All posts by Danilo Poccia

AWS Week in Review – March 20, 2023

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-march-20-2023/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

A new week starts, and Spring is almost here! If you’re curious about AWS news from the previous seven days, I got you covered.

Last Week’s Launches
Here are the launches that got my attention last week:

Picture of an S3 bucket and AWS CEO Adam Selipsky.Amazon S3 – Last week there was AWS Pi Day 2023 celebrating 17 years of innovation since Amazon S3 was introduced on March 14, 2006. For the occasion, the team released many new capabilities:

Amazon Linux 2023 – Our new Linux-based operating system is now generally available. Sébastien’s post is full of tips and info.

Application Auto Scaling – Now can use arithmetic operations and mathematical functions to customize the metrics used with Target Tracking policies. You can use it to scale based on your own application-specific metrics. Read how it works with Amazon ECS services.

AWS Data Exchange for Amazon S3 is now generally available – You can now share and find data files directly from S3 buckets, without the need to create or manage copies of the data.

Amazon Neptune – Now offers a graph summary API to help understand important metadata about property graphs (PG) and resource description framework (RDF) graphs. Neptune added support for Slow Query Logs to help identify queries that need performance tuning.

Amazon OpenSearch Service – The team introduced security analytics that provides new threat monitoring, detection, and alerting features. The service now supports OpenSearch version 2.5 that adds several new features such as support for Point in Time Search and improvements to observability and geospatial functionality.

AWS Lake Formation and Apache Hive on Amazon EMR – Introduced fine-grained access controls that allow data administrators to define and enforce fine-grained table and column level security for customers accessing data via Apache Hive running on Amazon EMR.

Amazon EC2 M1 Mac Instances – You can now update guest environments to a specific or the latest macOS version without having to tear down and recreate the existing macOS environments.

AWS Chatbot – Now Integrates With Microsoft Teams to simplify the way you troubleshoot and operate your AWS resources.

Amazon GuardDuty RDS Protection for Amazon Aurora – Now generally available to help profile and monitor access activity to Aurora databases in your AWS account without impacting database performance

AWS Database Migration Service – Now supports validation to ensure that data is migrated accurately to S3 and can now generate an AWS Glue Data Catalog when migrating to S3.

AWS Backup – You can now back up and restore virtual machines running on VMware vSphere 8 and with multiple vNICs.

Amazon Kendra – There are new connectors to index documents and search for information across these new content: Confluence Server, Confluence Cloud, Microsoft SharePoint OnPrem, Microsoft SharePoint Cloud. This post shows how to use the Amazon Kendra connector for Microsoft Teams.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Example of a geospatial query.Women founders Q&A – We’re talking to six women founders and leaders about how they’re making impacts in their communities, industries, and beyond.

What you missed at that 2023 IMAGINE: Nonprofit conference – Where hundreds of nonprofit leaders, technologists, and innovators gathered to learn and share how AWS can drive a positive impact for people and the planet.

Monitoring load balancers using Amazon CloudWatch anomaly detection alarms – The metrics emitted by load balancers provide crucial and unique insight into service health, service performance, and end-to-end network performance.

Extend geospatial queries in Amazon Athena with user-defined functions (UDFs) and AWS Lambda – Using a solution based on Uber’s Hexagonal Hierarchical Spatial Index (H3) to divide the globe into equally-sized hexagons.

How cities can use transport data to reduce pollution and increase safety – A guest post by Rikesh Shah, outgoing head of open innovation at Transport for London.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
Here are some opportunities to meet:

AWS Public Sector Day 2023 (March 21, London, UK) – An event dedicated to helping public sector organizations use technology to achieve more with less through the current challenging conditions.

Women in Tech at Skills Center Arlington (March 23, VA, USA) – Let’s celebrate the history and legacy of women in tech.

The AWS Summits season is warming up! You can sign up here to know when registration opens in your area.

That’s all from me for this week. Come back next Monday for another Week in Review!

Danilo

New – Use Amazon S3 Object Lambda with Amazon CloudFront to Tailor Content for End Users

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-use-amazon-s3-object-lambda-with-amazon-cloudfront-to-tailor-content-for-end-users/

With S3 Object Lambda, you can use your own code to process data retrieved from Amazon S3 as it is returned to an application. Over time, we added new capabilities to S3 Object Lambda, like the ability to add your own code to S3 HEAD and LIST API requests, in addition to the support for S3 GET requests that was available at launch.

Today, we are launching aliases for S3 Object Lambda Access Points. Aliases are now automatically generated when S3 Object Lambda Access Points are created and are interchangeable with bucket names anywhere you use a bucket name to access data stored in Amazon S3. Therefore, your applications don’t need to know about S3 Object Lambda and can consider the alias to be a bucket name.

Architecture diagram.

You can now use an S3 Object Lambda Access Point alias as an origin for your Amazon CloudFront distribution to tailor or customize data for end users. You can use this to implement automatic image resizing or to tag or annotate content as it is downloaded. Many images still use older formats like JPEG or PNG, and you can use a transcoding function to deliver images in more efficient formats like WebP, BPG, or HEIC. Digital images contain metadata, and you can implement a function that strips metadata to help satisfy data privacy requirements.

Architecture diagram.

Let’s see how this works in practice. First, I’ll show a simple example using text that you can follow along by just using the AWS Management Console. After that, I’ll implement a more advanced use case processing images.

Using an S3 Object Lambda Access Point as the Origin of a CloudFront Distribution
For simplicity, I am using the same application in the launch post that changes all text in the original file to uppercase. This time, I use the S3 Object Lambda Access Point alias to set up a public distribution with CloudFront.

I follow the same steps as in the launch post to create the S3 Object Lambda Access Point and the Lambda function. Because the Lambda runtimes for Python 3.8 and later do not include the requests module, I update the function code to use urlopen from the Python Standard Library:

import boto3
from urllib.request import urlopen

s3 = boto3.client('s3')

def lambda_handler(event, context):
  print(event)

  object_get_context = event['getObjectContext']
  request_route = object_get_context['outputRoute']
  request_token = object_get_context['outputToken']
  s3_url = object_get_context['inputS3Url']

  # Get object from S3
  response = urlopen(s3_url)
  original_object = response.read().decode('utf-8')

  # Transform object
  transformed_object = original_object.upper()

  # Write object back to S3 Object Lambda
  s3.write_get_object_response(
    Body=transformed_object,
    RequestRoute=request_route,
    RequestToken=request_token)

  return

To test that this is working, I open the same file from the bucket and through the S3 Object Lambda Access Point. In the S3 console, I select the bucket and a sample file (called s3.txt) that I uploaded earlier and choose Open.

Console screenshot.

A new browser tab is opened (you might need to disable the pop-up blocker in your browser), and its content is the original file with mixed-case text:

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers...

I choose Object Lambda Access Points from the navigation pane and select the AWS Region I used before from the dropdown. Then, I search for the S3 Object Lambda Access Point that I just created. I select the same file as before and choose Open.

Console screenshot.

In the new tab, the text has been processed by the Lambda function and is now all in uppercase:

AMAZON SIMPLE STORAGE SERVICE (AMAZON S3) IS AN OBJECT STORAGE SERVICE THAT OFFERS...

Now that the S3 Object Lambda Access Point is correctly configured, I can create the CloudFront distribution. Before I do that, in the list of S3 Object Lambda Access Points in the S3 console, I copy the Object Lambda Access Point alias that has been automatically created:

Console screenshot.

In the CloudFront console, I choose Distributions in the navigation pane and then Create distribution. In the Origin domain, I use the S3 Object Lambda Access Point alias and the Region. The full syntax of the domain is:

ALIAS.s3.REGION.amazonaws.com

Console screenshot.

S3 Object Lambda Access Points cannot be public, and I use CloudFront origin access control (OAC) to authenticate requests to the origin. For Origin access, I select Origin access control settings and choose Create control setting. I write a name for the control setting and select Sign requests and S3 in the Origin type dropdown.

Console screenshot.

Now, my Origin access control settings use the configuration I just created.

Console screenshot.

To reduce the number of requests going through S3 Object Lambda, I enable Origin Shield and choose the closest Origin Shield Region to the Region I am using. Then, I select the CachingOptimized cache policy and create the distribution. As the distribution is being deployed, I update permissions for the resources used by the distribution.

Setting Up Permissions to Use an S3 Object Lambda Access Point as the Origin of a CloudFront Distribution
First, the S3 Object Lambda Access Point needs to give access to the CloudFront distribution. In the S3 console, I select the S3 Object Lambda Access Point and, in the Permissions tab, I update the policy with the following:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "cloudfront.amazonaws.com"
            },
            "Action": "s3-object-lambda:Get*",
            "Resource": "arn:aws:s3-object-lambda:REGION:ACCOUNT:accesspoint/NAME",
            "Condition": {
                "StringEquals": {
                    "aws:SourceArn": "arn:aws:cloudfront::ACCOUNT:distribution/DISTRIBUTION-ID"
                }
            }
        }
    ]
}

The supporting access point also needs to allow access to CloudFront when called via S3 Object Lambda. I select the access point and update the policy in the Permissions tab:

{
    "Version": "2012-10-17",
    "Id": "default",
    "Statement": [
        {
            "Sid": "s3objlambda",
            "Effect": "Allow",
            "Principal": {
                "Service": "cloudfront.amazonaws.com"
            },
            "Action": "s3:*",
            "Resource": [
                "arn:aws:s3:REGION:ACCOUNT:accesspoint/NAME",
                "arn:aws:s3:REGION:ACCOUNT:accesspoint/NAME/object/*"
            ],
            "Condition": {
                "ForAnyValue:StringEquals": {
                    "aws:CalledVia": "s3-object-lambda.amazonaws.com"
                }
            }
        }
    ]
}

The S3 bucket needs to allow access to the supporting access point. I select the bucket and update the policy in the Permissions tab:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "*"
            },
            "Action": "*",
            "Resource": [
                "arn:aws:s3:::BUCKET",
                "arn:aws:s3:::BUCKET/*"
            ],
            "Condition": {
                "StringEquals": {
                    "s3:DataAccessPointAccount": "ACCOUNT"
                }
            }
        }
    ]
}

Finally, CloudFront needs to be able to invoke the Lambda function. In the Lambda console, I choose the Lambda function used by S3 Object Lambda, and then, in the Configuration tab, I choose Permissions. In the Resource-based policy statements section, I choose Add permissions and select AWS Account. I enter a unique Statement ID. Then, I enter cloudfront.amazonaws.com as Principal and select lambda:InvokeFunction from the Action dropdown and Save. We are working to simplify this step in the future. I’ll update this post when that’s available.

Testing the CloudFront Distribution
When the distribution has been deployed, I test that the setup is working with the same sample file I used before. In the CloudFront console, I select the distribution and copy the Distribution domain name. I can use the browser and enter https://DISTRIBUTION_DOMAIN_NAME/s3.txt in the navigation bar to send a request to CloudFront and get the file processed by S3 Object Lambda. To quickly get all the info, I use curl with the -i option to see the HTTP status and the headers in the response:

curl -i https://DISTRIBUTION_DOMAIN_NAME/s3.txt

HTTP/2 200 
content-type: text/plain
content-length: 427
x-amzn-requestid: a85fe537-3502-4592-b2a9-a09261c8c00c
date: Mon, 06 Mar 2023 10:23:02 GMT
x-cache: Miss from cloudfront
via: 1.1 a2df4ad642d78d6dac65038e06ad10d2.cloudfront.net (CloudFront)
x-amz-cf-pop: DUB56-P1
x-amz-cf-id: KIiljCzYJBUVVxmNkl3EP2PMh96OBVoTyFSMYDupMd4muLGNm2AmgA==

AMAZON SIMPLE STORAGE SERVICE (AMAZON S3) IS AN OBJECT STORAGE SERVICE THAT OFFERS...

It works! As expected, the content processed by the Lambda function is all uppercase. Because this is the first invocation for the distribution, it has not been returned from the cache (x-cache: Miss from cloudfront). The request went through S3 Object Lambda to process the file using the Lambda function I provided.

Let’s try the same request again:

curl -i https://DISTRIBUTION_DOMAIN_NAME/s3.txt

HTTP/2 200 
content-type: text/plain
content-length: 427
x-amzn-requestid: a85fe537-3502-4592-b2a9-a09261c8c00c
date: Mon, 06 Mar 2023 10:23:02 GMT
x-cache: Hit from cloudfront
via: 1.1 145b7e87a6273078e52d178985ceaa5e.cloudfront.net (CloudFront)
x-amz-cf-pop: DUB56-P1
x-amz-cf-id: HEx9Fodp184mnxLQZuW62U11Fr1bA-W1aIkWjeqpC9yHbd0Rg4eM3A==
age: 3

AMAZON SIMPLE STORAGE SERVICE (AMAZON S3) IS AN OBJECT STORAGE SERVICE THAT OFFERS...

This time the content is returned from the CloudFront cache (x-cache: Hit from cloudfront), and there was no further processing by S3 Object Lambda. By using S3 Object Lambda as the origin, the CloudFront distribution serves content that has been processed by a Lambda function and can be cached to reduce latency and optimize costs.

Resizing Images Using S3 Object Lambda and CloudFront
As I mentioned at the beginning of this post, one of the use cases that can be implemented using S3 Object Lambda and CloudFront is image transformation. Let’s create a CloudFront distribution that can dynamically resize an image by passing the desired width and height as query parameters (w and h respectively). For example:

https://DISTRIBUTION_DOMAIN_NAME/image.jpg?w=200&h=150

For this setup to work, I need to make two changes to the CloudFront distribution. First, I create a new cache policy to include query parameters in the cache key. In the CloudFront console, I choose Policies in the navigation pane. In the Cache tab, I choose Create cache policy. Then, I enter a name for the cache policy.

Console screenshot.

In the Query settings of the Cache key settings, I select the option to Include the following query parameters and add w (for the width) and h (for the height).

Console screenshot.

Then, in the Behaviors tab of the distribution, I select the default behavior and choose Edit.

There, I update the Cache key and origin requests section:

  • In the Cache policy, I use the new cache policy to include the w and h query parameters in the cache key.
  • In the Origin request policy, use the AllViewerExceptHostHeader managed policy to forward query parameters to the origin.

Console screenshot.

Now I can update the Lambda function code. To resize images, this function uses the Pillow module that needs to be packaged with the function when it is uploaded to Lambda. You can deploy the function using a tool like the AWS SAM CLI or the AWS CDK. Compared to the previous example, this function also handles and returns HTTP errors, such as when content is not found in the bucket.

import io
import boto3
from urllib.request import urlopen, HTTPError
from PIL import Image

from urllib.parse import urlparse, parse_qs

s3 = boto3.client('s3')

def lambda_handler(event, context):
    print(event)

    object_get_context = event['getObjectContext']
    request_route = object_get_context['outputRoute']
    request_token = object_get_context['outputToken']
    s3_url = object_get_context['inputS3Url']

    # Get object from S3
    try:
        original_image = Image.open(urlopen(s3_url))
    except HTTPError as err:
        s3.write_get_object_response(
            StatusCode=err.code,
            ErrorCode='HTTPError',
            ErrorMessage=err.reason,
            RequestRoute=request_route,
            RequestToken=request_token)
        return

    # Get width and height from query parameters
    user_request = event['userRequest']
    url = user_request['url']
    parsed_url = urlparse(url)
    query_parameters = parse_qs(parsed_url.query)

    try:
        width, height = int(query_parameters['w'][0]), int(query_parameters['h'][0])
    except (KeyError, ValueError):
        width, height = 0, 0

    # Transform object
    if width > 0 and height > 0:
        transformed_image = original_image.resize((width, height), Image.ANTIALIAS)
    else:
        transformed_image = original_image

    transformed_bytes = io.BytesIO()
    transformed_image.save(transformed_bytes, format='JPEG')

    # Write object back to S3 Object Lambda
    s3.write_get_object_response(
        Body=transformed_bytes.getvalue(),
        RequestRoute=request_route,
        RequestToken=request_token)

    return

I upload a picture I took of the Trevi Fountain in the source bucket. To start, I generate a small thumbnail (200 by 150 pixels).

https://DISTRIBUTION_DOMAIN_NAME/trevi-fountain.jpeg?w=200&h=150

Picture of the Trevi Fountain with size 200x150 pixels.

Now, I ask for a slightly larger version (400 by 300 pixels):

https://DISTRIBUTION_DOMAIN_NAME/trevi-fountain.jpeg?w=400&h=300

Picture of the Trevi Fountain with size 400x300 pixels.

It works as expected. The first invocation with a specific size is processed by the Lambda function. Further requests with the same width and height are served from the CloudFront cache.

