLE_ARN] with the ARN of the role created by the CloudFormation stack (Patient360BlogRole):
    CREATE EXTERNAL SCHEMA from_kds
    FROM KINESIS
    IAM_ROLE '[IAM_ROLE_ARN]';

  3. Use the following SQL when creating a materialized view to consume data from the stream:
    CREATE MATERIALIZED VIEW patient_wearable_data AUTO REFRESH YES AS 
    SELECT approximate_arrival_timestamp, 
          JSON_PARSE(kinesis_data) as Data FROM from_kds."wearables_stream" 
    WHERE CAN_JSON_PARSE(kinesis_data);

  4. To validate that streaming ingestion works as expected, refresh the materialized view to get the data you already sent to the data stream and query the table to make sure data has landed in Amazon Redshift:
    REFRESH MATERIALIZED VIEW patient_wearable_data;
    
    SELECT *
    FROM patient_wearable_data
    ORDER BY approximate_arrival_timestamp DESC;

Query and analyze patient wearable data

The results in the data column of the preceding query are in JSON format. Amazon Redshift makes it straightforward to work with semi-structured data in JSON format. It uses PartiQL language to offer SQL-compatible access to relational, semi-structured, and nested data. Use the following query to flatten data:

SELECT data."patient_id"::varchar AS patient_id,       
      data."steps_increment"::integer as steps_increment,       
      data."heart_rate"::integer as heart_rate, 
      approximate_arrival_timestamp 
FROM patient_wearable_data 
ORDER BY approximate_arrival_timestamp DESC;

The result looks like the following screenshot.

Now that you know how to flatten JSON data, you can analyze it further. Use the following query to get the number of minutes a patient has been physically active per day, based on their heart rate (greater than 80):

WITH patient_wearble_flattened AS
(
   SELECT data."patient_id"::varchar AS patient_id,
      data."steps_increment"::integer as steps_increment,
      data."heart_rate"::integer as heart_rate,
      approximate_arrival_timestamp,
      DATE(approximate_arrival_timestamp) AS date_received,
      extract(hour from approximate_arrival_timestamp) AS    hour_received,
      extract(minute from approximate_arrival_timestamp) AS minute_received
   FROM patient_wearable_data
), patient_active_minutes AS
(
   SELECT patient_id,
      date_received,
      hour_received,
      minute_received,
      avg(heart_rate) AS heart_rate
   FROM patient_wearble_flattened
   GROUP BY patient_id,
      date_received,
      hour_received,
      minute_received
   HAVING avg(heart_rate) > 80
)
SELECT patient_id,
      date_received,
      COUNT(heart_rate) AS active_minutes_count
FROM patient_active_minutes
GROUP BY patient_id,
      date_received
ORDER BY patient_id,
      date_received;

Create a complete patient 360

Now that you are able to query all patient data with Redshift Serverless, you can combine the three datasets you used in this post and form a comprehensive patient 360 view with the following query:

WITH patient_appointment_info AS
(
      SELECT "patient_id",
         "gender",
         "date_of_birth",
         "appointment_datetime",
         "phone_number"
      FROM ztl_demo.front_desk_app_db.patient_appointment
),
patient_wearble_flattened AS
(
      SELECT data."patient_id"::varchar AS patient_id,
         data."steps_increment"::integer as steps_increment,
         data."heart_rate"::integer as heart_rate,
         approximate_arrival_timestamp,
         DATE(approximate_arrival_timestamp) AS date_received,
         extract(hour from approximate_arrival_timestamp) AS hour_received,
         extract(minute from approximate_arrival_timestamp) AS minute_received
      FROM patient_wearable_data
), patient_active_minutes AS
(
      SELECT patient_id,
         date_received,
         hour_received,
         minute_received,
         avg(heart_rate) AS heart_rate
      FROM patient_wearble_flattened
      GROUP BY patient_id,
         date_received,
         hour_received,
         minute_received
         HAVING avg(heart_rate) > 80
), patient_active_minutes_count AS
(
      SELECT patient_id,
         date_received,
         COUNT(heart_rate) AS active_minutes_count
      FROM patient_active_minutes
      GROUP BY patient_id,
         date_received
)
SELECT pai.patient_id,
      pai.gender,
      pai.prefix,
      pai.given_name,
      pai.family_name,
      pai.allery_category,
      pai.allergy_code,
      pai.allergy_description,
      ppi.date_of_birth,
      ppi.appointment_datetime,
      ppi.phone_number,
      pamc.date_received,
      pamc.active_minutes_count
FROM patient_allergy_info pai
      LEFT JOIN patient_active_minutes_count pamc
            ON pai.patient_id = pamc.patient_id
      LEFT JOIN patient_appointment_info ppi
            ON pai.patient_id = ppi.patient_id
GROUP BY pai.patient_id,
      pai.gender,
      pai.prefix,
      pai.given_name,
      pai.family_name,
      pai.allery_category,
      pai.allergy_code,
      pai.allergy_description,
      ppi.date_of_birth,
      ppi.appointment_datetime,
      ppi.phone_number,
      pamc.date_received,
      pamc.active_minutes_count
ORDER BY pai.patient_id,
      pai.gender,
      pai.prefix,
      pai.given_name,
      pai.family_name,
      pai.allery_category,
      pai.allergy_code,
      pai.allergy_description,
      ppi.date_of_birth DESC,
      ppi.appointment_datetime DESC,
      ppi.phone_number DESC,
      pamc.date_received,
      pamc.active_minutes_count

You can use the solution and queries used here to expand the datasets used in your analysis. For example, you can include other tables from AWS HealthLake as needed.

Clean up

To clean up resources you created, complete the following steps:

  1. Delete the zero-ETL integration between Amazon RDS and Amazon Redshift.
  2. Delete the CloudFormation stack.
  3. Delete AWS HealthLake data store

Conclusion

Forming a comprehensive 360 view of patients by integrating data from various different sources offers numerous benefits for organizations operating in the healthcare industry. It enables healthcare providers to gain a holistic understanding of a patient’s medical journey, enhances clinical decision-making, and allows for more accurate diagnosis and tailored treatment plans. With zero-ETL features for data integration on AWS, it is effortless to build a view of patients securely, cost-effectively, and with minimal effort.

You can then use visualization tools such as Amazon QuickSight to build dashboards or use Amazon Redshift ML to enable data analysts and database developers to train machine learning (ML) models with the data integrated through Amazon Redshift zero-ETL. The result is a set of ML models that are trained with a broader view into patients, their medical history, and their lifestyle, and therefore enable you make more accurate predictions about their upcoming health needs.


About the Authors

Saeed Barghi is a Sr. Analytics Specialist Solutions Architect specializing in architecting enterprise data platforms. He has extensive experience in the fields of data warehousing, data engineering, data lakes, and AI/ML. Based in Melbourne, Australia, Saeed works with public sector customers in Australia and New Zealand.

Satesh Sonti is a Sr. Analytics Specialist Solutions Architect based out of Atlanta, specialized in building enterprise data platforms, data warehousing, and analytics solutions. He has over 17 years of experience in building data assets and leading complex data platform programs for banking and insurance clients across the globe.

Use Amazon Verified Permissions for fine-grained authorization at scale

Post Syndicated from Abhishek Panday original https://aws.amazon.com/blogs/security/use-amazon-verified-permissions-for-fine-grained-authorization-at-scale/

Implementing user authentication and authorization for custom applications requires significant effort. For authentication, customers often use an external identity provider (IdP) such as Amazon Cognito. Yet, authorization logic is typically implemented in code. This code can be prone to errors, especially as permissions models become complex, and presents significant challenges when auditing permissions and deciding who has access to what. As a result, within Common Weakness Enumeration’s (CWE’s) list of the Top 25 Most Dangerous Software Weaknesses for 2023, four are related to incorrect authorization.

At re:Inforce 2023, we launched Amazon Verified Permissions, a fine-grained permissions management service for the applications you build. Verified Permissions centralizes permissions in a policy store and lets developers use those permissions to authorize user actions within their applications. Permissions are expressed as Cedar policies. You can learn more about the benefits of moving your permissions centrally and expressing them as policies in Policy-based access control in application development with Amazon Verified Permissions.

In this post, we explore how you can provide a faster and richer user experience while still authorizing all requests in the application. You will learn two techniques—bulk authorization and response caching—to improve the efficiency of your applications. We describe how you can apply these techniques when listing authorized resources and actions and loading multiple components on webpages.

Use cases

You can use Verified Permissions to enforce permissions that determine what the user is able to see at the level of the user interface (UI), and what the user is permitted to do at the level of the API.

  1. UI permissions enable developers to control what a user is allowed see in the application. Developers enforce permissions in the UI to control the list of resources a user can see and the actions they can take. For example, a UI-level permission in a banking application might determine whether a transfer funds button is enabled for a given account.
  2. API permissions enable developers to control what a user is allowed to do in an application. Developers control access to individual API calls made by an application on behalf of the user. For example, an API-level permission in a banking application might determine whether a user is permitted to initiate a funds transfer from an account.

Cedar provides consistent and readable policies that can be used at both the level of the UI and the API. For example, a single policy can be checked at the level of the UI to determine whether to show the transfer funds button and checked at the level of the API to determine authority to initiate the funds transfer.

Challenges

Verified Permissions can be used for implementing fine-grained API permissions. Customer applications can use Verified Permissions to authorize API requests, based on centrally managed Cedar policies, with low latency. Applications authorize such requests by calling the IsAuthorized API of the service, and the response contains whether the request is allowed or denied. Customers are happy with the latency of individual authorization requests, but have asked us to help them improve performance for use cases that require multiple authorization requests. They typically mention two use cases:

  • Compound authorization: Compound authorization is needed when one high-level API action involves many low-level actions, each of which has its own permissions. This requires the application to make multiple requests to Verified Permissions to authorize the user action. For example, in a banking application, loading a credit card statement requires three API calls: GetCreditCardDetails, GetCurrentStatement, and GetCreditLimit. This requires three calls to Verified Permissions, one for each API call.
  • UI permissions: Developers implement UI permissions by calling the same authorization API for every possible resource a principal can access. Each request involves an API call, and the UI can only be presented after all of them have completed. Alternatively, for a resource-centric view, the application can make the call for multiple principals to determine which ones have access.

Solution

In this post, we show you two techniques to optimize the application’s latency based on API permissions and UI permissions.

  1. Batch authorization allows you to make up to 30 authorization decisions in a single API call. This feature was released in November 2023. See the what’s new post and API specifications to learn more.
  2. Response caching enables you to cache authorization responses in a policy enforcement point such as Amazon API Gateway, AWS AppSync, or AWS Lambda. You can cache responses using native enforcement point caches (for example, API Gateway caching) or managed caching services such as Amazon ElastiCache.

Solving for enforcing fine grained permissions while delivering a great user experience

You can use UI permissions to authorize what resources and actions a user can view in an application. We see developers implementing these controls by first generating a small set of resources based on database filters and then further reducing the set down to authorized resources by checking permissions on each resource using Verified Permissions. For example, when a user of a business banking system tries to view balances on company bank accounts, the application first filters the list to the set of bank accounts for that company. The application then filters the list further to only include the accounts that the user is authorized to view by making an API request to Verified Permissions for each account in the list. With batch authorization, the application can make a single API call to Verified Permissions to filter the list down to the authorized accounts.

Similarly, you can use UI permissions to determine what components of a page or actions should be visible to users of the application. For example, in a banking application, the application wants to control the sub-products (such as credit card, bank account, or stock trading) visible to a user or only display authorized actions (such as transfer or change address) when displaying an account overview page. Customers want to use Verified Permissions to determine which components of the page to display, but that can adversely impact the user experience (UX) if they make multiple API calls to build the page. With batch authorization, you can make one call to Verified Permissions to determine permissions for all components of the page. This enables you to provide a richer experience in your applications by displaying only the components that the user is allowed to access while maintaining low page load latency.

Solving for enforcing permissions for every API call without impacting performance

Compound authorization is where a single user action results in a sequence of multiple authorization calls. You can use bulk authorization combined with response caching to improve efficiency. The application makes a single bulk authorization request to Verified Permissions to determine whether each of the component API calls are permitted and the response is cached. This cache is then referenced for each component’s API call in the sequence.

Sample application – Use cases, personas, and permissions

We’re using an online order management application for a toy store to demonstrate how you can apply batch authorization and response caching to improve UX and application performance.

One function of the application is to enable employees in a store to process online orders.

Personas

The application is used by two types of users:

  • Pack associates are responsible for picking, packing, and shipping orders. They’re assigned to a specific department.
  • Store managers are responsible for overseeing the operations of a store.

Use cases

The application supports these use cases:

  1. Listing orders: Users can list orders. A user should only see the orders for which they have view permissions.
    • Pack associates can list all orders of their department.
    • Store managers can list all orders of their store.

    Figure 1 shows orders for Julian, who is a pack associate in the Soft Toy department

    Figure 1: Orders for Julian in the Soft Toy department

    Figure 1: Orders for Julian in the Soft Toy department

  2. Order actions: Users can take some actions on an order. The application enables the relevant UI elements based on the user’s permissions.
    • Pack associates can select Get Box Size and Mark as Shipped, as shown in Figure 2.
    • Store managers can select Get Box Size, Mark as Shipped, Cancel Order, and Route to different warehouse.
    Figure 2: Actions available to Julian as a pack associate

    Figure 2: Actions available to Julian as a pack associate

  3. Viewing an order: Users can view the details of a specific order. When a user views an order, the application loads the details, label, and receipt. Figure 3 shows the available actions for Julian who is a pack associate.
    Figure 3: Order Details for Julian, showing permitted actions

    Figure 3: Order Details for Julian, showing permitted actions

Policy design

The application uses Verified Permissions as a centralized policy store. These policies are expressed in Cedar. The application uses the Role management using policy templates approach for implementing role-based access controls. We encourage you to read best practices for using role-based access control in Cedar to understand if the approach fits your use case.

In the sample application, the policy template for the store owner role looks like the following:

permit (
        principal == ?principal,
        action in [
                avp::sample::toy::store::Action::"OrderActions",
                avp::sample::toy::store::Action::"AddPackAssociate",
                avp::sample::toy::store::Action::"AddStoreManager",
                avp::sample::toy::store::Action::"ListPackAssociates",
                avp::sample::toy::store::Action::"ListStoreManagers"
        ],
        resource in ?resource
);

When a user is assigned a role, the application creates a policy from the corresponding template by passing the user and store. For example, the policy created for the store owner is as follows:

permit (
    principal ==  avp::sample::toy::store::User::"test_user_pool|sub_store_manager_user", 
    action in  [
                avp::sample::toy::store::Action::"OrderActions",
                avp::sample::toy::store::Action::"AddPackAssociate",
                avp::sample::toy::store::Action::"AddStoreManager",
                avp::sample::toy::store::Action::"ListPackAssociates",
                avp::sample::toy::store::Action::"ListStoreManagers"
    ],
    resource in avp::sample::toy::store::Store::"toy store 1"
);

To learn more about the policy design of this application, see the readme file of the application.

Use cases – Design and implementation

In this section, we discuss high level design, challenges with the barebones integration, and how you can use the preceding techniques to reduce latency and costs.

Listing orders

Figure 4: Architecture for listing orders

Figure 4: Architecture for listing orders

As shown in Figure 4, the process to list orders is:

  1. The user accesses the application hosted in AWS Amplify.
  2. The user then authenticates through Amazon Cognito and obtains an identity token.
  3. The application uses Amplify to load the order page. The console calls the API ListOrders to load the order.
  4. The API is hosted in API Gateway and protected by a Lambda authorizer function.
  5. The Lambda function collects entity information from an in-memory data store to formulate the isAuthorized request.
  6. Then the Lambda function invokes Verified Permissions to authorize the request. The function checks against Verified Permissions for each order in the data store for the ListOrder call. If Verified Permissions returns deny, the order is not provided to the user. If Verified Permissions returns allow, the request is moved forward.

Challenge

Figure 5 shows that the application called IsAuthorized multiple times, sequentially. Multiple sequential calls cause the page to be slow to load and increase infrastructure costs.

Figure 5: Graph showing repeated calls to IsAuthorized

Figure 5: Graph showing repeated calls to IsAuthorized

Reduce latency using batch authorization

If you transition to using batch authorization, the application can receive 30 authorization decisions with a single API call to Verified Permissions. As you can see in Figure 6, the time to authorize has reduced from close to 800 ms to 79 ms, delivering a better overall user experience.

Figure 6: Reduced latency by using batch authorization

Figure 6: Reduced latency by using batch authorization

Order actions

Figure 7: Order actions architecture

Figure 7: Order actions architecture

As shown in Figure 7, the process to get authorized actions for an order is:

  1. The user goes to the application landing page on Amplify.
  2. The application calls the Order actions API at API Gateway
  3. The application sends a request to initiate order actions to display only authorized actions to the user.
  4. The Lambda function collects entity information from an in-memory data store to formulate the isAuthorized request.
  5. The Lambda function then checks with Verified Permissions for each order action. If Verified Permissions returns deny, the action is dropped. If Verified Permissions returns allow, the request is moved forward and the action is added to a list of order actions to be sent in a follow-up request to Verified Permissions to provide the actions in the user’s UI.

Challenge

As you saw with listing orders, Figure 8 shows how the application is still calling IsAuthorized multiple times, sequentially. This means the page remains slow to load and has increased impacts on infrastructure costs.

Figure 8: Graph showing repeated calls to IsAuthorized

Figure 8: Graph showing repeated calls to IsAuthorized

Reduce latency using batch authorization

If you add another layer by transitioning to using batch authorization once again, the application can receive all decisions with a single API call to Verified Permissions. As you can see from Figure 9, the time to authorize has reduced from close to 500 ms to 150 ms, delivering an improved user experience.

Figure 9: Graph showing results of layering batch authorization

Figure 9: Graph showing results of layering batch authorization

Viewing an order

Figure 10: Order viewing architecture

Figure 10: Order viewing architecture

The process to view an order, shown in Figure 10, is:

  1. The user accesses the application hosted in Amplify.
  2. The user authenticates through Amazon Cognito and obtains an identity token.
  3. The application calls three APIs hosted at API Gateway.
  4. The API’s: Get order details, Get label, and Get receipt are targeted sequentially to load the UI for the user in the application.
  5. A Lambda authorizer protects each of the above-mentioned APIs and is launched for each invoke.
  6. The Lambda function collects entity information from an in-memory data store to formulate the isAuthorized request.
  7. For each API, the following steps are repeated. The Lambda authorizer is invoked three times during page load.
    1. The Lambda function invokes Verified Permissions to authorize the request. If Verified Permissions returns deny, the request is rejected and an HTTP unauthorized response (403) is sent back. If Verified Permissions returns allow, the request is moved forward. 
    2. If the request is allowed, API Gateway calls the Lambda Order Management function to process the request. This is the primary Lambda function supporting the application and typically contains the core business logic of the application.

Challenge

In using the standard authorization pattern for this use case, the application calls Verified Permissions three times. This is because the user action to view an order requires compound authorization because each API call made by the console is authorized. While this enforces least privilege, it impacts the page load and reload latency of the application.