Availability and Pricing
Aliases for S3 Object Lambda Access Points are available today in all commercial AWS Regions. There is no additional cost for aliases. With S3 Object Lambda, you pay for the Lambda compute and request charges required to process the data, and for the data S3 Object Lambda returns to your application. You also pay for the S3 requests that are invoked by your Lambda function. For more information, see Amazon S3 Pricing.

Aliases are now automatically generated when an S3 Object Lambda Access Point is created. For existing S3 Object Lambda Access Points, aliases are automatically assigned and ready for use.

It’s now easier to use S3 Object Lambda with existing applications, and aliases open many new possibilities. For example, you can use aliases with CloudFront to create a website that converts content in Markdown to HTML, resizes and watermarks images, or masks personally identifiable information (PII) from text, images, and documents.

Customize content for your end users using S3 Object Lambda with CloudFront.

Danilo

How to Connect Business and Technology to Embrace Strategic Thinking (Book Review)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/how-to-connect-business-and-technology-to-embrace-strategic-thinking-book-review/

The Value Flywheel Effect: Power the Future and Accelerate Your Organization to the Modern Cloud
by David Anderson with Mark McCann and Michael O’Reilly

With this post, I’d like to share a new book that got my attention. It’s a book at the intersection of business, technology, and people. This is a great read for anyone who wants to understand how organizations can evolve to maximize the business impact of new technologies and speed up their internal processes.

The Value FlyWheel Effect book with David Anderson and Danilo Poccia

Last year at re:Invent, I had the opportunity to meet David Anderson. As Director of Technology at Liberty Mutual, he drove the technology change when the global insurance company, founded in 1912, moved its services to the cloud and adopted a serverless-first strategy. He created an environment where experimentation was normal, and software engineers had time and space to learn. This worked so well that, at some point, he had four AWS Heroes in his extended team.

A few months before, I heard that David was writing a book with Mark McCann and Michael O’Reilly. They all worked together at Liberty Mutual, and they were distilling their learnings to help other organizations implement a similar approach. The book was just out when we met, and I was curious to learn more, starting from the title. We met in the expo area, and David was kind enough to give me a signed copy of the book.

The book is published by IT Revolution, the same publisher behind some of my favorite books such as The Phoenix Project, Team Topologies, and Accelerate. The book is titled The Value Flywheel Effect because when you connect business and technology in an organization, you start to turn a flywheel that builds momentum with each small win.

The Value Flywhell
The four phases of the Value Flywheel are:

  1. Clarity of Purpose – This is the part where you look at what is really important for your organization, what makes your company different, and define your North Star and how to measure your distance from it. In this phase, you look at the company through the eyes of the CEO.
  2. Challenge & Landscape – Here you prepare the organization and set up the environment for the teams. We often forget the social aspect of technical teams and great focus is given here on how to set up the right level of psychological safety for teams to operate. This phase is for engineers.
  3. Next Best Action – In this phase, you think like a product leader and plan the next steps with a focus on how to improve the developer experience. One of the key aspects is that “code is a liability” and the less code you write to solve a business problem, the better it is for speed and maintenance. For example, you can avoid some custom implementations and offload their requirements to capabilities offered by cloud providers.
  4. Long-Term Value – This is the CTO perspective, looking at how to set up a problem-preventing culture with well-architected systems and a focus on observability and sustainability. Sustainability here is not just considering the global environment but also the teams and the people working for the organization.

As you would expect from a flywheel, you should iterate on these four phases so that every new spin gets easier and faster.

Wardley Mapping
One thing that I really appreciate from the book is how it made it easy for me to use Wardley mapping (usually applied to a business context) in a technical scenario. Wardley maps, invented by Simon Wardley, provide a visual representation of the landscape in which a business operates.

Each map consists of a value chain, where you draw the components that your customers need. The components are connected to show how they depend on each other. The position of the components is based on how visible they are to customers (vertical) and their evolution status from genesis to being a product or a commodity (horizontal). Over time, some components evolve from being custom-built to becoming a product or being commoditized. This displays on the map with a natural movement to the right as things evolve. For example, data centers were custom-built in the past, but then they became a standard product, and cloud computing made them available as a commodity.

Wardley mapping – Basic elements of a map

Basic elements of a map – Provided courtesy of Simon Wardley, CC BY-SA 4.0.

With mapping, you can more easily understand what improvements you need and what gaps you have in your technical solution. In this way, engineers can identify which components they should focus on to maximize their impact and what parts are not strategic and can be offloaded to a SaaS solution. It’s a sort of evolutionary architecture where mapping gives a way to look ahead at how the system should evolve over time and where inertia can slow down the evolution of part of the system.

Sometimes it seems the same best practices apply everywhere but this is not true. An advantage of mapping is that it helps identify the best team and methodology to use based on a component evolution status as described by its horizontal position on a map. For example, an “explorer” attitude is best suited for components in their genesis or being custom built, a “villager” works best on products, and when something becomes a commodity you need a “town planner.”

More Tools and Less Code
The authors look at many available tools and frameworks. For example, the book introduces the North Star Framework, a way to manage products by first identifying their most important metric (the North Star), and Gojko Adzic‘s Impact Mapping, a collaborative planning technique that focuses on leading indicators to help teams make a big impact with their software products. By the way, Gojko is also an AWS Serverless Hero.

Another interesting point is how to provide engineers with the necessary time and space to learn. I specifically like how internal events are called out and compared to public conferences. In internal events, engineers have a chance to use a new technology within their company environment, making it easier to demonstrate what can be done with all the limits of an actual scenario.

Finally, I’d like to highlight this part that clearly defines what the book intends by the statements, “code is a liability”:

“When you ask a software team to build something, they deliver a system, not lines of code. The asset is not the code; the asset is the system. The less code in the system, the less overhead you have bought. Some developers may brag about how much code they’ve written, but this isn’t something to brag about.”

This is not a programming book, and serverless technologies are used as examples of how you can speed up the flywheel. If you are looking for a technical deep dive on serverless technologies, you can find more on Serverless Land, a site that brings together the latest information and learning resources for serverless computing, or have a look at the Serverless Architectures on AWS book.

Now that every business is a technology business, The Value Flywheel Effect is about how to accelerate and transform an organization. It helps set the right environment, purpose, and stage to modernize your applications as you adopt cloud computing and get the benefit of it.

You can meet David, Mark, and Michael at the Serverless Edge, where a team of engineers, tech enthusiasts, marketers, and thought leaders obsessed with technology help learn and communicate how serverless can transform a business model.

Danilo

New for Amazon Redshift – Simplify Data Ingestion and Make Your Data Warehouse More Secure and Reliable

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-amazon-redshift-simplify-data-ingestion-and-make-your-data-warehouse-more-secure-and-reliable/

When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions. A common pattern with data-driven organizations is that they have many different data sources they need to ingest into their analytics systems. This requires them to build manual data pipelines spanning across their operational databases, data lakes, streaming data, and data within their warehouse. As a consequence of this complex setup, it can take data engineers weeks or even months to build data ingestion pipelines. These data pipelines are costly, and the delays can lead to missed business opportunities. Additionally, data warehouses are increasingly becoming mission critical systems that require high availability, reliability, and security.

Amazon Redshift is a fully managed petabyte-scale data warehouse used by tens of thousands of customers to easily, quickly, securely, and cost-effectively analyze all their data at any scale. This year at re:Invent, Amazon Redshift has announced a number of features to help you simplify data ingestion and get to insights easily and quickly, within a secure, reliable environment.

In this blog, I introduce some of these new features that fit into two main categories:

  • Simplify data ingestion
    • Amazon Redshift now supports auto-copy from Amazon S3 (available in preview). With this new capability, Amazon Redshift automatically loads the files that arrive in an Amazon Simple Storage Service (Amazon S3) location that you specify into your data warehouse. The files can use any of the formats supported by the Amazon Redshift copy command, such as CSV, JSON, Parquet, and Avro. In this way, you don’t need to manually or repeatedly run copy procedures. Amazon Redshift automates file ingestion and takes care of data-loading steps under the hood.
    • With Amazon Aurora zero-ETL integration with Amazon Redshift, you can use Amazon Redshift for near real-time analytics and machine learning on petabytes of transactional data stored on Amazon Aurora MySQL databases (available in limited preview). With this capability, you can choose the Amazon Aurora databases containing the data you want to analyze with Amazon Redshift. Data is then replicated into your data warehouse within seconds after transactional data is written into Amazon Aurora, eliminating the need to build and maintain complex data pipelines. You can replicate data from multiple Amazon Aurora databases into the same Amazon Redshift instance to run analytics across multiple applications. With near real-time access to transactional data, you can leverage Amazon Redshift’s analytics and capabilities, such as built-in machine learning (ML), materialized views, data sharing, and federated access to multiple data stores and data lakes, to derive insights from transactional and other data.
    • With the general availability of Amazon Redshift Streaming Ingestion, you can now natively ingest hundreds of megabytes of data per second from Amazon Kinesis Data Streams and Amazon MSK into an Amazon Redshift materialized view and query it in seconds. Learn more in this post.
  • Make your data warehouse more secure and reliable
    • You can now improve the availability of your data warehouse by choosing multiple Availability Zone (AZ) deployments. Multi-AZ deployments for your Amazon Redshift clusters are available in preview and reduce recovery times to seconds through automatic recovery. In this way, you can build solutions that are more compliant with the recommendations of the Reliability Pillar of the AWS Well-Architected Framework.
    • With dynamic data masking (available in preview), you can protect sensitive information stored in your data warehouse and ensure that only the relevant data is accessible by users based on their roles. You can limit how much identifiable data is visible to users using multiple levels of policies so different users and groups can have different levels of data access without having to create multiple copies of data. Dynamic data masking complements other granular access control capabilities in Amazon Redshift including row-level and column-level security and role-based access controls. In this way, Dynamic Data Masking helps you meet requirements for GDPR, CCPA, and other privacy regulations.
    • Amazon Redshift now supports central access controls for data sharing with AWS Lake Formation (available in public preview). You can now use Lake Formation to simplify governance of data shared from Amazon Redshift and centrally manage granular access across all data-sharing consumers.

There have been other interesting news for Amazon Redshift at re:Invent you might have already heard about:

  • The general availability of Amazon Redshift integration for Apache Spark makes it easy to build and run Spark applications on Amazon Redshift and Redshift Serverless, opening up the data warehouse for a broader set of AWS analytics and machine learning solutions.
  • AWS Backup now supports Amazon Redshift. AWS Backup allows you to define a central backup policy to manage data protection of your applications and can also protect your Amazon Redshift clusters. In this way, you have a consistent experience when managing data protection across all supported services.

Availability and Pricing
Multi-AZ deployments, central access control for data sharing with AWS Lake Formation, auto-copy from Amazon S3, and dynamic data masking are available in preview in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm).

There is no additional cost for using auto-copy from Amazon S3 and near real-time analytics on transactional data. There is no extra charge for dynamic data masking and central access control for data sharing. For more information, see Amazon Redshift pricing.

These new capabilities take you one step further in analyzing all your data across data sources with simple data ingestion capabilities, while improving the security and reliability of your data warehouse.

Danilo

Introducing VPC Lattice – Simplify Networking for Service-to-Service Communication (Preview)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-vpc-lattice-simplify-networking-for-service-to-service-communication-preview/

Modern applications are built using modular and distributed components. Each component is a service that implements its own subset of functionalities. To make these services communicate with each other, you need a way to let them discover where they are, authorize access, and route traffic. When troubleshooting issues, you need to keep communication configurations under control so that you can quickly understand what is happening at the application, service, and network levels. This can take a lot of your time.

Today, we are making available in preview Amazon VPC Lattice, a new capability of Amazon Virtual Private Cloud (Amazon VPC) that gives you a consistent way to connect, secure, and monitor communication between your services. With VPC Lattice, you can define policies for traffic management, network access, and monitoring so you can connect applications in a simple and consistent way across AWS compute services (instances, containers, and serverless functions). VPC Lattice automatically handles network connectivity between VPCs and accounts and network address translation between IPv4, IPv6, and overlapping IP addresses. VPC Lattice integrates with AWS Identity and Access Management (IAM) to give you the same authentication and authorization capabilities you are familiar with when interacting with AWS services today, but for your own service-to-service communication. With VPC Lattice, you have common controls to route traffic based on request characteristics and weighted routing for blue/green and canary-style deployments. For example, VPC Lattice allows you to mix and match compute types for a given service, which helps you modernize a monolith application architecture to microservices.

VPC Lattice is designed to be noninvasive, allowing teams across your organization to incrementally opt in over time. In this way, you are able to deliver applications faster by focusing on your application logic, while VPC Lattice handles service-to-service networking, security, and monitoring requirements.

How Amazon VPC Lattice Works
With VPC Lattice, you create a logical application layer network, called a service network, that connects clients and services across different VPCs and accounts, abstracting network complexity. A service network is a logical boundary that is used to automatically implement service discovery and connectivity as well as apply access and observability policies to a collection of services. It offers inter-application connectivity over HTTP/HTTPS and gRPC protocols within a VPC.

Once a VPC has been enabled for a service network, clients in the VPC will automatically be able to discover the services in the service network through DNS and will direct all inter-application traffic through VPC Lattice. You can use AWS Resource Access Manager (RAM) to control which accounts, VPCs, and applications can establish communication via VPC Lattice.

A service is an independently deployable unit of software that delivers a specific task or function. In VPC Lattice, a service is a logical component that can live in any VPC or account and can run on a mixture of compute types (virtual machines, containers, and serverless functions). A service configuration consists of:

  • One or two listeners that define the port and protocol that the service is expecting traffic on. Supported protocols are HTTP/1.1, HTTP/2, and gRPC, including HTTPS for TLS-enabled services.
  • Listeners have rules that consist of a priority, which specifies the order in which rules should be processed, one or more conditions that define when to apply the rule, and actions that forward traffic to target groups. Each listener has a default rule that takes effect when no additional rules are configured, or no conditions are met.
  • A target group is a collection of targets, or compute resources, that are running a specific workload you are trying to route toward. Targets can be Amazon Elastic Compute Cloud (Amazon EC2) instances, IP addresses, and Lambda functions. For Kubernetes workloads, VPC Lattice can target services and pods via the AWS Gateway Controller for Kubernetes. To have access to the AWS Gateway Controller for Kubernetes, you can join the preview.

VPC Lattice logical architecture.

To configure service access controls, you can use access policies. An access policy is an IAM resource policy that can be associated with a service network and individual services. With access policies, you can use the “PARC” (principal, action, resource, and condition) model to enforce context-specific access controls for services. For example, you can use an access policy to define which services can access a service you own. If you use AWS Organizations, you can limit access to a service network to a specific organization.

VPC Lattice also provides a service directory, a centralized view of the services that you own or have been shared with you via AWS RAM.

Using Amazon VPC Lattice
We expect people with different roles can use VPC Lattice. For example:

  • The service network administrator can:
    • Create and manage a service network.
    • Define access and monitoring for the service network.
    • Associate client and services.
    • Share the service network with other AWS accounts.
  • The service owner can:
    • Create and manage a service, including access and monitoring.
    • Define routing, for example, configuring listeners and rules that point to the target groups where the service is running.
    • Associate a service to service networks.

Let’s see how this works in practice. In this quick walkthrough, I am covering both roles.

Creating Two Backend Services
There is nothing specific to VPC Lattice in this section. I am just creating a couple of services, one running on Amazon EC2 and one on AWS Lambda, that I’ll use later when I configure networking with VPC Lattice.

In an Amazon Linux EC2 instance, I create a web app that replies “Hello from the instance” to HTTP requests. To allow access to the instance from clients coming via VPC Lattice, I add an inbound rule to the security group to allow TCP traffic on port 8080 from the VPC Lattice AWS-managed prefix list.

Here’s the app.py file. I am using Python and Flask for this app, but you don’t need to know them to follow along with the post.

from flask import Flask

app = Flask(__name__)

@app.route('/')
def index():
  return 'Hello from the instance'

@app.route('/<path>')
def somePath(path):
  return 'Hello from the instance at path "{}"'.format(path)

app.run(host='0.0.0.0', port=8080)

Here’s the requirements.txt file with the Python dependencies. There’s only one line because the only module I need is flask:

flask

I install the dependencies:

pip3 install -r requirements.txt

Then, I start the web app using the nohup command to keep it running in case I log out of the instance:

nohup flask run --host=0.0.0.0 --port 8080 &

On the EC2 instance, the web service is now listening to HTTP traffic on port 8080.

In the Lambda console, I create a simple function using the Node.js 18.x runtime that replies “Hello from the function” to all invocations.

exports.handler = async (event) => {
    const response = {
        statusCode: 200,
        body: JSON.stringify('Hello from the function'),
    };
    return response;
};

The two services are now both ready. Let’s use VPC Lattice to configure networking.