Reduce latency using batch authorization and decision caching

You can use batch authorization and decision caching to reduce latency. In the sample application, the cache is maintained by API Gateway. As shown in Figure 11, applying these techniques to the console application results in only one call to Verified Permissions, reducing latency.

Figure 11: Batch authorization with decision caching architecture

Figure 11: Batch authorization with decision caching architecture

The decision caching processshown in Figure 11, is:

  1. The user accesses the application hosted in Amplify.
  2. The user then authenticates through Amazon Cognito and obtains an identity token.
  3. The application then calls three APIs hosted at API Gateway
  4. When the Lambda function for the Get order details API is invoked, it uses the Lambda Authorizer to call batch authorization to get authorization decisions for the requested action, Get order details, and related actions, Get label and Get receipt.
  5. A Lambda authorizer protects each of the above-mentioned APIs but because of batch authorization, is invoked only once.
  6. The Lambda function collects entity information from an in-memory data store to formulate the isAuthorized request.
  7. The Lambda function invokes Verified Permissions to authorize the request. If Verified Permissions returns deny, the request is rejected and an HTTP unauthorized response (403) is sent back. If Verified Permissions returns allow, the request is moved forward.
    1. API Gateway caches the authorization decision for all actions (the requested action and related actions).
    2. If the request is allowed by the Lambda authorizer function, API Gateway calls the order management Lambda function to process the request. This is the primary Lambda function supporting the application and typically contains the core business logic of the application.
    3. When subsequent APIs are called, the API Gateway uses the cached authorization decisions and doesn’t use the Lambda authorization function.

Caching considerations

You’ve seen how you can use caching to implement fine-grained authorization at scale in your applications. This technique works well when your application has high cache hit rates, where authorization results are frequently loaded from the cache. Applications where the users initiate the same action multiple times or have a predictable sequence of actions will observe high cache hit rates. Another consideration is that employing caching can delay the time between policy updates and policy enforcement. We don’t recommend using caching for authorization decisions if your application requires policies to take effect quickly or your policies are time dependent (for example, a policy that gives access between 10:00 AM and 2:00 PM).

Conclusion

In this post, we showed you how to implement fine grained permissions in application at scale using Verified Permissions. We covered how you can use batch authorization and decision caching to improve performance and ensure Verified Permissions remains a cost-effective solution for large-scale applications. We applied these techniques to a demo application, avp-toy-store-sample, that is available to you for hands-on testing. For more information about Verified Permissions, see the Amazon Verified Permissions product details and Resources.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Abhishek Panday

Abhishek Panday

Abhishek is a product manager in the Amazon Verified Permissions team. He’s been working with AWS for more than two years and has been at Amazon for over five years. Abhishek enjoys working with customers to understand their challenges and building products to solve those challenges. Abhishek currently lives in Seattle and enjoys playing soccer, hiking, and cooking Indian cuisines.

Jeremy Wave

Jeremy Ware

Jeremy is a Security Specialist Solutions Architect focused on Identity and Access Management. Jeremy and his team enable AWS customers to implement sophisticated, scalable, and secure architectures to solve business challenges. Jeremy has spent many years working to improve the security maturity at numerous global enterprises. In his free time, Jeremy loves to enjoy the outdoors with his family.

Applying Spot-to-Spot consolidation best practices with Karpenter

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/applying-spot-to-spot-consolidation-best-practices-with-karpenter/

This post is written by Robert Northard – AWS Container Specialist Solutions Architect, and Carlos Manzanedo Rueda – AWS WW SA Leader for Efficient Compute

Karpenter is an open source node lifecycle management project built for Kubernetes. In this post, you will learn how to use the new Spot-to-Spot consolidation functionality released in Karpenter v0.34.0, which helps further optimize your cluster. Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances are spare Amazon EC2 capacity available for up to 90% off compared to On-Demand prices. One difference between On-Demand and Spot is that Spot Instances can be interrupted by Amazon EC2 when the capacity is needed back. Karpenter’s built-in support for Spot Instances allows users to seamlessly implement Spot best practices and helps users optimize the cost of stateless, fault tolerant workloads. For example, when Karpenter observes a Spot interruption, it automatically starts a new node in response.

Karpenter provisions nodes in response to unschedulable pods based on aggregated CPU, memory, volume requests, and other scheduling constraints. Over time, Karpenter has added functionality to simplify instance lifecycle configuration, providing a termination controller, instance expiration, and drift detection. Karpenter also helps optimize Kubernetes clusters by selecting the optimal instances while still respecting Kubernetes pod-to-node placement nuances, such as nodeSelector, affinity and anti-affinity, taints and tolerations, and topology spread constraints.

The Kubernetes scheduler assigns pods to nodes based on their scheduling constraints. Over time, as workloads are scaled out and scaled in or as new instances join and leave, the cluster placement and instance load might end up not being optimal. In many cases, it results in unnecessary extra costs. Karpenter has a consolidation feature that improves cluster placement by identifying and taking action in situations such as:

  1. when a node is empty
  2. when a node can be removed as the pods that are running on it can be rescheduled into other existing nodes
  3. when the number of pods in a node has gone down and the node can now be replaced with a lower-priced and rightsized variant (which is shown in the following figure)
Karpenter consolidation, replacing one 2xlarge Amazon EC2 Instance with an xlarge Amazon EC2 Instance.

Karpenter consolidation, replacing one 2xlarge Amazon EC2 Instance with an xlarge Amazon EC2 Instance.

Karpenter versions prior to v0.34.0 only supported consolidation for Amazon EC2 On-Demand Instances. On-Demand consolidation allowed consolidating from On-Demand into Spot Instances and to lower-priced On-Demand Instances. However, once a pod was placed on a Spot Instance, Spot nodes were only removed when the nodes were empty. In v0.34.0, you can enable the feature gate to use Spot-to-Spot consolidation.

Solution overview

When launching Spot Instances, Karpenter uses the price-capacity-optimized allocation strategy when calling the Amazon EC2 instant Fleet API (shown in the following figure) and passes in a selection of compute instance types based on the Karpenter NodePool configuration. The Amazon EC2 Fleet API in instant mode is a synchronous API call that immediately returns a list of instances that launched and any instance that could not be launched. For any instances that could not be launched, Karpenter might request alternative capacity or remove any soft Kubernetes scheduling constraints for the workload.

Karpenter instance orchestration

Karpenter instance orchestration

Spot-to-Spot consolidation needed an approach that was different from On-Demand consolidation. For On-Demand consolidation, rightsizing and lowest price are the main metrics used. For Spot-to-Spot consolidation to take place, Karpenter requires a diversified instance configuration (see the example NodePool defined in the walkthrough) with at least 15 instances types. Without this constraint, there would be a risk of Karpenter selecting an instance that has lower availability and, therefore, higher frequency of interruption.

Prerequisites

The following prerequisites are required to complete the walkthrough:

  • Install an Amazon Elastic Kubernetes Service (Amazon EKS) cluster (version 1.29 or higher) with Karpenter (v0.34.0 or higher). The Karpenter Getting Started Guide provides steps for setting up an Amazon EKS cluster and adding Karpenter.
  • Enable replacement with Spot consolidation through the SpotToSpotConsolidation feature gate. This can be enabled during a helm install of the Karpenter chart by adding –-set settings.featureGates.spotToSpotConsolidation=true argument.
  • Install kubectl, the Kubernetes command line tool for communicating with the Kubernetes control plane API, and kubectl context configured with Cluster Operator and Cluster Developer permissions.

Walkthrough

The following walkthrough guides you through the steps for simulating Spot-to-Spot consolidation.

1. Create a Karpenter NodePool and EC2NodeClass

Create a Karpenter NodePool and EC2NodeClass. Replace the following with your own values. If you used the Karpenter Getting Started Guide to create your installation, then the value would be your cluster name.

  • Replace <karpenter-discovery-tag-value> with your subnet tag for Karpenter subnet and security group auto-discovery.
  • Replace <role-name> with the name of the AWS Identity and Access Management (IAM) role for node identity.
cat <<EOF > nodepool.yaml
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  template:
    metadata:
      labels:
        intent: apps
    spec:
      nodeClassRef:
        name: default
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c","m","r"]
        - key: karpenter.k8s.aws/instance-size
          operator: NotIn
          values: ["nano","micro","small","medium"]
        - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values: ["nitro"]
  limits:
    cpu: 100
    memory: 100Gi
  disruption:
    consolidationPolicy: WhenUnderutilized
---
apiVersion: karpenter.k8s.aws/v1beta1
kind: EC2NodeClass
metadata:
  name: default
spec:
  amiFamily: Bottlerocket
  subnetSelectorTerms:          
    - tags:
        karpenter.sh/discovery: "<karpenter-discovery-tag-value>"
  securityGroupSelectorTerms:
    - tags:
        karpenter.sh/discovery: "<karpenter-discovery-tag-value>"
  role: "<role-name>"
  tags:
    Name: karpenter.sh/nodepool/default
    IntentLabel: "apps"
EOF

kubectl apply -f nodepool.yaml

The NodePool definition demonstrates a flexible configuration with instances from the C, M, or R EC2 instance families. The configuration is restricted to use smaller instance sizes but is still diversified as much as possible. For example, this might be needed in scenarios where you deploy observability DaemonSets. If your workload has specific requirements, then see the supported well-known labels in the Karpenter documentation.

2. Deploy a sample workload

Deploy a sample workload by running the following command. This command creates a Deployment with five pod replicas using the pause container image:

cat <<EOF > inflate.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: inflate
spec:
  replicas: 5
  selector:
    matchLabels:
      app: inflate
  template:
    metadata:
      labels:
        app: inflate
    spec:
      nodeSelector:
        intent: apps
      containers:
        - name: inflate
          image: public.ecr.aws/eks-distro/kubernetes/pause:3.2
          resources:
            requests:
              cpu: 1
              memory: 1.5Gi
EOF
kubectl apply -f inflate.yaml

Next, check the Kubernetes nodes by running a kubectl get nodes CLI command. The capacity pool (instance type and Availability Zone) selected depends on any Kubernetes scheduling constraints and spare capacity size. Therefore, it might differ from this example in the walkthrough. You can see Karpenter launched a new node of instance type c6g.2xlarge, an AWS Graviton2-based instance, in the eu-west-1c Region:

$ kubectl get nodes -L karpenter.sh/nodepool -L node.kubernetes.io/instance-type -L topology.kubernetes.io/zone -L karpenter.sh/capacity-type

NAME                                     STATUS   ROLES    AGE   VERSION               NODEPOOL   INSTANCE-TYPE   ZONE         CAPACITY-TYPE
ip-10-0-12-17.eu-west-1.compute.internal Ready    <none>   80s   v1.29.0-eks-a5ec690   default    c6g.2xlarge     eu-west-1c   spot

3. Scale in a sample workload to observe consolidation

To invoke a Karpenter consolidation event scale, inflate the deployment to 1. Run the following command:

kubectl scale --replicas=1 deployment/inflate 

Tail the Karpenter logs by running the following command. If you installed Karpenter in a different Kubernetes namespace, then replace the name for the -n argument in the command:

kubectl -n karpenter logs -l app.kubernetes.io/name=karpenter --all-containers=true -f --tail=20

After a few seconds, you should see the following disruption via consolidation message in the Karpenter logs. The message indicates the c6g.2xlarge Spot node has been targeted for replacement and Karpenter has passed the following 15 instance types—m6gd.xlarge, m5dn.large, c7a.xlarge, r6g.large, r6a.xlarge and 10 other(s)—to the Amazon EC2 Fleet API:

{"level":"INFO","time":"2024-02-19T12:09:50.299Z","logger":"controller.disruption","message":"disrupting via consolidation replace, terminating 1 candidates ip-10-0-12-181.eu-west-1.compute.internal/c6g.2xlarge/spot and replacing with spot node from types m6gd.xlarge, m5dn.large, c7a.xlarge, r6g.large, r6a.xlarge and 10 other(s)","commit":"17d6c05","command-id":"60f27cb5-98fa-40fb-8231-05b31fd41892"}

Check the Kubernetes nodes by running the following kubectl get nodes CLI command. You can see that Karpenter launched a new node of instance type c6g.large:

$ kubectl get nodes -L karpenter.sh/nodepool -L node.kubernetes.io/instance-type -L topology.kubernetes.io/zone -L karpenter.sh/capacity-type

NAME                                      STATUS   ROLES    AGE   VERSION               NODEPOOL   INSTANCE-TYPE ZONE       CAPACITY-TYPE
ip-10-0-12-156.eu-west-1.compute.internal           Ready    <none>   2m1s   v1.29.0-eks-a5ec690   default    c6g.large       eu-west-1c   spot

Use kubectl get nodeclaims to list all objects of type NodeClaim and then describe the NodeClaim Kubernetes resource using kubectl get nodeclaim/<claim-name> -o yaml. In the NodeClaim .spec.requirements, you can also see the 15 instance types passed to the Amazon EC2 Fleet API:

apiVersion: karpenter.sh/v1beta1
kind: NodeClaim
...
spec:
  nodeClassRef:
    name: default
  requirements:
  ...
  - key: node.kubernetes.io/instance-type
    operator: In
    values:
    - c5.large
    - c5ad.large
    - c6g.large
    - c6gn.large
    - c6i.large
    - c6id.large
    - c7a.large
    - c7g.large
    - c7gd.large
    - m6a.large
    - m6g.large
    - m6gd.large
    - m7g.large
    - m7i-flex.large
    - r6g.large
...

What would happen if a Spot node could not be consolidated?

If a Spot node cannot be consolidated because there are not 15 instance types in the compute selection, then the following message will appear in the events for the NodeClaim object. You might get this event if you overly constrained your instance type selection:

Normal  Unconsolidatable   31s   karpenter  SpotToSpotConsolidation requires 15 cheaper instance type options than the current candidate to consolidate, got 1

Spot best practices with Karpenter

The following are some best practices to consider when using Spot Instances with Karpenter.

  • Avoid overly constraining instance type selection: Karpenter selects Spot Instances using the price-capacity-optimized allocation strategy, which balances the price and availability of AWS spare capacity. Although a minimum of 15 instances are needed, you should avoid constraining instance types as much as possible. By not constraining instance types, there is a higher chance of acquiring Spot capacity at large scales with a lower frequency of Spot Instance interruptions at a lower cost.
  • Gracefully handle Spot interruptions and consolidation actions: Karpenter natively handles Spot interruption notifications by consuming events from an Amazon Simple Queue Service (Amazon SQS) queue, which is populated with Spot interruption notifications through Amazon EventBridge. As soon as Karpenter receives a Spot interruption notification, it gracefully drains the interrupted node of any running pods while also provisioning a new node for which those pods can schedule. With Spot Instances, this process needs to complete within 2 minutes. For a pod with a termination period longer than 2 minutes, the old node will be interrupted prior to those pods being rescheduled. To test a replacement node, AWS Fault Injection Service (FIS) can be used to simulate Spot interruptions.
  • Carefully configure resource requests and limits for workloads: Rightsizing and optimizing your cluster is a shared responsibility. Karpenter effectively optimizes and scales infrastructure, but the end result depends on how well you have rightsized your pod requests and any other Kubernetes scheduling constraints. Karpenter does not consider limits or resource utilization. For most workloads with non-compressible resources, such as memory, it is generally recommended to set requests==limits because if a workload tries to burst beyond the available memory of the host, an out-of-memory (OOM) error occurs. Karpenter consolidation can increase the probability of this as it proactively tries to reduce total allocatable resources for a Kubernetes cluster. For help with rightsizing your Kubernetes pods, consider exploring Kubecost, Vertical Pod Autoscaler configured in recommendation mode, or an open source tool such as Goldilocks.
  • Configure metrics for Karpenter: Karpenter emits metrics in the Prometheus format, so consider using Amazon Managed Service for Prometheus to track interruptions caused by Karpenter Drift, consolidation, Spot interruptions, or other Amazon EC2 maintenance events. These metrics can be used to confirm that interruptions are not having a significant impact on your service’s availability and monitor NodePool usage and pod lifecycles. The Karpenter Getting Started Guide contains an example Grafana dashboard configuration.

You can learn more about other application best practices in the Reliability section of the Amazon EKS Best Practices Guide.

Cleanup

To avoid incurring future charges, delete any resources you created as part of this walkthrough. If you followed the Karpenter Getting Started Guide to set up a cluster and add Karpenter, follow the clean-up instructions in the Karpenter documentation to delete the cluster. Alternatively, if you already had a cluster with Karpenter, delete the resources created as part of this walkthrough:

kubectl delete -f inflate.yaml
kubectl delete -f nodepool.yaml

Conclusion

In this post, you learned how Karpenter can actively replace a Spot node with another more cost-efficient Spot node. Karpenter can consolidate Spot nodes that have the right balance between lower price and low-frequency interruptions when there are at least 15 selectable instances to balance price and availability.

To get started, check out the Karpenter documentation as well as Karpenter Blueprints, which is a repository including common workload scenarios following the best practices.

You can share your feedback on this feature by a raising a GitHub Issue.

Migrate your Windows PKI from Microsoft Active Directory Certificate Services to AWS Private CA Connector for Active Directory

Post Syndicated from Axel Larsson original https://aws.amazon.com/blogs/security/migrate-your-windows-pki-from-microsoft-active-directory-certificate-services-to-aws-private-ca-connector-for-active-directory/

When you migrate your Windows environment to Amazon Web Services (AWS), you might need to address certificate management for computers and users in your Active Directory domain. Today, Windows administrators commonly use Active Directory Certificate Services (AD CS) to support this task. In this post, we will show you how to migrate AD CS to AWS Private Certificate Authority by using the AWS Private CA Connector for Active Directory.

AWS Private CA provides a highly available private certificate authority (CA) service without the upfront investment and ongoing maintenance costs of operating your own private CA. Using the APIs that AWS Private CA provides, you can create and deploy private certificates programmatically. You also have the flexibility to create private certificates for applications that require custom certificate lifetimes or resource names. With AWS Private CA, you can create your own CA hierarchy and issue certificates for authenticating internal users, computers, applications, services, servers, and devices and for signing computer code.

Use cases for certificate services that integrate with Active Directory

AD CS is commonly used in enterprise environments because it integrates certificate management with Active Directory authentication, authorization, and policy management. A common use case for AD CS is certificate auto-enrollment. Using AD Group Policies, you can configure certificates to automatically be created for users as they log in to the domain, or you can configure computer certificates, which are associated with each workstation or server that joins the domain. You can then use these certificates for authentication or digital signature purposes. A common use case is for authentication of devices to protected networks, such as wired networks that require 802.1x authentication or wireless networks that are protected by WPA2/3 with EAP-TLS authentication. Auto-enrolled computer and user certificates are also commonly used to authenticate VPN connections.

In addition to certificate auto-enrollment, AD CS also integrates with Active Directory to publish certificate information directly to the user and computer objects in Active Directory. In this way, you can integrate the lifecycle management of the certificates directly into your existing processes for managing the lifecycle of AD users and computers that are joined to the domain.