Creating VPC Lattice Target Groups
I start by creating two target groups, one for the EC2 instance and one for the Lambda function. In the VPC console, there is a new VPC Lattice section in the navigation pane. There, I choose Target groups and then Create target group.

For the first target group, I choose the Instances target type and enter a name.

Console screenshot.

I choose the protocol (HTTP) and port (8080) used by the web app running on the instance. I select the VPC where the instance is running and the protocol version (HTTP1).

Console screenshot.

Now I can configure the health check that will be used to test the target status. In this case, I use the default values proposed by the console.

Console screenshot.

In the next step, I can register the targets. I select the instance on which the web app is running from the list and choose to include it.

Console screenshot.

I review the selected targets (one instance in this case) and choose Submit.

In a similar way, I create a target group for the Lambda function. This time, I select the function from the list. I can choose which function version or function alias to use. For simplicity, I use the $LATEST version.

Console screenshot.

Creating VPC Lattice Services
Now that the target groups are ready, I choose Services in the navigation pane and then Create service. I enter a name and a description.

Console screenshot.

Now, I can choose the authentication type. If I choose None, the service network does not authenticate or authorize client access, and the auth policy, if present, is not used. I select AWS IAM and then, from the Apply policy template dropdown, the template that allows both authenticated and unauthenticated access.

Console screenshot.

In the Monitoring section, I turn on Access logs. As the destination for the access logs, I use an Amazon CloudWatch Log group that I created before. I also have the option to use an Amazon Simple Storage Service (Amazon S3) bucket or a Amazon Kinesis Data Firehose delivery stream.

Console screenshot.

In the next step, I define routing for the service. I choose Add listener. For the protocol, I configure the service to listen using HTTPS. In the default action, I choose to send two-thirds (Weight 20) of the requests to the instance target group and one-third (Weight 10) to the function target group.

Console screenshot.

Then, I add two additional rules. The first rule (Priority 10) sends all requests where the path is /to-instance to the instance target group.

Console screenshot.

The second rule (Priority 20) sends all traffic where the path is /to-function to the function target group.

Console screenshot.

In the next step, I am asked to associate the service with one or more service networks. I didn’t create a service network yet, so I skip this step for now and choose Next. I review the configuration and create the service.

Creating VPC Lattice Service Networks
Now, I create the service network so that I can associate the service and the VPCs I want to use. I choose Service network from the navigation pane and then Create service network. I enter a name and a description for the service network.

Console screenshot.

In the Associate services, I select the service I just created.

Console screenshot.

In the VPC associations, I select the VPC used by the instance where the web app runs. This can help in the future because it allows the web app to call other services associated with the service network.

Console screenshot.

Then, I select a second VPC where I have another EC2 instance that I want to use to run some tests.

Console screenshot.

For simplicity, in the Access section, I select the None auth type.

Console screenshot.

In the Monitoring section, I choose to send the access logs for the whole service network to an S3 bucket.

Console screenshot.

I review the summary of the configuration and create the service network. After a few seconds all service and VPC associations are active, and I can start using the service.

I write down the domain name of the service from the list of service associations.

Console screenshot.

Testing Access to the Service Using VPC Lattice
I look at the Routing tab of the service to find a nice recap of how the listener is handling routing towards the different target groups.

Console screenshot.

Then, I log into the EC2 instance in my second VPC and use curl to call the service domain name. As expected, I get about two-thirds of the responses from the instance and one-third from the function.

curl https://my-service-03e92ee54968d87ca.7d67968.vpc-lattice-svcs.us-west-2.on.aws
Hello from the instance

curl https://my-service-03e92ee54968d87ca.7d67968.vpc-lattice-svcs.us-west-2.on.aws
Hello from the instance

curl https://my-service-03e92ee54968d87ca.7d67968.vpc-lattice-svcs.us-west-2.on.aws
"Hello from the function"

When I call the /to-instance and /to-function paths, the additional rules forward the requests to the instance and the function, respectively.

curl https://my-service-03e92ee54968d87ca.7d67968.vpc-lattice-svcs.us-west-2.on.aws/to-instance
Hello from the instance "to-instance" path

curl https://my-service-03e92ee54968d87ca.7d67968.vpc-lattice-svcs.us-west-2.on.aws/to-function
"Hello from the function"

I can now review access to my service using the access log subscriptions I configured before.

For the service, I look in the CloudWatch Log group. There, I find a log stream containing detailed access information about the service.

Console screenshot.

The access log for all services associated with the service network is on the S3 bucket. I have only one service for now, but more are coming.

Console screenshot.

Available in Preview
Amazon VPC Lattice is available in preview in the US West (Oregon) Region.

VPC Lattice provides deployment consistency across AWS compute types so that you can connect your services across instances, containers, and serverless functions. You can use VPC Lattice to apply granular and rich traffic controls, such as policy-based routing and weighted targets to support blue/green and canary-style deployments.

VPC Lattice allows monitoring and troubleshooting service-to-service communication with detailed access logs and metrics that capture request type, volume of traffic, error rates, response time, and more. In this blog post, I only scratched the surface of what you can do with VPC Lattice.

Simplify the way you connect, secure, and monitor service-to-service communication with Amazon VPC Lattice.

New for Amazon Redshift – General Availability of Streaming Ingestion for Kinesis Data Streams and Managed Streaming for Apache Kafka

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-amazon-redshift-general-availability-of-streaming-ingestion-for-kinesis-data-streams-and-managed-streaming-for-apache-kafka/

Ten years ago, just a few months after I joined AWS, Amazon Redshift was launched. Over the years, many features have been added to improve performance and make it easier to use. Amazon Redshift now allows you to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes. More recently, Amazon Redshift Serverless became generally available to make it easier to run and scale analytics without having to manage your data warehouse infrastructure.

To process data as quickly as possible from real-time applications, customers are adopting streaming engines like Amazon Kinesis and Amazon Managed Streaming for Apache Kafka. Previously, to load streaming data into your Amazon Redshift database, you’d have to configure a process to stage data in Amazon Simple Storage Service (Amazon S3) before loading. Doing so would introduce a latency of one minute or more, depending on the volume of data.

Today, I am happy to share the general availability of Amazon Redshift Streaming Ingestion. With this new capability, Amazon Redshift can natively ingest hundreds of megabytes of data per second from Amazon Kinesis Data Streams and Amazon MSK into an Amazon Redshift materialized view and query it in seconds.

Architecture diagram.

Streaming ingestion benefits from the ability to optimize query performance with materialized views and allows the use of Amazon Redshift more efficiently for operational analytics and as the data source for real-time dashboards. Another interesting use case for streaming ingestion is analyzing real-time data from gamers to optimize their gaming experience. This new integration also makes it easier to implement analytics for IoT devices, clickstream analysis, application monitoring, fraud detection, and live leaderboards.

Let’s see how this works in practice.

Configuring Amazon Redshift Streaming Ingestion
Apart from managing permissions, Amazon Redshift streaming ingestion can be configured entirely with SQL within Amazon Redshift. This is especially useful for business users who lack access to the AWS Management Console or the expertise to configure integrations between AWS services.

You can set up streaming ingestion in three steps:

  1. Create or update an AWS Identity and Access Management (IAM) role to allow access to the streaming platform you use (Kinesis Data Streams or Amazon MSK). Note that the IAM role should have a trust policy that allows Amazon Redshift to assume the role.
  2. Create an external schema to connect to the streaming service.
  3. Create a materialized view that references the streaming object (Kinesis data stream or Kafka topic) in the external schemas.

After that, you can query the materialized view to use the data from the stream in your analytics workloads. Streaming ingestion works with Amazon Redshift provisioned clusters and with the new serverless option. To maximize simplicity, I am going to use Amazon Redshift Serverless in this walkthrough.

To prepare my environment, I need a Kinesis data stream. In the Kinesis console, I choose Data streams in the navigation pane and then Create data stream. For the Data stream name, I use my-input-stream and then leave all other options set to their default value. After a few seconds, the Kinesis data stream is ready. Note that by default I am using on-demand capacity mode. In a development or test environment, you can choose provisioned capacity mode with one shard to optimize costs.

Now, I create an IAM role to give Amazon Redshift access to the my-input-stream Kinesis data streams. In the IAM console, I create a role with this policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "kinesis:DescribeStreamSummary",
                "kinesis:GetShardIterator",
                "kinesis:GetRecords",
                "kinesis:DescribeStream"
            ],
            "Resource": "arn:aws:kinesis:*:123412341234:stream/my-input-stream"
        },
        {
            "Effect": "Allow",
            "Action": [
                "kinesis:ListStreams",
                "kinesis:ListShards"
            ],
            "Resource": "*"
        }
    ]
}

To allow Amazon Redshift to assume the role, I use the following trust policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "redshift.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

In the Amazon Redshift console, I choose Redshift serverless from the navigation pane and create a new workgroup and namespace, similar to what I did in this blog post. When I create the namespace, in the Permissions section, I choose Associate IAM roles from the dropdown menu. Then, I select the role I just created. Note that the role is visible in this selection only if the trust policy allows Amazon Redshift to assume it. After that, I complete the creation of the namespace using the default options. After a few minutes, the serverless database is ready for use.

In the Amazon Redshift console, I choose Query editor v2 in the navigation pane. I connect to the new serverless database by choosing it from the list of resources. Now, I can use SQL to configure streaming ingestion. First, I create an external schema that maps to the streaming service. Because I am going to use simulated IoT data as an example, I call the external schema sensors.

CREATE EXTERNAL SCHEMA sensors
FROM KINESIS
IAM_ROLE 'arn:aws:iam::123412341234:role/redshift-streaming-ingestion';

To access the data in the stream, I create a materialized view that selects data from the stream. In general, materialized views contain a precomputed result set based on the result of a query. In this case, the query is reading from the stream, and Amazon Redshift is the consumer of the stream.

Because streaming data is going to be ingested as JSON data, I have two options:

  1. Leave all the JSON data in a single column and use Amazon Redshift capabilities to query semi-structured data.
  2. Extract JSON properties into their own separate columns.

Let’s see the pros and cons of both options.

The approximate_arrival_timestamp, partition_key, shard_id, and sequence_number columns in the SELECT statement are provided by Kinesis Data Streams. The record from the stream is in the kinesis_data column. The refresh_time column is provided by Amazon Redshift.

To leave the JSON data in a single column of the sensor_data materialized view, I use the JSON_PARSE function:

CREATE MATERIALIZED VIEW sensor_data AUTO REFRESH YES AS
    SELECT approximate_arrival_timestamp,
           partition_key,
           shard_id,
           sequence_number,
           refresh_time,
           JSON_PARSE(kinesis_data, 'utf-8') as payload    
      FROM sensors."my-input-stream";
CREATE MATERIALIZED VIEW sensor_data AUTO REFRESH YES AS
SELECT approximate_arrival_timestamp,
partition_key,
shard_id,
sequence_number,
refresh_time,
JSON_PARSE(kinesis_data) as payload 
FROM sensors."my-input-stream";

Because I used the AUTO REFRESH YES parameter, the content of the materialized view is automatically refreshed when there is new data in the stream.

To extract the JSON properties into separate columns of the sensor_data_extract materialized view, I use the JSON_EXTRACT_PATH_TEXT function:

CREATE MATERIALIZED VIEW sensor_data_extract AUTO REFRESH YES AS
    SELECT approximate_arrival_timestamp,
           partition_key,
           shard_id,
           sequence_number,
           refresh_time,
           JSON_EXTRACT_PATH_TEXT(FROM_VARBYTE(kinesis_data, 'utf-8'),'sensor_id')::VARCHAR(8) as sensor_id,
           JSON_EXTRACT_PATH_TEXT(FROM_VARBYTE(kinesis_data, 'utf-8'),'current_temperature')::DECIMAL(10,2) as current_temperature,
           JSON_EXTRACT_PATH_TEXT(FROM_VARBYTE(kinesis_data, 'utf-8'),'status')::VARCHAR(8) as status,
           JSON_EXTRACT_PATH_TEXT(FROM_VARBYTE(kinesis_data, 'utf-8'),'event_time')::CHARACTER(26) as event_time
      FROM sensors."my-input-stream";

Loading Data into the Kinesis Data Stream
To put data in the my-input-stream Kinesis Data Stream, I use the following random_data_generator.py Python script simulating data from IoT sensors:

import datetime
import json
import random
import boto3

STREAM_NAME = "my-input-stream"


def get_random_data():
    current_temperature = round(10 + random.random() * 170, 2)
    if current_temperature > 160:
        status = "ERROR"
    elif current_temperature > 140 or random.randrange(1, 100) > 80:
        status = random.choice(["WARNING","ERROR"])
    else:
        status = "OK"
    return {
        'sensor_id': random.randrange(1, 100),
        'current_temperature': current_temperature,
        'status': status,
        'event_time': datetime.datetime.now().isoformat()
    }


def send_data(stream_name, kinesis_client):
    while True:
        data = get_random_data()
        partition_key = str(data["sensor_id"])
        print(data)
        kinesis_client.put_record(
            StreamName=stream_name,
            Data=json.dumps(data),
            PartitionKey=partition_key)


if __name__ == '__main__':
    kinesis_client = boto3.client('kinesis')
    send_data(STREAM_NAME, kinesis_client)

I start the script and see the records that are being put in the stream. They use a JSON syntax and contain random data.

$ python3 random_data_generator.py
{'sensor_id': 66, 'current_temperature': 69.67, 'status': 'OK', 'event_time': '2022-11-20T18:31:30.693395'}
{'sensor_id': 45, 'current_temperature': 122.57, 'status': 'OK', 'event_time': '2022-11-20T18:31:31.486649'}
{'sensor_id': 15, 'current_temperature': 101.64, 'status': 'OK', 'event_time': '2022-11-20T18:31:31.671593'}
...

Querying Streaming Data from Amazon Redshift
To compare the two materialized views, I select the first ten rows from each of them:

  • In the sensor_data materialized view, the JSON data in the stream is in the payload column. I can use Amazon Redshift JSON functions to access data stored in JSON format.Console screenshot.
  • In the sensor_data_extract materialized view, the JSON data in the stream has been extracted into different columns: sensor_id, current_temperature, status, and event_time.Console screenshot.

Now I can use the data in these views in my analytics workloads together with the data in my data warehouse, my operational databases, and my data lake. I can use the data in these views together with Redshift ML to train a machine learning model or use predictive analytics. Because materialized views support incremental updates, the data in these views can be efficiently used as a data source for dashboards, for example, using Amazon Redshift as a data source for Amazon Managed Grafana.

Availability and Pricing
Amazon Redshift streaming ingestion for Kinesis Data Streams and Managed Streaming for Apache Kafka is generally available today in all commercial AWS Regions.

There are no additional costs for using Amazon Redshift streaming ingestion. For more information, see Amazon Redshift pricing.

It’s never been easier to use low-latency streaming data in your data warehouse and in your data lake. Let us know what you build with this new capability!

Danilo

New – AWS Config Rules Now Support Proactive Compliance

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-aws-config-rules-now-support-proactive-compliance/

When operating a business, you have to find the right balance between speed and control for your cloud operations. On one side, you want to have the ability to quickly provision the cloud resources you need for your applications. At the same time, depending on your industry, you need to maintain compliance with regulatory, security, and operational best practices.

AWS Config provides rules, which you can run in detective mode to evaluate if the configuration settings of your AWS resources are compliant with your desired configuration settings. Today, we are extending AWS Config rules to support proactive mode so that they can be run at any time before provisioning and save time spent to implement custom pre-deployment validations.

When creating standard resource templates, platform teams can run AWS Config rules in proactive mode so that they can be tested to be compliant before being shared across your organization. When implementing a new service or a new functionality, development teams can run rules in proactive mode as part of their continuous integration and continuous delivery (CI/CD) pipeline to identify noncompliant resources.

You can also use AWS CloudFormation Guard in your deployment pipelines to check for compliance proactively and ensure that a consistent set of policies are applied both before and after resources are provisioned.

Let’s see how this works in practice.

Using Proactive Compliance with AWS Config
In the AWS Config console, I choose Rules in the navigation pane. In the rules table, I see the new Enabled evaluation mode column that specifies whether the rule is proactive or detective. Let’s set up my first rule.

Console screenshot.

I choose Add rule, and then I enter rds-storage in the AWS Managed Rules search box to find the rds-storage-encrypted rule. This rule checks whether storage encryption is enabled for your Amazon Relational Database Service (RDS) DB instances and can be added in proactive or detective evaluation mode. I choose Next.

Console screenshot.

In the Evaluation mode section, I turn on proactive evaluation. Now both the proactive and detective evaluation switches are enabled.

Console screenshot.

I leave all the other settings to their default values and choose Next. In the next step, I review the configuration and add the rule.

Console screenshot.

Now, I can use proactive compliance via the AWS Config API (including the AWS Command Line Interface (CLI) and AWS SDKs) or with CloudFormation Guard. In my CI/CD pipeline, I can use the AWS Config API to check the compliance of a resource before creating it. When deploying using AWS CloudFormation, I can set up a CloudFormation hook to proactively check my configuration before the actual deployment happens.

Let’s do an example using the AWS CLI. First, I call the StartProactiveEvaluationResponse API with in input the resource ID (for reference only), the resource type, and its configuration using the CloudFormation schema. For simplicity, in the database configuration, I only use the StorageEncrypted option and set it to true to pass the evaluation. I use an evaluation timeout of 60 seconds, which is more than enough for this rule.

aws configservice start-resource-evaluation --evaluation-mode PROACTIVE \
    --resource-details '{"ResourceId":"myDB",
                         "ResourceType":"AWS::RDS::DBInstance",
                         "ResourceConfiguration":"{\"StorageEncrypted\":true}",
                         "ResourceConfigurationSchemaType":"CFN_RESOURCE_SCHEMA"}' \
    --evaluation-timeout 60
{
    "ResourceEvaluationId": "be2a915a-540d-4595-ac7b-e105e39b7980-1847cb6320d"
}

I get back in output the ResourceEvaluationId that I use to check the evaluation status using the GetResourceEvaluationSummary API. In the beginning, the evaluation is IN_PROGRESS. It usually takes a few seconds to get a COMPLIANT or NON_COMPLIANT result.

aws configservice get-resource-evaluation-summary \
    --resource-evaluation-id be2a915a-540d-4595-ac7b-e105e39b7980-1847cb6320d
{
    "ResourceEvaluationId": "be2a915a-540d-4595-ac7b-e105e39b7980-1847cb6320d",
    "EvaluationMode": "PROACTIVE",
    "EvaluationStatus": {
        "Status": "SUCCEEDED"
    },
    "EvaluationStartTimestamp": "2022-11-15T19:13:46.029000+00:00",
    "Compliance": "COMPLIANT",
    "ResourceDetails": {
        "ResourceId": "myDB",
        "ResourceType": "AWS::RDS::DBInstance",
        "ResourceConfiguration": "{\"StorageEncrypted\":true}"
    }
}

As expected, the Amazon RDS configuration is compliant to the rds-storage-encrypted rule. If I repeat the previous steps with StorageEncrypted set to false, I get a noncompliant result.

If more than one rule is enabled for a resource type, all applicable rules are run in proactive mode for the resource evaluation. To find out individual rule-level compliance for the resource, I can call the GetComplianceDetailsByResource API:

aws configservice get-compliance-details-by-resource \
    --resource-evaluation-id be2a915a-540d-4595-ac7b-e105e39b7980-1847cb6320d
{
    "EvaluationResults": [
        {
            "EvaluationResultIdentifier": {
                "EvaluationResultQualifier": {
                    "ConfigRuleName": "rds-storage-encrypted",
                    "ResourceType": "AWS::RDS::DBInstance",
                    "ResourceId": "myDB",
                    "EvaluationMode": "PROACTIVE"
                },
                "OrderingTimestamp": "2022-11-15T19:14:42.588000+00:00",
                "ResourceEvaluationId": "be2a915a-540d-4595-ac7b-e105e39b7980-1847cb6320d"
            },
            "ComplianceType": "COMPLIANT",
            "ResultRecordedTime": "2022-11-15T19:14:55.588000+00:00",
            "ConfigRuleInvokedTime": "2022-11-15T19:14:42.588000+00:00"
        }
    ]
}

If, when looking at these details, your desired rule is not invoked, be sure to check that proactive mode is turned on.

Availability and Pricing
Proactive compliance will be available in all commercial AWS Regions where AWS Config is offered but it might take a few days to deploy this new capability across all these Regions. I’ll update this post when this deployment is complete. To see which AWS Config rules can be turned into proactive mode, see the Developer Guide.

You are charged based on the number of AWS Config rule evaluations recorded. A rule evaluation is recorded every time a resource is evaluated for compliance against an AWS Config rule. Rule evaluations can be run in detective mode and/or in proactive mode, if available. If you are running a rule in both detective mode and proactive mode, you will be charged for only the evaluations in detective mode. For more information, see AWS Config pricing.

With this new feature, you can use AWS Config to check your rules before provisioning and avoid implementing your own custom validations.

Danilo

New for AWS Control Tower – Comprehensive Controls Management (Preview)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-control-tower-comprehensive-controls-management-preview/

Today, customers in regulated industries face the challenge of defining and enforcing controls needed to meet compliance and security requirements while empowering engineers to make their design choices. In addition to addressing risk, reliability, performance, and resiliency requirements, organizations may also need to comply with frameworks and standards such as PCI DSS and NIST 800-53.

Building controls that account for service relationships and their dependencies is time-consuming and expensive. Sometimes customers restrict engineering access to AWS services and features until their cloud architects identify risks and implement their own controls.

To make that easier, today we are launching comprehensive controls management in AWS Control Tower. You can use it to apply managed preventative, detective, and proactive controls to accounts and organizational units (OUs) by service, control objective, or compliance framework. AWS Control Tower does the mapping between them on your behalf, saving time and effort.

With this new capability, you can now also use AWS Control Tower to turn on AWS Security Hub detective controls across all accounts in an OU. In this way, Security Hub controls are enabled in every AWS Region that AWS Control Tower governs.

Let’s see how this works in practice.

Using AWS Control Tower Comprehensive Controls Management
In the AWS Control Tower console, there is a new Controls library section. There, I choose All controls. There are now more than three hundred controls available. For each control, I see which AWS service it is related to, the control objective this control is part of, the implementation (such as AWS Config rule or AWS CloudFormation Guard rule), the behavior (preventive, detective, or proactive), and the frameworks it maps to (such as NIST 800-53 or PCI DSS).

Console screenshot.

In the Find controls search box, I look for a preventive control called CT.CLOUDFORMATION.PR.1. This control uses a service control policy (SCP) to protect controls that use CloudFormation hooks and is required by the control that I want to turn on next. Then, I choose Enable control.

Console screenshot.

Then, I select the OU for which I want to enable this control.

Console screenshot.

Now that I have set up this control, let’s see how controls are presented in the console in categories. I choose Categories in the navigation pane. There, I can browse controls grouped as Frameworks, Services, and Control objectives. By default, the Frameworks tab is selected.

Console screenshot.

I select a framework (for example, PCI DSS version 3.2.1) to see all the related controls and control objectives. To implement a control, I can select the control from the list and choose Enable control.

Console screenshot.

I can also manage controls by AWS service. When I select the Services tab, I see a list of AWS services and the related control objectives and controls.

Console screenshot.

I choose Amazon DynamoDB to see the controls that I can turn on for this service.

Console screenshot.

I select the Control objectives tab. When I need to assess a control objective, this is where I have access to the list of related controls to turn on.

Console screenshot.

I choose Encrypt data at rest to see and search through the available controls for that control objective. I can also check which services are covered in this specific case. I type RDS in the search bar to find the controls related to Amazon Relational Database Service (RDS) for this control objective.

I choose CT.RDS.PR.16 – Require an Amazon RDS database cluster to have encryption at rest configured and then Enable control.

Console screenshot.

I select the OU for which I want to enable the control for, and I proceed. All the AWS accounts in this organization’s OU will have this control enabled in all the Regions that AWS Control Tower governs.

Console screenshot.

After a few minutes, the AWS Control Tower setup is updated. Now, the accounts in this OU have proactive control CT.RDS.PR.16 turned on. When an account in this OU deploys a CloudFormation stack, any Amazon RDS database cluster has to have encryption at rest configured. Because this control is proactive, it’ll be checked by a CloudFormation hook before the deployment starts. This saves a lot of time compared to a detective control that would find the issue only when the CloudFormation deployment is in progress or has terminated. This also improves my security posture by preventing something that’s not allowed as opposed to reacting to it after the fact.

Availability and Pricing
Comprehensive controls management is available in preview today in all AWS Regions where AWS Control Tower is offered. These enhanced control capabilities reduce the time it takes you to vet AWS services from months or weeks to minutes. They help you use AWS by undertaking the heavy burden of defining, mapping, and managing the controls required to meet the most common control objectives and regulations.

There is no additional charge to use these new capabilities during the preview. However, when you set up AWS Control Tower, you will begin to incur costs for AWS services configured to set up your landing zone and mandatory controls. For more information, see AWS Control Tower pricing.

Simplify how you implement compliance and security requirements with AWS Control Tower.

Danilo

New – Amazon CloudWatch Cross-Account Observability

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-amazon-cloudwatch-cross-account-observability/

Deploying applications using multiple AWS accounts is a good practice to establish security and billing boundaries between teams and reduce the impact of operational events. When you adopt a multi-account strategy, you have to analyze telemetry data that is scattered across several accounts. To give you the flexibility to monitor all the components of your applications from a centralized view, we are introducing today Amazon CloudWatch cross-account observability, a new capability to search, analyze, and correlate cross-account telemetry data stored in CloudWatch such as metrics, logs, and traces.

You can now set up a central monitoring AWS account and connect your other accounts as sources. Then, you can search, audit, and analyze logs across your applications to drill down into operational issues in a matter of seconds. You can discover and visualize metrics from many accounts in a single place and create alarms that evaluate metrics belonging to other accounts. You can start with an aggregated cross-account view of your application to visually identify the resources exhibiting errors and dive deep into correlated traces, metrics, and logs to find the root cause. This seamless cross-account data access and navigation helps reduce the time and effort required to troubleshoot issues.

Let’s see how this works in practice.

Configuring CloudWatch Cross-Account Observability
To enable cross-account observability, CloudWatch has introduced the concept of monitoring and source accounts:

  • A monitoring account is a central AWS account that can view and interact with observability data shared by other accounts.
  • A source account is an individual AWS account that shares observability data and resources with one or more monitoring accounts.

You can configure multiple monitoring accounts with the level of visibility you need. CloudWatch cross-account observability is also integrated with AWS Organizations. For example, I can have a monitoring account with wide access to all accounts in my organization for central security and operational teams and then configure other monitoring accounts with more restricted visibility across a business unit for individual service owners.

First, I configure the monitoring account. In the CloudWatch console, I choose Settings in the navigation pane. In the Monitoring account configuration section, I choose Configure.

Console screenshot.

Now I can choose which telemetry data can be shared with the monitoring account: Logs, Metrics, and Traces. I leave all three enabled.

Console screenshot.

To list the source accounts that will share data with this monitoring account, I can use account IDs, organization IDs, or organization paths. I can use an organization ID to include all the accounts in the organization or an organization path to include all the accounts in a department or business unit. In my case, I have only one source account to link, so I enter the account ID.

Console screenshot.

When using the CloudWatch console in the monitoring account to search and display telemetry data, I see the account ID that shared that data. Because account IDs are not easy to remember, I can display a more descriptive “account label.” When configuring the label via the console, I can choose between the account name or the email address used to identify the account. When using an email address, I can also choose whether to include the domain. For example, if all the emails used to identify my accounts are using the same domain, I can use as labels the email addresses without that domain.

There is a quick reminder that cross-account observability only works in the selected Region. If I have resources in multiple Regions, I can configure cross-account observability in each Region. To complete the configuration of the monitoring account, I choose Configure.

Console screenshot.

The monitoring account is now enabled, and I choose Resources to link accounts to determine how to link my source accounts.

Console screenshot.

To link source accounts in an AWS organization, I can download an AWS CloudFormation template to be deployed in a CloudFormation delegated administration account.

To link individual accounts, I can either download a CloudFormation template to be deployed in each account or copy a URL that helps me use the console to set up the accounts. I copy the URL and paste it into another browser where I am signed in as the source account. Then, I can configure which telemetry data to share (logs, metrics, or traces). The Amazon Resource Name (ARN) of the monitoring account configuration is pre-filled because I copy-pasted the URL in the previous step. If I don’t use the URL, I can copy the ARN from the monitoring account and paste it here. I confirm the label used to identify my source account and choose Link.

In the Confirm monitoring account permission dialog, I type Confirm to complete the configuration of the source account.

Using CloudWatch Cross-Account Observability
To see how things work with cross-account observability, I deploy a simple cross-account application using two AWS Lambda functions, one in the source account (multi-account-function-a) and one in the monitoring account (multi-account-function-b). When triggered, the function in the source account publishes an event to an Amazon EventBridge event bus in the monitoring account. There, an EventBridge rule triggers the execution of the function in the monitoring account. This is a simplified setup using only two accounts. You’d probably have your workloads running in multiple source accounts.Architectural diagram.

In the Lambda console, the two Lambda functions have Active tracing and Enhanced monitoring enabled. To collect telemetry data, I use the AWS Distro for OpenTelemetry (ADOT) Lambda layer. The Enhanced monitoring option turns on Amazon CloudWatch Lambda Insights to collect and aggregate Lambda function runtime performance metrics.

Console screenshot.

I prepare a test event in the Lambda console of the source account. Then, I choose Test and run the function a few times.

Console screenshot.

Now, I want to understand what the components of my application, running in different accounts, are doing. I start with logs and then move to metrics and traces.

In the CloudWatch console of the monitoring account, I choose Log groups in the Logs section of the navigation pane. There, I search for and find the log groups created by the two Lambda functions running in different AWS accounts. As expected, each log group shows the account ID and label originating the data. I select both log groups and choose View in Logs Insights.

Console screenshot.

I can now search and analyze logs from different AWS accounts using the CloudWatch Logs Insights query syntax. For example, I run a simple query to see the last twenty messages in the two log groups. I include the @log field to see the account ID that the log belongs to.

Console screenshot.

I can now also create Contributor Insights rules on cross-account log groups. This enables me, for example, to have a holistic view of what security events are happening across accounts or identify the most expensive Lambda requests in a serverless application running in multiple accounts.

Then, I choose All metrics in the Metrics section of the navigation pane. To see the Lambda function runtime performance metrics collected by CloudWatch Lambda Insights, I choose LambdaInsights and then function_name. There, I search for multi-account and memory to see the memory metrics. Again, I see the account IDs and labels that tell me that these metrics are coming from two different accounts. From here, I can just select the metrics I am interested in and create cross-account dashboards and alarms. With the metrics selected, I choose Add to dashboard in the Actions dropdown.

Console screenshot.

I create a new dashboard and choose the Stacked area widget type. Then, I choose Add to dashboard.

Console screenshot.

I do the same for the CPU and memory metrics (but using different widget types) to quickly create a cross-account dashboard where I can keep under control my multi-account setup. Well, there isn’t a lot of traffic yet but I am hopeful.

Console screenshot.

Finally, I choose Service map from the X-Ray traces section of the navigation pane to see the flow of my multi-account application. In the service map, the client triggers the Lambda function in the source account. Then, an event is sent to the other account to run the other Lambda function.

Console screenshot.

In the service map, I select the gear icon for the function running in the source account (multi-account-function-a) and then View traces to look at the individual traces. The traces contain data from multiple AWS accounts. I can search for traces coming from a specific account using a syntax such as:

service(id(account.id: "123412341234"))

Console screenshot.

The service map now stitches together telemetry from multiple accounts in a single place, delivering a consolidated view to monitor their cross-account applications. This helps me to pinpoint issues quickly and reduces resolution time.

Availability and Pricing
Amazon CloudWatch cross-account observability is available today in all commercial AWS Regions using the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. AWS CloudFormation support is coming in the next few days. Cross-account observability in CloudWatch comes with no extra cost for logs and metrics, and the first trace copy is free. See the Amazon CloudWatch pricing page for details.

Having a central point of view to monitor all the AWS accounts that you use gives you a better understanding of your overall activities and helps solve issues for applications that span multiple accounts.

Start using CloudWatch cross-account observability to monitor all your resources.

Danilo

New – Amazon Redshift Support in AWS Backup

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-amazon-redshift-support-in-aws-backup/

With Amazon Redshift, you can analyze data in the cloud at any scale. Amazon Redshift offers native data protection capabilities to protect your data using automatic and manual snapshots. This works great by itself, but when you’re using other AWS services, you have to configure more than one tool to manage your data protection policies.

To make this easier, I am happy to share that we added support for Amazon Redshift in AWS Backup. AWS Backup allows you to define a central backup policy to manage data protection of your applications and can now also protect your Amazon Redshift clusters. In this way, you have a consistent experience when managing data protection across all supported services. If you have a multi-account setup, the centralized policies in AWS Backup let you define your data protection policies across all your accounts within your AWS Organizations. To help you meet your regulatory compliance needs, AWS Backup now includes Amazon Redshift in its auditor-ready reports. You also have the option to use AWS Backup Vault Lock to have immutable backups and prevent malicious or inadvertent changes.

Let’s see how this works in practice.