Options to deploy certificate services that integrate with Active Directory on AWS

To migrate your Windows environment to the cloud, you probably want to retain the capabilities of a CA that integrates with Active Directory. Although you can migrate AD CS directly to AWS and run it on an Amazon Elastic Compute Cloud (Amazon EC2) instance running Windows, we will show you how to use AWS Private CA with the Connector for AD to provide an Active Directory integrated CA that offers the benefits of AD CS without the need to manage AD CS or hardware security modules (HSMs).

Why migrate your on-premises CA to AWS Private CA?

Migrating AD CS to AWS Private CA has several benefits. With AWS Private CA, you get simplified certificate management, eliminating the need for an on-premises CA infrastructure and reducing operational complexity. AWS Private CA provides a managed service, reducing the operational burden and providing high availability and scalability. Additionally, it integrates with other AWS services, so it’s simpler to manage and deploy certificates across your infrastructure. The centralized management, enhanced security features, and simplified workflows in AWS Private CA can streamline certificate issuance and renewal processes, enabling you to more efficiently achieve your security goals.

AWS manages the underlying infrastructure, which can help reduce costs, and automation features help prevent disruptions that expired certificates could cause. AWS Private CA operates as a Regional service with an SLA of 99.9% availability. For environments that require the highest level of availability, you can deploy CAs in multiple AWS Regions by following the guidance on redundancy and disaster recovery in the documentation.

AWS Private CA Connector for AD extends the certificate management of Private CA to AD environments. With the Connector for AD, you can use Private CA to issue certificates for AD users and computers joined to your domain. This includes integration with Windows Group Policy for certificate autoenrollment.

How do I migrate to the Connector for AD?

Transitioning from an existing AD CS server to the Connector for AD involves several steps.

Assessment and planning

Before you begin, identify the use cases for your existing AD CS server and how certificates are issued. In this post, we focus on certificates that are auto-enrolled by using a Group Policy, but you might have other use cases where you must manually enroll certificates by using the Web Enrollment server or APIs. These might include use cases for signing software packages or web server certificates for intranet applications. Start by creating a certificate inventory from your existing AD CS server.

To create a certificate inventory from your existing AD CS server

  1. In the Microsoft CA console, select Issued Certificates.
  2. From the Action menu, select Export List.
     
    Figure 1: Export a list of existing certificates

    Figure 1: Export a list of existing certificates

This produces a CSV file of the certificates that the server issued. To determine which certificates were created as part of an auto-enrollment policy and to identify special cases that require manual attention, sort this file by Certificate Template.

Set up AWS Private CA and the Connector for AD

To complete the initial setup of the Connector for AD, see Getting started with AWS Private CA Connector for Active Directory. When you complete the setup, you can start transitioning enrollment to the new CA.

Configure trust for the new CA

Depending on where you put the new private CA in your organization’s public key infrastructure (PKI) hierarchy, you might want to make sure that its certificate is imported into all of the client trust stores before you issue new certificates using the CA. For Windows devices, creation of the Connector for AD imports the CA certificate into Active Directory, and automatically distributes it to the trust stores of domain-joined computers.

For non-Windows devices, you need to evaluate other use cases for issued certificates on the network and follow vendor instructions for updating trust stores. For example, if you use client certificates for wired 802.1x and Wi-Fi Protected Access (WPA) enterprise authentication, you need to import the new root CA certificate into the trust stores of the RADIUS servers that you use for authentication.

Export the CA certificate

The next step is to export the certificate by using the AWS Management Console.

To export the certificate

  1. Open the AWS Private CA console.
  2. Navigate to your private CA.
  3. Choose the CA certificate tab.
  4. Select Export certificate body to a file.

    For import into an Active Directory Group Policy Object (GPO), name the exported file with a .cer file extension.

     

    Figure 2: Export the CA certificate

    Figure 2: Export the CA certificate

Transition certificate enrollment to the new CA

After you configure AWS Private CA and the Connector for AD and update your trust stores as necessary, you can begin to transition certificate enrollment to the new CA. In Active Directory domains, certificates are typically created automatically by using an auto-enrollment Group Policy. To migrate enrollment to your new CA, you need to configure certificate templates with the appropriate properties to match your requirements, assign permissions to the templates, and configure the Group Policy to point the enrollment client on Windows devices to the new CA.

Configure certificate templates

Certificate templates define the enrollment policy on a CA. An Active Directory CA only issues certificates that conform to the templates that you have configured. Using the certificate inventory that you collected from your existing AD CS server, you should have a list of certificate templates that are in active use in your environment and that you need to replicate in the Connector for AD.

Start by noting the properties of these certificate templates on your existing AD CS server.

To note the properties of the certificate templates

  1. Open the Certificate Authority console on your AD CS server.
  2. Navigate to the Certificate Templates folder.
  3. From the Action menu, select Manage. This opens the Certificate Templates console, which shows a list of the certificate templates available in Active Directory.
     
    Figure 3: Identify certificate templates

    Figure 3: Identify certificate templates

  4. For each certificate that is in active use, open it and take note of its settings, particularly the following:
    • Template name, validity period, and renewal period from the General tab.
    • Certificate recipient compatibility from the Compatibility tab.
    • Certificate purpose and associated checkboxes in addition to whether a private key is allowed to be exported from the Request Handling tab.
    • Cryptography settings from the Cryptography tab.
    • The extensions configured from the Extensions tab.
    • Settings for certificate subject and subject alternate name from the Subject Name tab.
    • Review the Security tab for the list of Active Directory users and groups that have Enroll or AutoEnroll permissions. The other permission settings, which control which AD principals have the ability to modify or view the template itself, don’t apply to AWS Private CA because IAM authorization is used for these purposes.
       
      Figure 4: Certificate template properties

      Figure 4: Certificate template properties

After you gather the configuration details for the certificate templates that are in active use, you need to configure equivalent templates within the Connector for AD.

To configure templates in the Connector for AD

  1. Open the AWS Private CA console.
  2. Navigate to Private CA Connector for AD.
  3. Select your connector.
  4. In the Templates section, choose Create template.
     
    Figure 5: Certificate template configuration in the Connector for AD

    Figure 5: Certificate template configuration in the Connector for AD

  5. You can then begin configuring your certificate template by using the settings that you obtained from your existing AD CS server. For a complete description of the settings that are available in the certificate template, see Creating a connector template.
     
    Figure 6: Certificate template settings

    Figure 6: Certificate template settings

  6. Assign permissions to the template.

    You must manually enter the Active Directory Security Identifier (SID) of the user or group that you are assigning the Enroll or Auto-enroll permission to. For instructions on how to use PowerShell to obtain the SID of an Active Directory object, see Managing AD groups and permissions for templates.

    We recommend that you initially assign your certificate templates to a small test group that contains a set of Active Directory computers or users that will be used to test the new CA. When you are confident that the new CA issues certificates correctly, you can modify the permissions to include the full set of Active Directory user and computer groups that were assigned to the template on your original AD CS server.

Configure Group Policy for automatic certificate enrollment

With the Connector for AD configured with the required certificate templates, you are ready to configure the AD Group Policy to enable automatic enrollment of user and computer certificates. We suggest that you start with a test organizational unit (OU) in Active Directory, where you can put user and computer objects to make sure that enrollment is working properly. The existing AD CS server and the Connector for AD can continue to coexist until you are ready to replace the certificates.

In this example, you configure a new Group Policy object that is linked to an OU called Test OU, where you will place computer objects for testing.

To configure a new Group Policy object

  1. Within the Group Policy object, locate the settings for controlling enrollment under Computer Configuration  > Policies > Windows Settings > Security Settings > Public Key Policies.
     
    Figure 7: Active Directory Group Policy Editor

    Figure 7: Active Directory Group Policy Editor

  2. Configure the Certificate Services Client – Certificate Enrollment Policy to point clients at the URL of the Connector for AD:
    1. Set the Configuration Model to Enabled.
    2. Add a new item to the Certificate enrollment policy list.
       
      Figure 8: Certificate Services Client Group Policy settings

      Figure 8: Certificate Services Client Group Policy settings

  3. Enter the URL of your connector and leave the Authentication mode set to Windows Integrated. Then choose Validate.

    Note: You can find the URL of your connector in the AWS Private CA Connector for AD console under Certificate enrollment policy server endpoint.

    Figure 9: Connector details

    Figure 9: Connector details

  4. After you save your configuration, remove the Active Directory Enrollment Policy from the list so that the Group Policy only references the Connector for AD. A completed configuration will look similar to the following:
     
    Figure 10: Certificate services client settings with Active Directory enrollment policy removed

    Figure 10: Certificate services client settings with Active Directory enrollment policy removed

  5. From within the Group Policy editor, open the Certificate Services Client – Auto-enrollment policy to configure auto-enrollment of computer certificates. Set Configuration Model to Enabled, and select the following:
    • Renew expired certificates, update pending certificates, and remove revoked certificates
    • Update certificates that use certificate templates
       
      Figure 11: Certificate Services client auto-enrollment policy settings

      Figure 11: Certificate Services client auto-enrollment policy settings

After you configure the Group Policy, computers in OUs that the Group Policy is linked to will start automatically enrolling certificates by using AWS Private CA, subject to the permissions defined on the certificate templates. To review the progress of certificate enrollment, use private CA audit reports.

When you complete testing and gain confidence in your certificate roll-out, extend the scope of the GPO and Active Directory permissions on the certificate templates to cover additional users and computers.

Revocation and decommissioning

You can continue to review the Private CA audit reports to confirm progress with auto-enrollment of certificates from the new CA. If you have computers that infrequently connect to the network, this can take some time. As part of this process, address your use cases that aren’t covered by auto-enrollment, which you identified from your initial certificate inventory. These might include web server certificates for internal applications or code-signing certificates for distributing software packages. You can issue replacement certificates for these use cases by using the AWS Private CA APIs or CLI without depending on the Active Directory integration. For more information, see Issuing private end-entity certificates.