Using AWS Backup with Amazon Redshift
The first step is to turn on the Redshift resource type for AWS Backup. In the AWS Backup console, I choose Settings in the navigation pane and then, in the Service opt-in section, Configure resources. There, I toggle the Redshift resource type on and choose Confirm.

Console screenshot.

Now, I can create or update a backup plan to include the backup of all, or some, of my Redshift clusters. In the backup plan, I can define how often these backups should be taken and for how long they should be kept. For example, I can have daily backups with one week of retention, weekly backups with one month of retention, and monthly backups with one year of retention.

I can also create on-demand backups. Let’s see this with more details. I choose Protected resources in the navigation pane and then Create on-demand backup.

I select Redshift in the Resource type dropdown. In the Cluster identifier, I select one of my clusters. For this workload, I need two weeks of retention. Then, I choose Create on-demand backup.

Console screenshot.

My data warehouse is not huge, so after a few minutes, the backup job has completed.

Console screenshot.

I now see my Redshift cluster in the list of the resources protected by AWS Backup.

Console screenshot.

In the Protected resources list, I choose the Redshift cluster to see the list of the available recovery points.

Console screenshot.

When I choose one of the recovery points, I have the option to restore the full data warehouse or just a table into a new Redshift cluster.

Console screenshot.

I now have the possibility to edit the cluster and database configuration, including security and networking settings. I just update the cluster identifier, otherwise the restore would fail because it must be unique. Then, I choose Restore backup to start the restore job.

After some time, the restore job has completed, and I see the old and the new clusters in the Amazon Redshift console. Using AWS Backup gives me a simple centralized way to manage data protection for Redshift clusters as well as many other resources in my AWS accounts.

Console screenshot.

Availability and Pricing
Amazon Redshift support in AWS Backup is available today in the AWS Regions where both AWS Backup and Amazon Redshift are offered, with the exception of the Regions based in China. You can use this capability via the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs.

There is no additional cost for using AWS Backup compared to the native snapshot capability of Amazon Redshift. Your overall costs depend on the amount of storage and retention you need. For more information, see AWS Backup pricing.

Danilo

New for AWS Backup – Protect and Restore Your CloudFormation Stacks

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-backup-protect-and-restore-your-cloudformation-stacks/

To define the data protection policy of an application, you have to look at its components and find which ones store data that needs to be protected. Those are the stateful components of your application, such as databases and file systems. Other components don’t store data but need to be restored as well in case of issues. These are stateless components, such as containers and their network configurations.

When you manage your application using infrastructure as code (IaC), you have a single repository where all these components are described. Can we use this information to help protect your applications? Yes! AWS Backup now supports attaching an AWS CloudFormation stack to your data protection policies.

When you use CloudFormation as a resource, all stateful components supported by AWS Backup are backed up around the same time. The backup also includes the stateless resources in the stack, such as AWS Identity and Access Management (IAM) roles and Amazon Virtual Private Cloud (Amazon VPC) security groups. This gives you a single recovery point that you can use to recover the application stack or the individual resources you need. In case of recovery, you don’t need to mix automated tools with custom scripts and manual activities to recover and put the whole application stack back together. As you modernize and update an application managed with CloudFormation, AWS Backup automatically keeps track of changes and updates the data protection policies for you.

CloudFormation support for AWS Backup also helps you prove compliance of your data protection policies. You can monitor your application resources in AWS Backup Audit Manager, a feature of AWS Backup that enables you to audit and report on the compliance of data protection policies. You can also use AWS Backup Vault Lock to manage the immutability of your backups as required by your compliance obligations.

Let’s see how this works in practice.

Using AWS Backup Support for CloudFormation Stacks
First, I need to turn on the CloudFormation resource type for AWS Backup. In the AWS Backup console, I choose Settings in the navigation pane and then, in the Service opt-in section, Configure resources. There, I toggle the CloudFormation resource type on and choose Confirm.

Console screenshot.

Now that CloudFormation support is enabled, I choose Dashboard in the navigation pane and then Create backup plan. I select the Start with a template option and then the Daily-35day-Retention template. As the name suggests, this template creates daily backups that are kept for 35 days before being automatically deleted. I enter a name for the backup plan and choose Create plan.

Console screenshot.

Now I can assign resources to my backup plan. I enter a resource assignment name and use the default IAM role that is automatically created with the correct permissions.

Console screenshot.

In the Resource selection, I can select Include all resource types to automatically protect all resource types that are enabled in my account. Because I’d like to show how CloudFormation support works, I select Include specific resource types and then CloudFormation in the Select resource types dropdown menu. In the Choose resources menu, I can use the All supported CloudFormation stacks option to have all my stacks protected. For simplicity, I choose to protect only one stack, the my-app stack.

Console screenshot.

I leave the other options at their default values and choose Assign resources. That’s all! Now the CloudFormation stack that I selected will be backed up daily with 35 days of retention. What does that mean? Let’s have a look at what happens when I create an on-demand backup of a CloudFormation stack.

Creating On-Demand Backups for CloudFormation Stacks
I choose Protected resources in the navigation pane and then Create on-demand backup. The next steps are similar to what I did before when assigning resources to a backup plan. I select the CloudFormation resource type and the my-app stack. I use the Create backup now option to start the backup within one hour. I choose 7 days of retention and the Default backup vault. Backup vaults are logical containers that store and organize your backups. I select the default IAM role and choose Create on-demand backup.

Console screenshot.

Within a few minutes, the backup job is running. I expand the Backup job ID in the Backup jobs list to see the resources being backed up. The stateful resources (such as Amazon DynamoDB tables and Amazon Relational Database Service (RDS) databases) are listed with the current state of the backup job. The stateless resources in my stack (such as IAM roles, AWS Lambda functions, and VPC configurations) are backed up by the job with the CloudFormation resource type.

Console screenshot.

When the backup job has completed, I go back to the Protected resources page to see the list of resources that I can now restore. In the list, I see the IDs of the stateful resources (in this case, two DynamoDB tables and an Aurora database) and of the CloudFormation stack. If I choose each of the stateful resources, I see the available recovery points corresponding to the different points in time when that resource has been backed up.

Console screenshot.

If I choose the CloudFormation stack, I get a list of composite recovery points. Each composite recovery point includes all stateless and stateful resources in the stack. More specifically, the stateless resources are included in the CloudFormation template recovery point (the last one in the following screenshot).

Console screenshot.

Restoring a CloudFormation Backup
Inside the composite recovery point, I select the recovery point of the CloudFormation stack and choose Restore. Restoring a CloudFormation stack backup creates a new stack with a change set that represents the backup. I enter the new stack and change set names and choose Restore backup. After a few minutes, the restore job is completed.

In the CloudFormation console, the new stack is under review. I need to apply the change set.

Console screenshot.

I choose the new stack and select the change set created by the restore job to apply the change set.

Console screenshot.

After some time, the resources in my original stack have been recreated in the new stack. The stateful resources have been recreated empty. To recover the stateful resources, I can go back to the list of recovery points, select the recovery point I need, and initiate a restore.

Availability and Pricing
AWS Backup support for CloudFormation stacks is available today using the console, AWS Command Line Interface (CLI), and AWS SDKs in all AWS Regions where AWS Backup is offered. There is no additional cost for the stateless resources backed up and restored by AWS Backup. You only pay for the stateful resources such as databases, storage volumes, or file systems. For more information, see AWS Backup pricing.

You now have an automated solution to create and restore your applications with a simplified experience, eliminating the need to manage custom scripts.

Danilo

New for Amazon Transcribe – Real-Time Analytics During Live Calls

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-amazon-transcribe-real-time-analytics-during-live-calls/

The experience customers have when interacting with a contact center can have a profound impact on them. For this reason, we launched Amazon Transcribe Call Analytics last year to help you analyze customer call recordings and get insights into issues and trends related to customer satisfaction and agent performance.

To assist agents in resolving live calls faster, we are introducing today real-time call analytics in Amazon Transcribe Call Analytics. Real-time call analytics provides APIs for developers to accurately transcribe live calls and at the same time identify customer experience issues and sentiment in real time. Transcribe Call Analytics uses state-of-the-art machine learning capabilities to automatically assess thousands of in-progress calls and detect customer experience issues, such as repeated requests to speak to a manager or cancel a subscription.

With a few clicks, supervisors and analysts can create categories in the AWS console to identify customer experience issues using criteria such as specific terms such as “not happy,” “poor quality,” and “cancel my subscription.” Transcribe Call Analytics analyzes in-progress calls in real time to detect when a category is met. Developers can use those signals, along with sentiment trends from the API, to build a proactive system that alerts supervisors about emerging issues or assists agents with relevant information to solve customer issues.

Transcribe Call Analytics also provides a real-time transcript of the live conversation that supervisors can use to quickly get up to speed on the customer interaction and assess the appropriate action. The in-call transcript also eliminates the need for customers to repeat themselves if the call is transferred to another agent. Agents can focus all their attention on the customer during the call instead of taking notes for entry in a CRM system because Transcribe Call Analytics includes an automated call summarization capability, which identifies the issue, outcome, and action item associated with a call.

Transcribe Call Analytics is a foundational API for AWS Contact Center Intelligence solutions such as post-call analytics and the updated real-time call analytics with agent assist solution using the new real-time capabilities.

Let’s see how this works in practice.

Exploring Real-Time Call Analytics in the Console
To see how this works visually, I use the Amazon Transcribe console. First, I create a category to be notified if some terms are used in the call that would require an escalation. I choose Category Management from the navigation pane and then Create category.

I enter Escalation as the name for the category. I select REAL_TIME in the Category type dropdown. Then, I choose Create from scratch.

Console screenshot.

I only need one rule for this category. In the Rule type dropdown, I select Transcript content match. In the next three options, I choose to trigger the rule when any of the words are mentioned during the entire call, and the speaker is either the customer or the agent. Now, I can enter the words or phrases to look for in the transcript. In this case, I enter cancel, canceled, cancelled, manager, and supervisor. In your case, you might be more specific depending on your business. For example, if subscriptions are your business, you can look for the phrase cancel my subscription.

Console screenshot.

Now that the category has been created, I use one of the sample calls in the console to test it. I choose Real-Time Analytics in the navigation pane. By choosing Configure advanced settings, I can configure the personally identifiable information (PII) identification and redaction settings. For example, I can choose to identify personal data such as email addresses or redact financial data like bank account numbers.

With no additional charge, I can enable Post-call Analytics so that, at the end of the call, I receive the output of the transcription job in an Amazon Simple Storage Service (Amazon S3) bucket. This output is in a similar format to what I’d receive if I were analyzing a call recording with Transcribe Call Analytics. In this way, I can use the post-call analytics output derived from the audio stream in any process I already have in place for output of analytics generated from call recordings, for example, to update dashboards or generate periodic reports.

With Insurance complaints in Step 1: Specify input audio selected, I choose Start streaming. In the Transcription output section of the console, I receive in real-time the transcription of the call. The words of the customer and agent appear as they are pronounced. Each sentence is flagged with its recognized sentiment (positive, neutral, or negative). The Escalation category that I just configured is found in two sentences, first, when the customer mentions that their insurance has been canceled, and then when the agent mentions their manager. Also, part of a sentence is underlined because an issue has been detected.

Console screenshot.

Using the Download dropdown, I download the full JSON transcript. If I am only interested in the transcription, I can download the text transcript. The JSON transcript contains an array where each item is similar to what I’d get in real time when using the real-time call analytics API.

Using the Live Call Analytics With Agent Assist (LCA) Solution
You can use the open-source real-time call analytics with agent assist solution for your contact center or as an inspiration of what Amazon Transcribe enables for developers. Let’s look at a couple of screenshots to understand how it works.

Here there is a list of on-going calls with the overall sentiment, the sentiment trend (is it improving or not?), and the categories found in real-time during the call that can be used for specific activities.

Screenshot from the real-time call analytics with agent assist solution.

When selecting a call from the list, you have access to more in-depth information, including the call transcript and the issues found during the on-going call. This allows to take action quickly to help resolve the call.

Screenshot from the real-time call analytics with agent assist solution.

Availability and Pricing
Amazon Transcribe Call Analytics with real-time capabilities is available today in US (N. Virginia, Oregon), Canada (Central), Europe (Frankfurt, London), and Asia Pacific (Seoul, Sydney, Tokyo) and supports US English, British English, Australian English, US Spanish, Canadian French, French, German, Italian, and Brazilian Portuguese.

With Amazon Transcribe Call Analytics, you pay as you go and are billed monthly based on tiered pricing. For more information, see Amazon Transcribe pricing.

As part of the AWS Free Tier, you can get started with Amazon Transcribe Call Analytics for free, including the new real-time call analytics API. You can analyze up to 60 minutes of call audio monthly for free for the first 12 months. For more information, see the AWS Free Tier page.

If you’re at re:Invent, you can learn more about this new capability in session AIM307 – JPMorganChase real-time agent assist for contact center productivity. I will update this post when the recording of the session is publicly available.

Start analyzing contact center conversations in real-time to improve your customers’ experience.

Danilo

AWS Week in Review – November 21, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-november-21-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

A new week starts, and the News Blog team is getting ready for AWS re:Invent! Many of us will be there next week and it would be great to meet in person. If you’re coming, do you know about PeerTalk? It’s an onsite networking program for re:Invent attendees available through the AWS Events mobile app (which you can get on Google Play or Apple App Store) to help facilitate connections among the re:Invent community.

If you’re not coming to re:Invent, no worries, you can get a free online pass to watch keynotes and leadership sessions.

Last Week’s Launches
It was a busy week for our service teams! Here are the launches that got my attention:

AWS Region in Spain – The AWS Region in Aragón, Spain, is now open. The official name is Europe (Spain), and the API name is eu-south-2.

Amazon Athena – You can now apply AWS Lake Formation fine-grained access control policies with all table and file format supported by Amazon Athena to centrally manage permissions and access data catalog resources in your Amazon Simple Storage Service (Amazon S3) data lake. With fine-grained access control, you can restrict access to data in query results using data filters to achieve column-level, row-level, and cell-level security.

Amazon EventBridge – With these additional filtering capabilities, you can now filter events by suffix, ignore case, and match if at least one condition is true. This makes it easier to write complex rules when building event-driven applications.

AWS Controllers for Kubernetes (ACK) – The ACK for Amazon Elastic Compute Cloud (Amazon EC2) is now generally available and lets you provision and manage EC2 networking resources, such as VPCs, security groups and internet gateways using the Kubernetes API. Also, the ACK for Amazon EMR on EKS is now generally available to allow you to declaratively define and manage EMR on EKS resources such as virtual clusters and job runs as Kubernetes custom resources. Learn more about ACK for Amazon EMR on EKS in this blog post.

Amazon HealthLake – New analytics capabilities make it easier to query, visualize, and build machine learning (ML) models. Now HealthLake transforms customer data into an analytics-ready format in near real-time so that you can query, and use the resulting data to build visualizations or ML models. Also new is Amazon HealthLake Imaging (preview), a new HIPAA-eligible capability that enables you to easily store, access, and analyze medical images at any scale. More on HealthLake Imaging can be found in this blog post.

Amazon RDS – You can now transfer files between Amazon Relational Database Service (RDS) for Oracle and an Amazon Elastic File System (Amazon EFS) file system. You can use this integration to stage files like Oracle Data Pump export files when you import them. You can also use EFS to share a file system between an application and one or more RDS Oracle DB instances to address specific application needs.

Amazon ECS and Amazon EKS – We added centralized logging support for Windows containers to help you easily process and forward container logs to various AWS and third-party destinations such as Amazon CloudWatch, S3, Amazon Kinesis Data Firehose, Datadog, and Splunk. See these blog posts for how to use this new capability with ECS and with EKS.

AWS SAM CLI – You can now use the Serverless Application Model CLI to locally test and debug an AWS Lambda function defined in a Terraform application. You can see a walkthrough in this blog post.

AWS Lambda – Now supports Node.js 18 as both a managed runtime and a container base image, which you can learn more about in this blog post. Also check out this interesting article on why and how you should use AWS SDK for JavaScript V3 with Node.js 18. And last but not least, there is new tooling support to build and deploy native AOT compiled .NET 7 applications to AWS Lambda. With this tooling, you can enable faster application starts and benefit from reduced costs through the faster initialization times and lower memory consumption of native AOT applications. Learn more in this blog post.

AWS Step Functions – Now supports cross-account access for more than 220 AWS services to process data, automate IT and business processes, and build applications across multiple accounts. Learn more in this blog post.

AWS Fargate – Adds the ability to monitor the utilization of the ephemeral storage attached to an Amazon ECS task. You can track the storage utilization with Amazon CloudWatch Container Insights and ECS Task Metadata endpoint.