After the required certificates have been enrolled and you have confirmed that the services that depend upon those certificates are functioning correctly, it’s time to revoke issued certificates and decommission your existing AD CS server. Microsoft provides detailed documentation for properly revoking certificates and decommissioning an Enterprise CA, including clean-up of related AD objects.

Conclusion

In this post, we covered some use cases for Active Directory integrated certificate management in Windows environments and introduced the new AWS Private CA Connector for Active Directory. AWS Private CA and the Connector for AD can help you reduce operational overhead, enabling you to simplify the process of provisioning certificates while maintaining the Active Directory integration that you are accustomed to in a Microsoft AD CS environment. You learned how to evaluate your existing Microsoft CA and migrate to AWS Private CA with the Connector for AD, with a specific focus on auto-enrollment of certificates, which is commonly used in enterprise environments for device and end-user authentication.

To learn more about the services described in the post, see the documentation for Connector for AD, AWS Private CA , CA best practices and AWS Directory Services. You can get started creating CAs in AWS Private CA by using the console.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS Certificate Manager re:Post or contact AWS Support.

Author

Axel Larsson

Axel is a Principal Solutions Architect at AWS based in the greater New York City area. He supports FinTech customers and is passionate about helping them to establish a secure and compliant foundation on AWS to accelerate their business outcomes. Outside of work, he is an avid tinkerer and enjoys experimenting with home automation.

Jean-Pierre Roux

Jean-Pierre Roux

Jean-Pierre is a Senior Security Consultant who has earned recognition as an ACM subject matter expert. With a specialized focus on the financial services industry, JP helps clients globally to securely use AWS services while aligning with regulatory standards. Outside of work, he enjoys activities such as surfing and gaming, and quality time with family and friends.

De’Shedric Boler

De’Shedric Boler

De’Shedric is a Senior Solutions Architect at AWS. He is part of the account team that supports enterprise customers in their cloud transformation journeys. Passionate about technology, he enjoys helping customers use technology to solve their business challenges.

Bubonke Matandela

Bubonke Matandela

Bubonke is a Professional Services Consultant at AWS based in South Africa, with an interest in security, risk, and governance to assist customers with their AWS Security journeys in the cloud. Outside of work, he enjoys spending time in the kitchen creating hearty dishes.

Unlock insights on Amazon RDS for MySQL data with zero-ETL integration to Amazon Redshift

Post Syndicated from Milind Oke original https://aws.amazon.com/blogs/big-data/unlock-insights-on-amazon-rds-for-mysql-data-with-zero-etl-integration-to-amazon-redshift/

Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift was announced in preview at AWS re:Invent 2023 for Amazon RDS for MySQL version 8.0.28 or higher. In this post, we provide step-by-step guidance on how to get started with near real-time operational analytics using this feature. This post is a continuation of the zero-ETL series that started with Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift.

Challenges

Customers across industries today are looking to use data to their competitive advantage and increase revenue and customer engagement by implementing near real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. There are two broad approaches to analyzing operational data for these use cases:

  • Analyze the data in-place in the operational database (such as read replicas, federated query, and analytics accelerators)
  • Move the data to a data store optimized for running use case-specific queries such as a data warehouse

The zero-ETL integration is focused on simplifying the latter approach.

The extract, transform, and load (ETL) process has been a common pattern for moving data from an operational database to an analytics data warehouse. ELT is where the extracted data is loaded as is into the target first and then transformed. ETL and ELT pipelines can be expensive to build and complex to manage. With multiple touchpoints, intermittent errors in ETL and ELT pipelines can lead to long delays, leaving data warehouse applications with stale or missing data, further leading to missed business opportunities.

Alternatively, solutions that analyze data in-place may work great for accelerating queries on a single database, but such solutions aren’t able to aggregate data from multiple operational databases for customers that need to run unified analytics.

Zero-ETL

Unlike the traditional systems where data is siloed in one database and the user has to make a trade-off between unified analysis and performance, data engineers can now replicate data from multiple RDS for MySQL databases into a single Redshift data warehouse to derive holistic insights across many applications or partitions. Updates in transactional databases are automatically and continuously propagated to Amazon Redshift so data engineers have the most recent information in near real time. There is no infrastructure to manage and the integration can automatically scale up and down based on the data volume.

At AWS, we have been making steady progress towards bringing our zero-ETL vision to life. The following sources are currently supported for zero-ETL integrations:

When you create a zero-ETL integration for Amazon Redshift, you continue to pay for underlying source database and target Redshift database usage. Refer to Zero-ETL integration costs (Preview) for further details.

With zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse. The data becomes available in Amazon Redshift within seconds, allowing you to use the analytics features of Amazon Redshift and capabilities like data sharing, workload optimization autonomics, concurrency scaling, machine learning, and many more. You can continue with your transaction processing on Amazon RDS or Amazon Aurora while simultaneously using Amazon Redshift for analytics workloads such as reporting and dashboards.

The following diagram illustrates this architecture.

AWS architecture diagram showcasing example zero-ETL architecture

Solution overview

Let’s consider TICKIT, a fictional website where users buy and sell tickets online for sporting events, shows, and concerts. The transactional data from this website is loaded into an Amazon RDS for MySQL 8.0.28 (or higher version) database. The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near real time using a zero-ETL integration.

The integration is set up between Amazon RDS for MySQL (source) and Amazon Redshift (destination). The transactional data from the source gets refreshed in near real time on the destination, which processes analytical queries.

You can use either the serverless option or an encrypted RA3 cluster for Amazon Redshift. For this post, we use a provisioned RDS database and a Redshift provisioned data warehouse.

The following diagram illustrates the high-level architecture.

High-level zero-ETL architecture for TICKIT data use case

The following are the steps needed to set up zero-ETL integration. These steps can be done automatically by the zero-ETL wizard, but you will require a restart if the wizard changes the setting for Amazon RDS or Amazon Redshift. You could do these steps manually, if not already configured, and perform the restarts at your convenience. For the complete getting started guides, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview) and Working with zero-ETL integrations.

  1. Configure the RDS for MySQL source with a custom DB parameter group.
  2. Configure the Redshift cluster to enable case-sensitive identifiers.
  3. Configure the required permissions.
  4. Create the zero-ETL integration.
  5. Create a database from the integration in Amazon Redshift.

Configure the RDS for MySQL source with a customized DB parameter group

To create an RDS for MySQL database, complete the following steps:

  1. On the Amazon RDS console, create a DB parameter group called zero-etl-custom-pg.

Zero-ETL integration works by using binary logs (binlogs) generated by MySQL database. To enable binlogs on Amazon RDS for MySQL, a specific set of parameters must be enabled.

  1. Set the following binlog cluster parameter settings:
    • binlog_format = ROW
    • binlog_row_image = FULL
    • binlog_checksum = NONE

In addition, make sure that the binlog_row_value_options parameter is not set to PARTIAL_JSON. By default, this parameter is not set.

  1. Choose Databases in the navigation pane, then choose Create database.
  2. For Engine Version, choose MySQL 8.0.28 (or higher).

Selected MySQL Community edition Engine version 8.0.36

  1. For Templates, select Production.
  2. For Availability and durability, select either Multi-AZ DB instance or Single DB instance (Multi-AZ DB clusters are not supported, as of this writing).
  3. For DB instance identifier, enter zero-etl-source-rms.

Selected Production template, Multi-AZ DB instance and DB instance identifier zero-etl-source-rms

  1. Under Instance configuration, select Memory optimized classes and choose the instance db.r6g.large, which should be sufficient for TICKIT use case.

Selected db.r6g.large for DB instance class under Instance configuration

  1. Under Additional configuration, for DB cluster parameter group, choose the parameter group you created earlier (zero-etl-custom-pg).

Selected DB parameter group zero-etl-custom-pg under Additional configuration

  1. Choose Create database.

In a couple of minutes, it should spin up an RDS for MySQL database as the source for zero-ETL integration.

RDS instance status showing as Available

Configure the Redshift destination

After you create your source DB cluster, you must create and configure a target data warehouse in Amazon Redshift. The data warehouse must meet the following requirements:

  • Using an RA3 node type (ra3.16xlarge, ra3.4xlarge, or ra3.xlplus) or Amazon Redshift Serverless
  • Encrypted (if using a provisioned cluster)

For our use case, create a Redshift cluster by completing the following steps:

  1. On the Amazon Redshift console, choose Configurations and then choose Workload management.
  2. In the parameter group section, choose Create.
  3. Create a new parameter group named zero-etl-rms.
  4. Choose Edit parameters and change the value of enable_case_sensitive_identifier to True.
  5. Choose Save.

You can also use the AWS Command Line Interface (AWS CLI) command update-workgroup for Redshift Serverless:

aws redshift-serverless update-workgroup --workgroup-name <your-workgroup-name> --config-parameters parameterKey=enable_case_sensitive_identifier,parameterValue=true

Cluster parameter group setup

  1. Choose Provisioned clusters dashboard.

At the top of you console window, you will see a Try new Amazon Redshift features in preview banner.

  1. Choose Create preview cluster.

Create preview cluster

  1. For Preview track, chose preview_2023.
  2. For Node type, choose one of the supported node types (for this post, we use ra3.xlplus).

Selected ra3.xlplus node type for preview cluster

  1. Under Additional configurations, expand Database configurations.
  2. For Parameter groups, choose zero-etl-rms.
  3. For Encryption, select Use AWS Key Management Service.

Database configuration showing parameter groups and encryption

  1. Choose Create cluster.

The cluster should become Available in a few minutes.