AWS Proton – Now has a centralized dashboard for all resources deployed and managed by AWS Proton, which you can learn more about in this blog post. You can now also specify custom commands to provision infrastructure from templates. In this way, you can manage templates defined using the AWS Cloud Development Kit (AWS CDK) and other templating and provisioning tools. More on CDK support and AWS CodeBuild provisioning can be found in this blog post.

AWS IAM – You can now use more than one multi-factor authentication (MFA) device for root account users and IAM users in your AWS accounts. More information is available in this post.

Amazon ElastiCache – You can now use IAM authentication to access Redis clusters. With this new capability, IAM users and roles can be associated with ElastiCache for Redis users to manage their cluster access.

Amazon WorkSpaces – You can now use version 2.0 of the WorkSpaces Streaming Protocol (WSP) host agent that offers significant streaming quality and performance improvements, and you can learn more in this blog post. Also, with Amazon WorkSpaces Multi-Region Resilience, you can implement business continuity solutions that keep users online and productive with less than 30-minute recovery time objective (RTO) in another AWS Region during disruptive events. More on multi-region resilience is available in this post.

Amazon CloudWatch RUM – You can now send custom events (in addition to predefined events) for better troubleshooting and application specific monitoring. In this way, you can monitor specific functions of your application and troubleshoot end user impacting issues unique to the application components.

AWS AppSync – You can now define GraphQL API resolvers using JavaScript. You can also mix functions written in JavaScript and Velocity Template Language (VTL) inside a single pipeline resolver. To simplify local development of resolvers, AppSync released two new NPM libraries and a new API command. More info can be found in this blog post.

AWS SDK for SAP ABAP – This new SDK makes it easier for ABAP developers to modernize and transform SAP-based business processes and connect to AWS services natively using the SAP ABAP language. Learn more in this blog post.

AWS CloudFormation – CloudFormation can now send event notifications via Amazon EventBridge when you create, update, or delete a stack set.

AWS Console – With the new Applications widget on the Console home, you have one-click access to applications in AWS Systems Manager Application Manager and their resources, code, and related data. From Application Manager, you can view the resources that power your application and your costs using AWS Cost Explorer.

AWS Amplify – Expands Flutter support (developer preview) to Web and Desktop for the API, Analytics, and Storage use cases. You can now build cross-platform Flutter apps with Amplify that target iOS, Android, Web, and Desktop (macOS, Windows, Linux) using a single codebase. Learn more on Flutter Web and Desktop support for AWS Amplify in this post. Amplify Hosting now supports fully managed CI/CD deployments and hosting for server-side rendered (SSR) apps built using Next.js 12 and 13. Learn more in this blog post and see how to deploy a NextJS 13 app with the AWS CDK here.

Amazon SQS – With attribute-based access control (ABAC), you can define permissions based on tags attached to users and AWS resources. With this release, you can now use tags to configure access permissions and policies for SQS queues. More details can be found in this blog.

AWS Well-Architected Framework – The latest version of the Data Analytics Lens is now available. The Data Analytics Lens is a collection of design principles, best practices, and prescriptive guidance to help you running analytics on AWS.

AWS Organizations – You can now manage accounts, organizational units (OUs), and policies within your organization using CloudFormation templates.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more stuff you might have missed:

Introducing our final AWS Heroes of the year – As the end of 2022 approaches, we are recognizing individuals whose enthusiasm for knowledge-sharing has a real impact with the AWS community. Please meet them here!

The Distributed Computing ManifestoWerner Vogles, VP & CTO at Amazon.com, shared the Distributed Computing Manifesto, a canonical document from the early days of Amazon that transformed the way we built architectures and highlights the challenges faced at the end of the 20th century.

AWS re:Post – To make this community more accessible globally, we expanded the user experience to support five additional languages. You can now interact with AWS re:Post also using Traditional Chinese, Simplified Chinese, French, Japanese, and Korean.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
As usual, there are many opportunities to meet:

AWS re:Invent – Our yearly event is next week from November 28 to December 2. If you can’t be there in person, get your free online pass to watch live the keynotes and the leadership sessions.

AWS Community DaysAWS Community Day events are community-led conferences to share and learn together. Join us in Sri Lanka (on December 6-7), Dubai, UAE (December 10), Pune, India (December 10), and Ahmedabad, India (December 17).

That’s all from me for this week. Next week we’ll focus on re:Invent, and then we’ll take a short break. We’ll be back with the next Week in Review on December 12!

Danilo

Introducing AWS Resource Explorer – Quickly Find Resources in Your AWS Account

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-aws-resource-explorer-quickly-find-resources-in-your-aws-account/

Looking for a specific Amazon Elastic Compute Cloud (Amazon EC2) instance, Amazon Elastic Container Service (Amazon ECS) task, or Amazon CloudWatch log group can take some time, especially if you have many resources and use multiple AWS Regions.

Today, we’re making that easier. Using the new AWS Resource Explorer, you can search through the AWS resources in your account across Regions using metadata such as names, tags, and IDs. When you find a resource in the AWS Management Console, you can quickly go from the search results to the corresponding service console and Region to start working on that resource. In a similar way, you can use the AWS Command Line Interface (CLI) or any of the AWS SDKs to find resources in your automation tools.

Let’s see how this works in practice.

Using AWS Resource Explorer
To start using Resource Explorer, I need to turn it on so that it creates and maintains the indexes that will provide fast responses to my search queries. Usually, the administrator of the account is the one taking these steps so that authorized users in that account can start searching.

To run a query, I need a view that gives access to an index. If the view is using an aggregator index, then the query can search across all indexed Regions.

Aggregator index diagram.

If the view is using a local index, then the query has access only to the resources in that Region.

Local index diagram.

I can control the visibility of resources in my account by creating views that define what resource information is available for search and discovery. These controls are not based only on resources but also on the information that resources bring. For example, I can give access to the Amazon Resource Names (ARNs) of all resources but not to their tags which might contain information that I want to keep confidential.

In the Resource Explorer console, I choose Enable Resource Explorer. Then, I select the Quick setup option to have visibility for all supported resources within my account. This option creates local indexes in all Regions and an aggregator index in the selected Region. A default view with a filter that includes all supported resources in the account is also created in the same Region as the aggregator index.

Console screenshot.

With the Advanced setup option, I have access to more granular controls that are useful when there are specific governance requirements. For example, I can select in which Regions to create indexes. I can choose not to replicate resource information to any other Region so that resources from each AWS Region are searchable only from within the same Region. I can also control what information is available in the default view or avoid the creation of the default view.

With the Quick setup option selected, I choose Go to Resource Explorer. A quick overview shows the progress of enabling Resource Explorer across Regions. After the indexes have been created, it can take up to 36 hours to index all supported resources, and search results might be incomplete until then. When resources are created or deleted, your indexes are automatically updated. These updates are asynchronous, so it can take some time (usually a few minutes) to see the changes.

Searching With AWS Resource Explorer
After resources have been indexed, I choose Proceed to resource search. In the Search criteria, I choose which View to use. Currently, I have the default view selected. Then, I start typing in the Query field to search through the resources in my AWS account across all Regions. For example, I have an application where I used the convention to start resource names with my-app. For the resources I created manually, I also added the Project tag with value MyApp.

To find the resource of this application, I start by searching for my-app.

Console screenshot.

The results include resources from multiple services and Regions and global resources from AWS Identity and Access Management (IAM). I have a service, tasks, and a task definition from Amazon ECS, roles and policies from AWS IAM, log groups from CloudWatch. Optionally, I can filter results by Region or resource type. If I choose any of the listed resources, the link will bring me to the corresponding service console and Region with the resource selected.

Console screenshot.

To look for something in a specific Region, such as Europe (Ireland), I can restrict the results by adding region:eu-west-1 to the query.

Console screenshot.

I can further restrict results to Amazon ECS resources by adding service:ecs to the query. Now I only see the ECS cluster, service, tasks, and task definition in Europe (Ireland). That’s the task definition I was looking for!

Console screenshot.

I can also search using tags. For example, I can see the resources where I added the MyApp tag by including tag.value:MyApp in a query. To specify the actual key-value pair of the tag, I can use tag:Project=MyApp.

Console screenshot.

Creating a Custom View
Sometimes you need to control the visibility of the resources in your account. For example, all the EC2 instances used for development in my account are in US West (Oregon). I create a view for the development team by choosing a specific Region (us-west-2) and filtering the results with service:ec2 in the query. Optionally, I could further filter results based on resource names or tags. For example, I could add tag:Environment=Dev to only see resources that have been tagged to be in a development environment.

Console screenshot.

Now I allow access to this view to users and roles used by the development team. To do so, I can attach an identity-based policy to the users and roles of the development team. In this way, they can only explore and search resources using this view.

Console screenshot.

Unified Search in the AWS Management Console
After I turn Resource Explorer on, I can also search through my AWS resources in the search bar at the top of the Management Console. We call this capability unified search as it gives results that include AWS services, features, blogs, documentation, tutorial, events, and more.

To focus my search on AWS resources, I add /Resources at the beginning of my search.

Console screenshot.

Note that unified search automatically inserts a wildcard character (*) at the end of the first keyword in the string. This means that unified search results include resources that match any string that starts with the specified keyword.

Console screenshot.

The search performed by the Query text box on the Resource search page in the Resource Explorer console does not automatically append a wildcard character but I can do it manually after any term in the search string to have similar results.

Unified search works when I have the default view in the same Region that contains the aggregator index. To check if unified search works for me, I look at the top of the Settings page.

Console screenshot.

Availability and Pricing
You can start using AWS Resource Explorer today with a global console and via the AWS Command Line Interface (CLI) and the AWS SDKs. AWS Resource Explorer is available at no additional charge. Using Resource Explorer makes it much faster to find the resources you need and use them in your automation processes and in their service console.

Discover and access your AWS resources across all the Regions you use with AWS Resource Explorer.

Danilo

Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-neptune-serverless-a-fully-managed-graph-database-that-adjusts-capacity-for-your-workloads/

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Neptune has always been fully managed and handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection and repair. However, managing database capacity for optimal cost and performance requires you to monitor and reconfigure capacity as workload characteristics change. Also, many applications have variable or unpredictable workloads where the volume and complexity of database queries can change significantly. For example, a knowledge graph application for social media may see a sudden spike in queries due to sudden popularity.

Introducing Amazon Neptune Serverless
Today, we’re making that easier with the launch of Amazon Neptune Serverless. Neptune Serverless scales automatically as your queries and your workloads change, adjusting capacity in fine-grained increments to provide just the right amount of database resources that your application needs. In this way, you pay only for the capacity you use. You can use Neptune Serverless for development, test, and production workloads and optimize your database costs compared to provisioning for peak capacity.

With Neptune Serverless you can quickly and cost-effectively deploy graphs for your modern applications. You can start with a small graph, and as your workload grows, Neptune Serverless will automatically and seamlessly scale your graph databases to provide the performance you need. You no longer need to manage database capacity and you can now run graph applications without the risk of higher costs from over-provisioning or insufficient capacity from under-provisioning.

With Neptune Serverless, you can continue to use the same query languages (Apache TinkerPop Gremlin, openCypher, and RDF/SPARQL) and features (such as snapshots, streams, high availability, and database cloning) already available in Neptune.

Let’s see how this works in practice.

Creating an Amazon Neptune Serverless Database
In the Neptune console, I choose Databases in the navigation pane and then Create database. For Engine type, I select Serverless and enter my-database as the DB cluster identifier.

Console screenshot.

I can now configure the range of capacity, expressed in Neptune capacity units (NCUs), that Neptune Serverless can use based on my workload. I can now choose a template that will configure some of the next options for me. I choose the Production template that by default creates a read replica in a different Availability Zone. The Development and Testing template would optimize my costs by not having a read replica and giving access to DB instances that provide burstable capacity.

Console screenshot.

For Connectivity, I use my default VPC and its default security group.

Console screenshot.

Finally, I choose Create database. After a few minutes, the database is ready to use. In the list of databases, I choose the DB identifier to get the Writer and Reader endpoints that I am going to use later to access the database.

Using Amazon Neptune Serverless
There is no difference in the way you use Neptune Serverless compared to a provisioned Neptune database. I can use any of the query languages supported by Neptune. For this walkthrough, I choose to use openCypher, a declarative query language for property graphs originally developed by Neo4j that was open-sourced in 2015 and contributed to the openCypher project.

To connect to the database, I start an Amazon Linux Amazon Elastic Compute Cloud (Amazon EC2) instance in the same AWS Region and associate the default security group and a second security group that gives me SSH access.

With a property graph I can represent connected data. In this case, I want to create a simple graph that shows how some AWS services are part of a service category and implement common enterprise integration patterns.

I use curl to access the Writer openCypher HTTPS endpoint and create a few nodes that represent patterns, services, and service categories. The following commands are split into multiple lines in order to improve readability.

curl https://<my-writer-endpoint>:8182/openCypher \
-d "query=CREATE (mq:Pattern {name: 'Message Queue'}),
(pubSub:Pattern {name: 'Pub/Sub'}),
(eventBus:Pattern {name: 'Event Bus'}),
(workflow:Pattern {name: 'WorkFlow'}),
(applicationIntegration:ServiceCategory {name: 'Application Integration'}),
(sqs:Service {name: 'Amazon SQS'}), (sns:Service {name: 'Amazon SNS'}),
(eventBridge:Service {name: 'Amazon EventBridge'}), (stepFunctions:Service {name: 'AWS StepFunctions'}),
(sqs)-[:IMPLEMENT]->(mq), (sns)-[:IMPLEMENT]->(pubSub),
(eventBridge)-[:IMPLEMENT]->(eventBus),
(stepFunctions)-[:IMPLEMENT]->(workflow),
(applicationIntegration)-[:CONTAIN]->(sqs),
(applicationIntegration)-[:CONTAIN]->(sns),
(applicationIntegration)-[:CONTAIN]->(eventBridge),
(applicationIntegration)-[:CONTAIN]->(stepFunctions);"

This is a visual representation of the nodes and their relationships for the graph created by the previous command. The type (such as Service or Pattern) and properties (such as name) are shown inside each node. The arrows represent the relationships (such as CONTAIN or IMPLEMENT) between the nodes.

Visualization of graph data.

Now, I query the database to get some insights. To query the database, I can use either a Writer or a Reader endpoint. First, I want to know the name of the service implementing the “Message Queue” pattern. Note how the syntax of openCypher resembles that of SQL with MATCH instead of SELECT.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (s:Service)-[:IMPLEMENT]->(p:Pattern {name: 'Message Queue'}) RETURN s.name;"
{
  "results" : [ {
    "s.name" : "Amazon SQS"
  } ]
}

I use the following query to see how many services are in the “Application Integration” category. This time, I use the WHERE clause to filter results.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (c:ServiceCategory)-[:CONTAIN]->(s:Service) WHERE c.name='Application Integration' RETURN count(s);"
{
  "results" : [ {
    "count(s)" : 4
  } ]
}

There are many options now that I have this graph database up and running. I can add more data (services, categories, patterns) and more relationships between the nodes. I can focus on my application and let Neptune Serverless manage capacity and infrastructure for me.

Availability and Pricing
Amazon Neptune Serverless is available today in the following AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Tokyo), and Europe (Ireland, London).

With Neptune Serverless, you only pay for what you use. The database capacity is adjusted to provide the right amount of resources you need in terms of Neptune capacity units (NCUs). Each NCU is a combination of approximately 2 gibibytes (GiB) of memory with corresponding CPU and networking. The use of NCUs is billed per second. For more information, see the Neptune pricing page.

Having a serverless graph database opens many new possibilities. To learn more, see the Neptune Serverless documentation. Let us know what you build with this new capability!

Simplify the way you work with highly connected data using Neptune Serverless.

Danilo

AWS Week in Review – October 3, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-3-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

A new week and a new month just started. Curious which were the most significant AWS news from the previous seven days? I got you covered with this post.

Last Week’s Launches
Here are the launches that got my attention last week:

Amazon File Cache – A high performance cache on AWS that accelerates and simplifies demanding cloud bursting and hybrid workflows by giving access to files using a fast and familiar POSIX interface, no matter if the original files live on premises on any file system that can be accessed through NFS v3 or on S3.

Amazon Data Lifecycle Manager – You can now automatically archive Amazon EBS snapshots to save up to 75 percent on storage costs for those EBS snapshots that you intend to retain for more than 90 days and rarely access.

AWS App Runner – You can now build and run web applications and APIs from source code using the new Node.js 16 managed runtime.

AWS Copilot – The CLI for containerized apps adds IAM permission boundaries, support for FIFO SNS/SQS for the Copilot worker-service pattern, and using Amazon CloudFront for low-latency content delivery and fast TLS-termination for public load-balanced web services.

Bottlerocket – The Linux-based operating system purpose-built to run container workloads is now supported by Amazon Inspector. Amazon Inspector can now recommend an update of Bottlerocket if it finds a vulnerability.