Cluster status showing as Available

  1. Navigate to the namespace zero-etl-target-rs-ns and choose the Resource policy tab.
  2. Choose Add authorized principals.
  3. Enter either the Amazon Resource Name (ARN) of the AWS user or role, or the AWS account ID (IAM principals) that are allowed to create integrations.

An account ID is stored as an ARN with root user.

Add authorized principals on the Clusters resource policy tab

  1. In the Authorized integration sources section, choose Add authorized integration source to add the ARN of the RDS for MySQL DB instance that’s the data source for the zero-ETL integration.

You can find this value by going to the Amazon RDS console and navigating to the Configuration tab of the zero-etl-source-rms DB instance.

Add authorized integration source to the Configuration tab of the zero-etl-source-rms DB instance

Your resource policy should resemble the following screenshot.

Completed resource policy setup

Configure required permissions

To create a zero-ETL integration, your user or role must have an attached identity-based policy with the appropriate AWS Identity and Access Management (IAM) permissions. An AWS account owner can configure required permissions for users or roles who may create zero-ETL integrations. The sample policy allows the associated principal to perform the following actions:

  • Create zero-ETL integrations for the source RDS for MySQL DB instance.
  • View and delete all zero-ETL integrations.
  • Create inbound integrations into the target data warehouse. This permission is not required if the same account owns the Redshift data warehouse and this account is an authorized principal for that data warehouse. Also note that Amazon Redshift has a different ARN format for provisioned and serverless clusters:
    • Provisioned – arn:aws:redshift:{region}:{account-id}:namespace:namespace-uuid
    • Serverless – arn:aws:redshift-serverless:{region}:{account-id}:namespace/namespace-uuid

Complete the following steps to configure the permissions:

  1. On the IAM console, choose Policies in the navigation pane.
  2. Choose Create policy.
  3. Create a new policy called rds-integrations using the following JSON (replace region and account-id with your actual values):
{
    "Version": "2012-10-17",
    "Statement": [{
        "Effect": "Allow",
        "Action": [
            "rds:CreateIntegration"
        ],
        "Resource": [
            "arn:aws:rds:{region}:{account-id}:db:source-instancename",
            "arn:aws:rds:{region}:{account-id}:integration:*"
        ]
    },
    {
        "Effect": "Allow",
        "Action": [
            "rds:DescribeIntegration"
        ],
        "Resource": ["*"]
    },
    {
        "Effect": "Allow",
        "Action": [
            "rds:DeleteIntegration"
        ],
        "Resource": [
            "arn:aws:rds:{region}:{account-id}:integration:*"
        ]
    },
    {
        "Effect": "Allow",
        "Action": [
            "redshift:CreateInboundIntegration"
        ],
        "Resource": [
            "arn:aws:redshift:{region}:{account-id}:cluster:namespace-uuid"
        ]
    }]
}
  1. Attach the policy you created to your IAM user or role permissions.

Create the zero-ETL integration

To create the zero-ETL integration, complete the following steps:

  1. On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane.
  2. Choose Create zero-ETL integration.

Create zero-ETL integration on the Amazon RDS console

  1. For Integration identifier, enter a name, for example zero-etl-demo.

Enter the Integration identifier

  1. For Source database, choose Browse RDS databases and choose the source cluster zero-etl-source-rms.
  2. Choose Next.

Browse RDS databases for zero-ETL source

  1. Under Target, for Amazon Redshift data warehouse, choose Browse Redshift data warehouses and choose the Redshift data warehouse (zero-etl-target-rs).
  2. Choose Next.

Browse Redshift data warehouses for zero-ETL integration

  1. Add tags and encryption, if applicable.
  2. Choose Next.
  3. Verify the integration name, source, target, and other settings.
  4. Choose Create zero-ETL integration.

Create zero-ETL integration step 4

You can choose the integration to view the details and monitor its progress. It took about 30 minutes for the status to change from Creating to Active.

Zero-ETL integration details

The time will vary depending on the size of your dataset in the source.

Create a database from the integration in Amazon Redshift

To create your database from the zero-ETL integration, complete the following steps:

  1. On the Amazon Redshift console, choose Clusters in the navigation pane.
  2. Open the zero-etl-target-rs cluster.
  3. Choose Query data to open the query editor v2.

Query data via the Query Editor v2

  1. Connect to the Redshift data warehouse by choosing Save.

Connect to the Redshift data warehouse

  1. Obtain the integration_id from the svv_integration system table:

select integration_id from svv_integration; -- copy this result, use in the next sql

Query for integration identifier

  1. Use the integration_id from the previous step to create a new database from the integration:

CREATE DATABASE zetl_source FROM INTEGRATION '<result from above>';

Create database from integration

The integration is now complete, and an entire snapshot of the source will reflect as is in the destination. Ongoing changes will be synced in near real time.

Analyze the near real time transactional data

Now we can run analytics on TICKIT’s operational data.

Populate the source TICKIT data

To populate the source data, complete the following steps:

  1. Copy the CSV input data files into a local directory. The following is an example command:

aws s3 cp 's3://redshift-blogs/zero-etl-integration/data/tickit' . --recursive

  1. Connect to your RDS for MySQL cluster and create a database or schema for the TICKIT data model, verify that the tables in that schema have a primary key, and initiate the load process:

mysql -h <rds_db_instance_endpoint> -u admin -p password --local-infile=1

Connect to your RDS for MySQL cluster and create a database or schema for the TICKIT data model

  1. Use the following CREATE TABLE commands.
  2. Load the data from local files using the LOAD DATA command.

The following is an example. Note that the input CSV file is broken into several files. This command must be run for every file if you would like to load all data. For demo purposes, a partial data load should work as well.

Create users table for demo

Analyze the source TICKIT data in the destination

On the Amazon Redshift console, open the query editor v2 using the database you created as part of the integration setup. Use the following code to validate the seed or CDC activity:

SELECT * FROM SYS_INTEGRATION_ACTIVITY ORDER BY last_commit_timestamp DESC;

Query to validate the seed or CDC activity

You can now apply your business logic for transformations directly on the data that has been replicated to the data warehouse. You can also use performance optimization techniques like creating a Redshift materialized view that joins the replicated tables and other local tables to improve query performance for your analytical queries.

Monitoring

You can query the following system views and tables in Amazon Redshift to get information about your zero-ETL integrations with Amazon Redshift:

To view the integration-related metrics published to Amazon CloudWatch, open the Amazon Redshift console. Choose Zero-ETL integrations in the navigation pane and choose the integration to display activity metrics.

Zero-ETL integration activity metrics

Available metrics on the Amazon Redshift console are integration metrics and table statistics, with table statistics providing details of each table replicated from Amazon RDS for MySQL to Amazon Redshift.

Integration metrics and table statistics

Integration metrics contain table replication success and failure counts and lag details.

Integration metrics showing table replication success and failure counts and lag details. Integration metrics showing table replication success and failure counts and lag details. Integration metrics showing table replication success and failure counts and lag details.

Manual resyncs

The zero-ETL integration will automatically initiate a resync if a table sync state shows as failed or resync required. But in case the auto resync fails, you can initiate a resync at table-level granularity:

ALTER DATABASE zetl_source INTEGRATION REFRESH TABLES tbl1, tbl2;

A table can enter a failed state for multiple reasons:

  • The primary key was removed from the table. In such cases, you need to re-add the primary key and perform the previously mentioned ALTER command.
  • An invalid value is encountered during replication or a new column is added to the table with an unsupported data type. In such cases, you need to remove the column with the unsupported data type and perform the previously mentioned ALTER command.
  • An internal error, in rare cases, can cause table failure. The ALTER command should fix it.

Clean up

When you delete a zero-ETL integration, your transactional data isn’t deleted from the source RDS or the target Redshift databases, but Amazon RDS doesn’t send any new changes to Amazon Redshift.

To delete a zero-ETL integration, complete the following steps:

  1. On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane.
  2. Select the zero-ETL integration that you want to delete and choose Delete.
  3. To confirm the deletion, choose Delete.

delete a zero-ETL integration

Conclusion

In this post, we showed you how to set up a zero-ETL integration from Amazon RDS for MySQL to Amazon Redshift. This minimizes the need to maintain complex data pipelines and enables near real time analytics on transactional and operational data.

To learn more about Amazon RDS zero-ETL integration with Amazon Redshift, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview).


 About the Authors

Milind Oke is a senior Redshift specialist solutions architect who has worked at Amazon Web Services for three years. He is an AWS-certified SA Associate, Security Specialty and Analytics Specialty certification holder, based out of Queens, New York.

Aditya Samant is a relational database industry veteran with over 2 decades of experience working with commercial and open-source databases. He currently works at Amazon Web Services as a Principal Database Specialist Solutions Architect. In his role, he spends time working with customers designing scalable, secure and robust cloud native architectures. Aditya works closely with the service teams and collaborates on designing and delivery of the new features for Amazon’s managed databases.

Invoke AWS Lambda functions from cross-account Amazon Kinesis Data Streams

Post Syndicated from Amar Surjit original https://aws.amazon.com/blogs/big-data/invoke-aws-lambda-functions-from-cross-account-amazon-kinesis-data-streams/

A multi-account architecture on AWS is essential for enhancing security, compliance, and resource management by isolating workloads, enabling granular cost allocation, and facilitating collaboration across distinct environments. It also mitigates risks, improves scalability, and allows for advanced networking configurations.

In a streaming architecture, you may have event producers, stream storage, and event consumers in a single account or spread across different accounts depending on your business and IT requirements. For example, your company may want to centralize its clickstream data or log data from multiple different producers across different accounts. Data consumers from marketing, product engineering, or analytics require access to the same streaming data across accounts, which requires the ability to deliver a multi-account streaming architecture.