Amazon SageMaker Canvas – Now supports mathematical functions and operators for richer data exploration and to understand the relationships between variables in your data.

AWS Compute Optimizer – Now provides cost and performance optimization recommendations for 37 new EC2 instance types, including bare metal instances (m6g.metal) and compute optimized instances (c7g.2xlarge, hpc6a.48xlarge), and new memory metrics for Windows instances.

AWS Budgets – Use a simplified 1-click workflow for common budgeting scenarios with step-by-step tutorials on how to use each template.

Amazon Connect – Now provides an updated flow designer UI that makes it easier and faster to build personalized and automated end-customer experiences, as well as a queue dashboard to view and compare real-time queue performance through time series graphs.

Amazon WorkSpaces – You can now provision Ubuntu desktops and use virtual desktops for new categories of workloads, such as for your developers, engineers, and data scientists.

Amazon WorkSpaces Core – A fully managed infrastructure-only solution for third-party Virtual Desktop Infrastructure (VDI) management software that simplifies VDI migration and combines your current VDI software with the security and reliability of AWS. Read more about it in this Desktop and Application Streaming blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Introducing new language extensions in AWS CloudFormation – In this Cloud Operations & Migrations blog post, we introduce the new language transform that enhances CloudFormation core language with intrinsic functions that simplify handling JSON strings (Fn::ToJsonString), array lengths (Fn::Length), and update and deletion policies.

Building a GraphQL API with Java and AWS Lambda – This blog shows different options for resolving GraphQL queries using serverless technologies on AWS.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
As usual, there are many opportunities to meet:

AWS Summits– Connect, collaborate, and learn about AWS at these free in-person events: Bogotá (October 4), and Singapore (October 6).

AWS Community DaysAWS Community Day events are community-led conferences to share and learn together. Join us in Amersfoort, Netherlands (on October 3, today), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

Danilo

AWS Week in Review – September 5, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-5-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

As a new week begins, let’s quickly look back at the most significant AWS news from the previous seven days.

Last Week’s Launches
Here are the launches that got my attention last week:

AWS announces open-sourced credentials-fetcher to simplify Microsoft AD access from Linux containers. You can find more in the What’s New post.

AWS Step Functions now has 14 new intrinsic functions that help you process data more efficiently and make it easier to perform data processing tasks such as array manipulation, JSON object manipulation, and math functions within your workflows without having to invoke downstream services or add Task states.

AWS SAM CLI esbuild support is now generally available. You can now use esbuild in the SAM CLI build workflow for your JavaScript applications.

Amazon QuickSight launches a new user interface for dataset management that replaces the existing popup dialog modal with a full-page experience, providing a clearer breakdown of dataset management categories.

AWS GameKit adds Unity support. With this release for Unity, you can integrate cloud-based game features into Win64, MacOS, Android, or iOS games from both the Unreal and Unity engines with just a few clicks.

AWS and VMware announce VMware Cloud on AWS integration with Amazon FSx for NetApp ONTAP. Read more in Veliswa‘s blog post.

The AWS Region in the United Arab Emirates (UAE) is now open. More info in Marcia‘s blog post.

View of Abu Dhabi in the United Arab Emirates

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Easy analytics and cost-optimization with Amazon Redshift Serverless – Four different use cases of Redshift Serverless are discussed in this post.

Building cost-effective AWS Step Functions workflows – In this blog post, Ben explains the difference between Standard and Express Workflows, including costs, migrating from Standard to Express, and some interesting ways of using both together.

How to subscribe to the new Security Hub Announcements topic for Amazon SNS – You can now receive updates about new Security Hub services and features, newly supported standards and controls, and other Security Hub changes.

Deploying AWS Lambda functions using AWS Controllers for Kubernetes (ACK) – With the ACK service controller for AWS Lambda, you can provision and manage Lambda functions with kubectl and custom resources.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
Depending on where you are on this planet, there are many opportunities to meet and learn:

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Ottawa (September 8), New Delhi (September 9), Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community DaysAWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in the Bay Area, California (September 9) and Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

Danilo

Graviton Fast Start – A New Program to Help Move Your Workloads to AWS Graviton

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/graviton-fast-start-a-new-program-to-help-move-your-workloads-to-aws-graviton/

With the Graviton Challenge last year, we helped customers migrate to Graviton-based EC2 instances and get up to 40 percent price performance benefit in as little as 4 days. Tens of thousands of customers, including 48 of the top 50 Amazon Elastic Compute Cloud (Amazon EC2) customers, use AWS Graviton processors for their workloads. In addition to EC2, many AWS managed services can run their workloads on Graviton. For most customers, adoption is easy, requiring minimal code changes. However, the effort and time required to move workloads to Graviton depends on a few factors including your software development environment and the technology stack on which your application is built.

This year, we want to take it a step further and make it even easier for customers to adopt Graviton not only through EC2, but also through managed services. Today, we are launching AWS Graviton Fast Start, a new program that makes it even easier to move your workloads to AWS Graviton by providing step-by-step directions for EC2 and other managed services that support the Graviton platform:

  • Amazon Elastic Compute Cloud (Amazon EC2) – EC2 provides the most flexible environment for a migration and can support many kinds of workloads, such as web apps, custom databases, or analytics. You have full control over the interpreted or compiled code running in the EC2 instance. You can also use many open-source and commercial software products that support the Arm64 architecture.
  • AWS Lambda – Migrating your serverless functions can be really easy, especially if you use an interpreted runtime such as Node.js or Python. Most of the time, you only have to check the compatibility of your software dependencies. I have shown a few examples in this blog post.
  • AWS Fargate – Fargate works best if your applications are already running in containers or if you are planning to containerize them. By using multi-architecture container images or images that have Arm64 in their image manifest, you get the serverless benefits of Fargate and the price-performance advantages of Graviton.
  • Amazon Aurora – Relational databases are at the core of many applications. If you need a database compatible with PostgreSQL or MySQL, you can use Amazon Aurora to have a highly performant and globally available database powered by Graviton.
  • Amazon Relational Database Service (RDS) – Similarly to Aurora, Amazon RDS engines such as PostgreSQL, MySQL, and MariaDB can provide a fully managed relational database service using Graviton-based instances.
  • Amazon ElastiCache – When your workload requires ultra-low latency and high throughput, you can speed up your applications with ElastiCache and have a fully managed in-memory cache running on Graviton and compatible with Redis or Memcached.
  • Amazon EMR – With Amazon EMR, you can run large-scale distributed data processing jobs, interactive SQL queries, and machine learning applications on Graviton using open-source analytics frameworks such as Apache SparkApache Hive, and Presto.

Here’s some feedback we got from customers running their workloads on Graviton:

  • Formula 1 racing told us that Graviton2-based C6gn instances provided the best price performance benefits for some of their computational fluid dynamics (CFD) workloads. More recently, they found that Graviton3 C7g instances are 40 percent faster for the same simulations and expect Graviton3-based instances to become the optimal choice to run all of their CFD workloads.
  • Honeycomb has 100 percent of their production workloads running on Graviton using EC2 and Lambda. They have tested the high-throughput telemetry ingestion workload they use for their observability platform against early preview instances of Graviton3 and have seen a 35 percent performance increase for their workload over Graviton2. They were able to run 30 percent fewer instances of C7g than C6g serving the same workload and with 30 percent reduced latency. With these instances in production, they expect over 50 percent price performance improvement over x86 instances.
  • Twitter is working on a multi-year project to leverage Graviton-based EC2 instances to deliver Twitter timelines. As part of their ongoing effort to drive further efficiencies, they tested the new Graviton3-based C7g instances. Across a number of benchmarks representative of their workloads, they found Graviton3-based C7g instances deliver 20-80 percent higher performance compared to Graviton2-based C6g instances, while also reducing tail latencies by as much as 35 percent. They are excited to utilize Graviton3-based instances in the future to realize significant price performance benefits.

With all these options, getting the benefits of running all or part of your workload on AWS Graviton can be easier than you expect. To help you get started, there’s also a free trial on the Graviton-based T4g instances for up to 750 hours per month through December 31st, 2022.

Visit AWS Graviton Fast Start to get step-by-step directions on how to move your workloads to AWS Graviton.

Danilo

New for AWS Global Accelerator – Internet Protocol Version 6 (IPv6) Support

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-global-accelerator-internet-protocol-version-6-ipv6-support/

IPv6 adoption has consistently increased over the last few years, especially among mobile networks. The main reasons to move to IPv6 are:

  • The limited availability of IPv4 addresses can limit the ability to scale up public-facing web and applications servers.
  • IPv6 users from mobile networks experience better performance when their network traffic doesn’t need to manage IPv6 to IPv4 translation.
  • You might need to comply with regulatory rules (such as the Federal Acquisition Regulation in US) to run specific internet traffic over IPv6.

Based on this, we found that we could help improve the network path that your customers use to reach your applications by adding IPv6 support to AWS Global Accelerator. Global Accelerator uses the AWS global network to route network traffic and keep packet loss, jitter, and latency consistently low. Customers like Atlassian, New Relic, and SkyScanner already use Global Accelerator to improve the global availability and performance of their applications.

Global Accelerator provides two global static public IPs that act as a fixed entry point to your application. You can update your application endpoints without making user-facing changes to the IP address. If you configure more than one application endpoint, Global Accelerator automatically reroutes your traffic to your nearest healthy available endpoint to mitigate endpoint failure.

Starting today, you can provide better network performance by routing IPv6 traffic through Global Accelerator to your application endpoints running in AWS Regions. Global Accelerator now supports two types of accelerators: dual-stack and IPv4-only. With a dual-stack accelerator, you are provided with a pair of IPv4 and IPv6 global static IP addresses that can serve both IPv4 and IPv6 traffic.

For existing IPv4-only accelerators, you can update your accelerators to dual-stack to serve both IPv4 and IPv6 traffic. This update enables your accelerator to serve IPv6 traffic and doesn’t impact existing IPv4 traffic served by the accelerator.

Dual-stack accelerators supporting both IPv6 and IPv4 traffic require dual-stack endpoints in the back end. For example, Application Load Balancers (ALBs) can have their IP address type configured as either IPv4-only or dual stack, allowing them to accept both IPv4 or IPv6 client connections. Today, dual-stack ALBs are supported as endpoints for dual-stack accelerators.

Deploying a Dual-Stack Application
To test this new feature, I need a dual-stack application with an ALB entry point. The application must be deployed in Amazon Virtual Private Cloud (Amazon VPC) and support IPv6 traffic. I don’t happen to have IPv6-ready VPCs in my account. I can follow these instructions to migrate an existing VPC that supports IPv4 only to IPv6, or I can create a VPC that supports IPv6 addressing. For this post, I choose to create a VPC.

In the AWS Management Console, I navigate to the Amazon VPC Dashboard. I choose Launch VPC Wizard. In the wizard, I enter a value for the Name tag. This value will be used to auto-generate Name tags for all resources in the VPC. Then, I select the option to associate an Amazon-provided IPv6 CIDR block. I leave all other options to their default values and choose Create VPC.

Console screenshot.

After less than a minute, the VPC is ready. I edit the settings of both public subnets to enable the Auto-assign IP settings to automatically request both a public IPv4 address and an IPv6 address for new network interfaces in this subnet.

Console screenshot.

Now, I want to deploy an application in this VPC. The application will be the endpoint for my accelerator. I view and download the WordPress scalable and durable AWS CloudFormation template from the Sample solutions section of the CloudFormation documentation. This template deploys a full WordPress website behind an ALB. The web tier is scalable and implemented as an EC2 Auto Scaling group. The MySQL database is managed by Amazon Relational Database Service (RDS).

Before deploying the stack, I edit the template to make a few changes. First, I add a DBSubnetGroup resource:

"DBSubnetGroup" : {
  "Type": "AWS::RDS::DBSubnetGroup",
  "Properties": {
    "DBSubnetGroupDescription" : "DB subnet group",
    "SubnetIds" : { "Ref" : "Subnets"}
  }
},

Then, I add the DBSubnetGroupName property to the DBInstance resource. In this way, the database created by the template will be deployed in the same subnets (and VPC) as the web servers.

"DBSubnetGroupName" : { "Ref" : "DBSubnetGroup" },

The last change adds the IpAddressType property to the ApplicationLoadBalancer resource to create a dual-stack load balancer that has IPv6 addresses and will be ready to be used with the new dual-stack option of Global Accelerator.

"IpAddressType": "dualstack",

Because IpAddressType is set to dualstack, the ALB created by the stack will also have IPv6 addresses and will be ready to be used with the new dual-stack option of Global Accelerator.

In the CloudFormation console, I create a stack and upload the template I just edited. In the template parameters, I enter a database user and password to use. For the VpcId parameter, I select the IPv6-ready VPC I just created. For the Subnets parameter, I select the two public subnets of the same VPC. After that, I go to the next steps and create the stack.

After a few minutes, the stack creation is complete. To access the website, I need to open network access to the load balancer. In the EC2 console, I create a security group that allows public access using the HTTP and HTTPS protocols (ports 80 and 443).

Console screenshot.

I choose Load balancers from the navigation pane and select the ALB used by my application. In the Security section, I choose Edit security groups and add the security group I just created to allow web access.

Console screenshot.

Now, I look for the dual-stack (A or AAAA Record) DNS name of the load balancer. I open a browser and connect using the DNS name to complete the configuration of WordPress.

Website.

When connecting again to the endpoint, I see my new (and empty) WordPress website.

Website.

Using Dual-Stack Accelerators with Support for Both IPv6 and IPv4 traffic
Now that my application is ready, I add a dual-stack accelerator in front of the dual-stack ALB. In the Global Accelerator console, I choose Create accelerator. I enter a name for the accelerator and choose the Standard accelerator type.

Console screenshot.

To route both IPv4 and IPv6 through this accelerator, I select the Dual-stack option for the IP address type.

Console screenshot.

Then I add a listener for port 80 using the TCP protocol.

Console screenshot.

For that listener, I configure an endpoint group in the AWS Region where I have my application deployed.

Console screenshot.

I choose Application Load Balancer for the Endpoint type and select the ALB in the CloudFormation stack.

Console screenshot.

Then, I choose Create accelerator. After a few minutes, the accelerator is deployed, and I have a dual-stack DNS name to reach the ALB using IPv4 or IPv6 depending on the network used by the client.

Console screenshot.

Now, my customers can use the IPv4 and IPv6 addresses or, even better, the dual-stack DNS name of the accelerator to connect to the WordPress website. If there is a front-end or mobile application my customers use to connect to the WordPress REST APIs, I can use the dual-stack DNS name so that clients will connect using their preferred IPv4 or IPv6 route.

To understand if the communication between Global Accelerator and the ALB is working, I can monitor the new FlowsDrop Amazon CloudWatch metric. This metric tells me if Global Accelerator is unable to route IPv6 traffic through the endpoint. For example, that can happen if, after the creation of the accelerator, the configuration of the ALB is updated to use IPv4 only.

Availability and Pricing
You can configure dual-stack accelerators using the AWS Management Console, the AWS Command Line Interface (CLI), and AWS SDKs. You can use dual-stack accelerators to optimize access to your applications deployed in any commercial AWS Region.

Protocol translation is not supported, neither IPv4 to IPv6 nor IPv6 to IPv4. For example, Global Accelerator will not allow me to configure a dual-stack accelerator with an IPv4-only ALB endpoint. Also, for IPv6 ALB endpoints, client IP preservation must be enabled.

There are no additional costs for using dual-stack accelerators. You pay for the hours and the amount of data transfer in the dominant direction used by traffic to or from the accelerator. Data transfer costs depend on the location of your clients and the AWS Regions where you are running your applications. For more information, see the Global Accelerator pricing page.

Optimize the IPv6 and IPv4 network paths used by your customers to reach your applications with AWS Global Accelerator.

Danilo

New for Amazon GuardDuty – Malware Detection for Amazon EBS Volumes

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-amazon-guardduty-malware-detection-for-amazon-ebs-volumes/

With Amazon GuardDuty, you can monitor your AWS accounts and workloads to detect malicious activity. Today, we are adding to GuardDuty the capability to detect malware. Malware is malicious software that is used to compromise workloads, repurpose resources, or gain unauthorized access to data. When you have GuardDuty Malware Protection enabled, a malware scan is initiated when GuardDuty detects that one of your EC2 instances or container workloads running on EC2 is doing something suspicious. For example, a malware scan is triggered when an EC2 instance is communicating with a command-and-control server that is known to be malicious or is performing denial of service (DoS) or brute-force attacks against other EC2 instances.

GuardDuty supports many file system types and scans file formats known to be used to spread or contain malware, including Windows and Linux executables, PDF files, archives, binaries, scripts, installers, email databases, and plain emails.