To build a multi-account streaming architecture, you can use Amazon Kinesis Data Streams as the stream storage and AWS Lambda as the event consumer. Amazon Kinesis Data Streams enables real-time processing of streaming data at scale. When integrated with Lambda, it allows for serverless data processing, enabling you to analyze and react to data streams in real time without managing infrastructure. This integration supports various use cases, including real-time analytics, log processing, Internet of Things (IoT) data ingestion, and more, making it valuable for businesses requiring timely insights from their streaming data. In this post, we demonstrate how you can process data ingested into a stream in one account with a Lambda function in another account.

The recent launch of Kinesis Data Streams support for resource-based policies enables invoking a Lambda from another account. With a resource-based policy, you can specify AWS accounts, AWS Identity and Access Management (IAM) users, or IAM roles and the exact Kinesis Data Streams actions for which you want to grant access. After access is granted, you can configure a Lambda function in another account to start processing the data stream belonging to your account. This reduces cost and simplifies the data processing pipeline, because you no longer have to copy streaming data using Lambda functions in both accounts. Sharing access to your data streams or registered consumers does not incur additional charges to your account. Cross-account usage of Kinesis Data Streams resources will continue to be billed to the resource owners.

In this post, we use Kinesis Data Streams with enhanced fan-out feature, empowering consumers with dedicated read throughput tailored to their applications. By default, Kinesis Data Streams offers shared read throughput of 2 MB/sec per shard across consumers, but with enhanced fan-out, each consumer can enjoy dedicated throughput of 2 MB/sec per shard. This flexibility allows you to seamlessly adapt Kinesis Data Streams to your specific requirements, choosing between enhanced fan-out for dedicated throughput or shared throughput according to your needs.

Solution overview

For our solution, we deploy Kinesis Data Streams in Account 1 and Lambda as the consumer in Account 2 to receive data from the data stream. The following diagram illustrates the high-level architecture.

Amazon KDS-Lambda cross acct solution architecture

The setup requires the following key elements:

  • Kinesis data stream in Account 1 and Lambda function in Account 2
  • Kinesis Data Streams resource policies in Account 1, allowing a cross-account Lambda execution role to perform operations on the Kinesis data stream
  • A Lambda execution role in Account 2 and an enhanced fan-out consumer resource policy in Account 1, allowing the cross-account Lambda execution role to perform operations on the Kinesis data stream

For the setup, you use three AWS CloudFormation templates to create the key resources:

  • CloudFormation template 1 creates the following key resources in Account 1:
    • Kinesis data stream
    • Kinesis data stream enhanced fan-out consumer
  • CloudFormation template 2 creates the following key resources in Account 2:
    • Consumer Lambda function
    • Consumer Lambda function execution role
  • CloudFormation template 3 creates the following resource in Account 2:
    • Consumer Lambda function event source mapping

The solution supports single-Region deployment, and the CloudFormation templates must be deployed in the same Region across different AWS accounts. In this solution, we use Kinesis Data Streams enhanced fan-out, which is a best practice for deploying architectures requiring large throughput across multiple consumers. Complete the steps in the following sections to deploy this solution.

Prerequisites

You should have two AWS accounts and the required permissions to run a CloudFormation template to create the services mentioned in the solution architecture. You also need the AWS Command Line Interface (AWS CLI) installed, version 2.15 and above.

Launch CloudFormation template 1

Complete the following steps to launch the first CloudFormation template:

  1. Sign in to the AWS Management Console as Account 1 and select the appropriate AWS Region.
  2. Download and launch CloudFormation template 1 where you want to deploy your Kinesis data stream.
  3. For LambdaConsumerAccountId, enter your Lambda consumer account ID and click submit. The CloudFormation template deployment will take a few minutes to complete.
  4. When the stack is complete, on the AWS CloudFormation console, navigate to the stack Outputs tab and copy the values of following parameters:
    • KinesisStreamArn
    • KinesisStreamEFOConsumerArn
    • KMSKeyArn

You will need these values in later steps.

Launch CloudFormation template 2

Complete the following steps to launch the second CloudFormation template:

  1. Sign in to the console as Account 2 and select the appropriate Region.
  2. Download and launch CloudFormation template 2 where you want to host the Lambda consumer.
  3. Provide the following input parameters captured from the previous step:
    • KinesisStreamArn
    • KinesisStreamEFOConsumerArn
    • KMSKeyArn

The CloudFormation template creates the following key resources:

  • Lambda consumer
  • Lambda execution role

The Lambda function’s execution role is an IAM role that grants the function permission to access AWS services and resources. Here, you create a Lambda execution role that has the required Kinesis Data Streams and Lambda invocation permissions.

The CloudFormation template deployment will take a few minutes to complete.

  1. When the stack is complete, on the AWS CloudFormation console, navigate to the stack Outputs tab and copy the values of following parameters:
    • KinesisStreamCreateResourcePolicyCommand
    • KinesisStreamEFOConsumerCreateResourcePolicyCommand
  2. Run the following AWS CLI commands in Account 1 using AWS CloudShell. We recommend using CloudShell because it will have the latest version of the AWS CLI and avoid any kind of failures.
    • KinesisStreamCreateResourcePolicyCommand – This creates the resource policy in Account 1 for Kinesis Data Stream. The following is a sample resource policy:
      {
      "Version": "2012-10-17",
      "Statement": [
      {
      "Sid": "StreamEFOReadStatementID",
      "Effect": "Allow",
      "Principal": {
      "AWS": [
      "arn:aws:iam::<AWS Lambda - Consumer account id>:role/kds-cross-account-stream-consumer-lambda-execution-role"
      ]
      },
      "Action": [
      "kinesis:DescribeStreamSummary",
      "kinesis:ListShards",
      "kinesis:DescribeStream",
      "kinesis:GetRecords",
      "kinesis:GetShardIterator"
      ],
      "Resource": "arn:aws:kinesis:<region id>:<Account 1 - Amazon KDS account id>:stream/kds-cross-account-stream"
      }
      ]
      }

    • KinesisStreamEFOConsumerCreateResourcePolicyCommand – This creates the resource policy for the enhanced fan-out consumer for the Kinesis data stream in Account 1. The following is a sample resource policy:
      {
      "Version": "2012-10-17",
      "Statement": [
      {
      "Sid": "ConsumerEFOReadStatementID",
      "Effect": "Allow",
      "Principal": {
      "AWS": [
      " arn:aws:iam::<AWS Lambda - Consumer account id>:role/kds-cross-account-stream-consumer-lambda-execution-role"
      ]
      },
      "Action": [
      "kinesis:DescribeStreamConsumer",
      "kinesis:SubscribeToShard"
      ],
      "Resource": "arn:aws:kinesis:<region id>:<Account 1 - Amazon KDS account id>:stream/kds-cross-account-stream/consumer/kds-cross-account-stream-efo-consumer:1706616477"
      }
      ]
      }

You can also access this policy on the Kinesis Data Streams console, under Enhanced fan-out, Consumer name, and Consumer sharing resource-based policy.

Launch CloudFormation template 3

Now that you have created resource policies in Account 1 for the Kinesis data stream and its enhanced fan-out consumer, you can create Lambda event source mapping for the consumer Lambda function in Account 2. Complete the following steps:

  1. Sign in to the console as Account 2 and select the appropriate Region.
  2. Download and launch CloudFormation template 3 to update the stack you created using CloudFormation template 2.

The CloudFormation template creates the Lambda event source mapping.

Validate the solution

At this point, the deployment is complete. A Kinesis data stream is available to consume the messages and a Lambda function receives these messages in the destination account. To send sample messages to the data stream in Account 1, run the following AWS CLI command using CloudShell:

aws kinesis put-record --stream-name kds-cross-account-stream --data sampledatarecord --partition-key samplepartitionkey3 --region <region id>

The Lambda function in Account 2 is able to receive the messages, and you should be able to verify the same using Amazon CloudWatch logs:

  1. On the CloudWatch console, choose Log groups in the navigation pane.
  2. Locate the log group /aws/lambda/kds-cross-account-stream-efo-consumer.
  3. Choose Search log group to view the relevant log messages. The following is an example message:
    "Records": [
    {
    "kinesis": {
    "kinesisSchemaVersion": "1.0",
    "partitionKey": "samplepartitionkey3",
    "sequenceNumber": "49648798411111169765201534322676841348246990356337393698",
    "data": "sampledatarecord",
    "approximateArrivalTimestamp": 1706623274.658
    },

Clean up

It’s always a good practice to clean up all the resources you created as part of this post to avoid any additional cost.

To clean up your resources, delete the respective CloudFormation stacks from Accounts 1 and 2, and stop the producer from pushing events to the Kinesis data stream. This makes sure that you are not charged unnecessarily.

Summary

In this post, we demonstrated how to configure a cross-account Lambda integration with Kinesis Data Streams using AWS resource-based policies. This enables processing of data ingested into a stream within one AWS account through a Lambda function located in another account. To support customers who use a Kinesis data stream in their central account and have multiple consumers reading data from it, we have used the Kinesis Data Streams enhanced fan-out feature.

To get started, open the Kinesis Data Streams console or use the new API PutResourcePolicy to attach a resource policy to your data stream or consumer.


About the authors

Pratik Patel is Sr. Technical Account Manager and streaming analytics specialist. He works with AWS customers and provides ongoing support and technical guidance to help plan and build solutions using best practices and proactively keep customers’ AWS environments operationally healthy.

Amar is a Senior Solutions Architect at Amazon AWS in the UK. He works across power, utilities, manufacturing and automotive customers on strategic implementations, specializing in using AWS Streaming and advanced data analytics solutions, to drive optimal business outcomes.