When potential malware is identified, actionable security findings are generated with information such as the threat and file name, the file path, the EC2 instance ID, resource tags and, in the case of containers, the container ID and the container image used. GuardDuty supports container workloads running on EC2, including customer-managed Kubernetes clusters or individual Docker containers. If the container is managed by Amazon Elastic Kubernetes Service (EKS) or Amazon Elastic Container Service (Amazon ECS), the findings also include the cluster name and the task or pod ID so application and security teams can quickly find the affected container resources.

As with all other GuardDuty findings, malware detections are sent to the GuardDuty console, pushed through Amazon EventBridge, routed to AWS Security Hub, and made available in Amazon Detective for incident investigation.

How GuardDuty Malware Protection Works
When you enable malware protection, you set up an AWS Identity and Access Management (IAM) service-linked role that grants GuardDuty permissions to perform malware scans. When a malware scan is initiated for an EC2 instance, GuardDuty Malware Protection uses those permissions to take a snapshot of the attached Amazon Elastic Block Store (EBS) volumes that are less than 1 TB in size and then restore the EBS volumes in an AWS service account in the same AWS Region to scan them for malware. You can use tagging to include or exclude EC2 instances from those permissions and from scanning. In this way, you don’t need to deploy security software or agents to monitor for malware, and scanning the volumes doesn’t impact running workloads. The EBS volumes in the service account and the snapshots in your account are deleted after the scan. Optionally, you can preserve the snapshots when malware is detected.

The service-linked role grants GuardDuty access to AWS Key Management Service (AWS KMS) keys used to encrypt EBS volumes. If the EBS volumes attached to a potentially compromised EC2 instance are encrypted with a customer-managed key, GuardDuty Malware Protection uses the same key to encrypt the replica EBS volumes as well. If the volumes are not encrypted, GuardDuty uses its own key to encrypt the replica EBS volumes and ensure privacy. Volumes encrypted with EBS-managed keys are not supported.

Security in cloud is a shared responsibility between you and AWS. As a guardrail, the service-linked role used by GuardDuty Malware Protection cannot perform any operation on your resources (such as EBS snapshots and volumes, EC2 instances, and KMS keys) if it has the GuardDutyExcluded tag. Once you mark your snapshots with GuardDutyExcluded set to true, the GuardDuty service won’t be able to access these snapshots. The GuardDutyExcluded tag supersedes any inclusion tag. Permissions also restrict how GuardDuty can modify your snapshot so that they cannot be made public while shared with the GuardDuty service account.

The EBS volumes created by GuardDuty are always encrypted. GuardDuty can use KMS keys only on EBS snapshots that have a GuardDuty scan ID tag. The scan ID tag is added by GuardDuty when snapshots are created after an EC2 finding. The KMS keys that are shared with GuardDuty service account cannot be invoked from any other context except the Amazon EBS service. Once the scan completes successfully, the KMS key grant is revoked and the volume replica in GuardDuty service account is deleted, making sure GuardDuty service cannot access your data after completing the scan operation.

Enabling Malware Protection for an AWS Account
If you’re not using GuardDuty yet, Malware Protection is enabled by default when you activate GuardDuty for your account. Because I am already using GuardDuty, I need to enable Malware Protection from the console. If you’re using AWS Organizations, your delegated administrator accounts can enable this for existing member accounts and configure if new AWS accounts in the organization should be automatically enrolled.

In the GuardDuty console, I choose Malware Protection under Settings in the navigation pane. There, I choose Enable and then Enable Malware Protection.

Console screenshot.

Snapshots are automatically deleted after they are scanned. In General settings, I have the option to retain in my AWS account the snapshots where malware is detected and have them available for further analysis.

Console screenshot.

In Scan options, I can configure a list of inclusion tags, so that only EC2 instances with those tags are scanned, or exclusion tags, so that EC2 instances with tags in the list are skipped.

Console screenshot.

Testing Malware Protection GuardDuty Findings
To generate several Amazon GuardDuty findings, including the new Malware Protection findings, I clone the Amazon GuardDuty Tester repo:

$ git clone https://github.com/awslabs/amazon-guardduty-tester

First, I create an AWS CloudFormation stack using the guardduty-tester.template file. When the stack is ready, I follow the instructions to configure my SSH client to log in to the tester instance through the bastion host. Then, I connect to the tester instance:

$ ssh tester

From the tester instance, I start the guardduty_tester.sh script to generate the findings:

$ ./guardduty_tester.sh 

***********************************************************************
* Test #1 - Internal port scanning                                    *
* This simulates internal reconaissance by an internal actor or an   *
* external actor after an initial compromise. This is considered a    *
* low priority finding for GuardDuty because its not a clear indicator*
* of malicious intent on its own.                                     *
***********************************************************************


Starting Nmap 6.40 ( http://nmap.org ) at 2022-05-19 09:36 UTC
Nmap scan report for ip-172-16-0-20.us-west-2.compute.internal (172.16.0.20)
Host is up (0.00032s latency).
Not shown: 997 filtered ports
PORT     STATE  SERVICE
22/tcp   open   ssh
80/tcp   closed http
5050/tcp closed mmcc
MAC Address: 06:25:CB:F4:E0:51 (Unknown)

Nmap done: 1 IP address (1 host up) scanned in 4.96 seconds

-----------------------------------------------------------------------

***********************************************************************
* Test #2 - SSH Brute Force with Compromised Keys                     *
* This simulates an SSH brute force attack on an SSH port that we    *
* can access from this instance. It uses (phony) compromised keys in  *
* many subsequent attempts to see if one works. This is a common      *
* techique where the bad actors will harvest keys from the web in     *
* places like source code repositories where people accidentally leave*
* keys and credentials (This attempt will not actually succeed in     *
* obtaining access to the target linux instance in this subnet)       *
***********************************************************************

2022-05-19 09:36:29 START
2022-05-19 09:36:29 Crowbar v0.4.3-dev
2022-05-19 09:36:29 Trying 172.16.0.20:22
2022-05-19 09:36:33 STOP
2022-05-19 09:36:33 No results found...
2022-05-19 09:36:33 START
2022-05-19 09:36:33 Crowbar v0.4.3-dev
2022-05-19 09:36:33 Trying 172.16.0.20:22
2022-05-19 09:36:37 STOP
2022-05-19 09:36:37 No results found...
2022-05-19 09:36:37 START
2022-05-19 09:36:37 Crowbar v0.4.3-dev
2022-05-19 09:36:37 Trying 172.16.0.20:22
2022-05-19 09:36:41 STOP
2022-05-19 09:36:41 No results found...
2022-05-19 09:36:41 START
2022-05-19 09:36:41 Crowbar v0.4.3-dev
2022-05-19 09:36:41 Trying 172.16.0.20:22
2022-05-19 09:36:45 STOP
2022-05-19 09:36:45 No results found...
2022-05-19 09:36:45 START
2022-05-19 09:36:45 Crowbar v0.4.3-dev
2022-05-19 09:36:45 Trying 172.16.0.20:22
2022-05-19 09:36:48 STOP
2022-05-19 09:36:48 No results found...
2022-05-19 09:36:49 START
2022-05-19 09:36:49 Crowbar v0.4.3-dev
2022-05-19 09:36:49 Trying 172.16.0.20:22
2022-05-19 09:36:52 STOP
2022-05-19 09:36:52 No results found...
2022-05-19 09:36:52 START
2022-05-19 09:36:52 Crowbar v0.4.3-dev
2022-05-19 09:36:52 Trying 172.16.0.20:22
2022-05-19 09:36:56 STOP
2022-05-19 09:36:56 No results found...
2022-05-19 09:36:56 START
2022-05-19 09:36:56 Crowbar v0.4.3-dev
2022-05-19 09:36:56 Trying 172.16.0.20:22
2022-05-19 09:37:00 STOP
2022-05-19 09:37:00 No results found...
2022-05-19 09:37:00 START
2022-05-19 09:37:00 Crowbar v0.4.3-dev
2022-05-19 09:37:00 Trying 172.16.0.20:22
2022-05-19 09:37:04 STOP
2022-05-19 09:37:04 No results found...
2022-05-19 09:37:04 START
2022-05-19 09:37:04 Crowbar v0.4.3-dev
2022-05-19 09:37:04 Trying 172.16.0.20:22
2022-05-19 09:37:08 STOP
2022-05-19 09:37:08 No results found...
2022-05-19 09:37:08 START
2022-05-19 09:37:08 Crowbar v0.4.3-dev
2022-05-19 09:37:08 Trying 172.16.0.20:22
2022-05-19 09:37:12 STOP
2022-05-19 09:37:12 No results found...
2022-05-19 09:37:12 START
2022-05-19 09:37:12 Crowbar v0.4.3-dev
2022-05-19 09:37:12 Trying 172.16.0.20:22
2022-05-19 09:37:16 STOP
2022-05-19 09:37:16 No results found...
2022-05-19 09:37:16 START
2022-05-19 09:37:16 Crowbar v0.4.3-dev
2022-05-19 09:37:16 Trying 172.16.0.20:22
2022-05-19 09:37:20 STOP
2022-05-19 09:37:20 No results found...
2022-05-19 09:37:20 START
2022-05-19 09:37:20 Crowbar v0.4.3-dev
2022-05-19 09:37:20 Trying 172.16.0.20:22
2022-05-19 09:37:23 STOP
2022-05-19 09:37:23 No results found...
2022-05-19 09:37:23 START
2022-05-19 09:37:23 Crowbar v0.4.3-dev
2022-05-19 09:37:23 Trying 172.16.0.20:22
2022-05-19 09:37:27 STOP
2022-05-19 09:37:27 No results found...
2022-05-19 09:37:27 START
2022-05-19 09:37:27 Crowbar v0.4.3-dev
2022-05-19 09:37:27 Trying 172.16.0.20:22
2022-05-19 09:37:31 STOP
2022-05-19 09:37:31 No results found...
2022-05-19 09:37:31 START
2022-05-19 09:37:31 Crowbar v0.4.3-dev
2022-05-19 09:37:31 Trying 172.16.0.20:22
2022-05-19 09:37:34 STOP
2022-05-19 09:37:34 No results found...
2022-05-19 09:37:35 START
2022-05-19 09:37:35 Crowbar v0.4.3-dev
2022-05-19 09:37:35 Trying 172.16.0.20:22
2022-05-19 09:37:38 STOP
2022-05-19 09:37:38 No results found...
2022-05-19 09:37:38 START
2022-05-19 09:37:38 Crowbar v0.4.3-dev
2022-05-19 09:37:38 Trying 172.16.0.20:22
2022-05-19 09:37:42 STOP
2022-05-19 09:37:42 No results found...
2022-05-19 09:37:42 START
2022-05-19 09:37:42 Crowbar v0.4.3-dev
2022-05-19 09:37:42 Trying 172.16.0.20:22
2022-05-19 09:37:46 STOP
2022-05-19 09:37:46 No results found...

-----------------------------------------------------------------------

***********************************************************************
* Test #3 - RDP Brute Force with Password List                        *
* This simulates an RDP brute force attack on the internal RDP port  *
* of the windows server that we installed in the environment.  It uses*
* a list of common passwords that can be found on the web. This test  *
* will trigger a detection, but will fail to get into the target      *
* windows instance.                                                   *
***********************************************************************

Sending 250 password attempts at the windows server...
Hydra v9.4-dev (c) 2022 by van Hauser/THC & David Maciejak - Please do not use in military or secret service organizations, or for illegal purposes (this is non-binding, these *** ignore laws and ethics anyway).

Hydra (https://github.com/vanhauser-thc/thc-hydra) starting at 2022-05-19 09:37:46
[WARNING] rdp servers often don't like many connections, use -t 1 or -t 4 to reduce the number of parallel connections and -W 1 or -W 3 to wait between connection to allow the server to recover
[INFO] Reduced number of tasks to 4 (rdp does not like many parallel connections)
[WARNING] the rdp module is experimental. Please test, report - and if possible, fix.
[DATA] max 4 tasks per 1 server, overall 4 tasks, 1792 login tries (l:7/p:256), ~448 tries per task
[DATA] attacking rdp://172.16.0.24:3389/
[STATUS] 1099.00 tries/min, 1099 tries in 00:01h, 693 to do in 00:01h, 4 active
1 of 1 target completed, 0 valid password found
Hydra (https://github.com/vanhauser-thc/thc-hydra) finished at 2022-05-19 09:39:23

-----------------------------------------------------------------------

***********************************************************************
* Test #4 - CryptoCurrency Mining Activity                            *
* This simulates interaction with a cryptocurrency mining pool which *
* can be an indication of an instance compromise. In this case, we are*
* only interacting with the URL of the pool, but not downloading      *
* any files. This will trigger a threat intel based detection.        *
***********************************************************************

Calling bitcoin wallets to download mining toolkits

-----------------------------------------------------------------------

***********************************************************************
* Test #5 - DNS Exfiltration                                          *
* A common exfiltration technique is to tunnel data out over DNS      *
* to a fake domain.  Its an effective technique because most hosts    *
* have outbound DNS ports open.  This test wont exfiltrate any data,  *
* but it will generate enough unusual DNS activity to trigger the     *
* detection.                                                          *
***********************************************************************

Calling large numbers of large domains to simulate tunneling via DNS

***********************************************************************
* Test #6 - Fake domain to prove that GuardDuty is working            *
* This is a permanent fake domain that customers can use to prove that*
* GuardDuty is working.  Calling this domain will always generate the *
* Backdoor:EC2/C&CActivity.B!DNS finding type                         *
***********************************************************************

Calling a well known fake domain that is used to generate a known finding

; <<>> DiG 9.11.4-P2-RedHat-9.11.4-26.P2.amzn2.5.2 <<>> GuardDutyC2ActivityB.com any
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 11495
;; flags: qr rd ra; QUERY: 1, ANSWER: 8, AUTHORITY: 0, ADDITIONAL: 1

;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 4096
;; QUESTION SECTION:
;GuardDutyC2ActivityB.com.	IN	ANY

;; ANSWER SECTION:
GuardDutyC2ActivityB.com. 6943	IN	SOA	ns1.markmonitor.com. hostmaster.markmonitor.com. 2018091906 86400 3600 2592000 172800
GuardDutyC2ActivityB.com. 6943	IN	NS	ns3.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns5.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns7.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns2.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns4.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns6.markmonitor.com.
GuardDutyC2ActivityB.com. 6943	IN	NS	ns1.markmonitor.com.

;; Query time: 27 msec
;; SERVER: 172.16.0.2#53(172.16.0.2)
;; WHEN: Thu May 19 09:39:23 UTC 2022
;; MSG SIZE  rcvd: 238


*****************************************************************************************************
Expected GuardDuty Findings

Test 1: Internal Port Scanning
Expected Finding: EC2 Instance  i-011e73af27562827b  is performing outbound port scans against remote host. 172.16.0.20
Finding Type: Recon:EC2/Portscan

Test 2: SSH Brute Force with Compromised Keys
Expecting two findings - one for the outbound and one for the inbound detection
Outbound:  i-011e73af27562827b  is performing SSH brute force attacks against  172.16.0.20
Inbound:  172.16.0.25  is performing SSH brute force attacks against  i-0bada13e0aa12d383
Finding Type: UnauthorizedAccess:EC2/SSHBruteForce

Test 3: RDP Brute Force with Password List
Expecting two findings - one for the outbound and one for the inbound detection
Outbound:  i-011e73af27562827b  is performing RDP brute force attacks against  172.16.0.24
Inbound:  172.16.0.25  is performing RDP brute force attacks against  i-0191573dec3b66924
Finding Type : UnauthorizedAccess:EC2/RDPBruteForce

Test 4: Cryptocurrency Activity
Expected Finding: EC2 Instance  i-011e73af27562827b  is querying a domain name that is associated with bitcoin activity
Finding Type : CryptoCurrency:EC2/BitcoinTool.B!DNS

Test 5: DNS Exfiltration
Expected Finding: EC2 instance  i-011e73af27562827b  is attempting to query domain names that resemble exfiltrated data
Finding Type : Trojan:EC2/DNSDataExfiltration

Test 6: C&C Activity
Expected Finding: EC2 instance  i-011e73af27562827b  is querying a domain name associated with a known Command & Control server. 
Finding Type : Backdoor:EC2/C&CActivity.B!DNS

After a few minutes, the findings appear in the GuardDuty console. At the top, I see the malicious files found by the new Malware Protection capability. One of the findings is related to an EC2 instance, the other to an ECS cluster.

Console screenshot.

First, I select the finding related to the EC2 instance. In the panel, I see the information on the instance and the malicious file, such as the file name and path. In the Malware scan details section, the Trigger finding ID points to the original GuardDuty finding that triggered the malware scan. In my case, the original finding was that this EC2 instance was performing RDP brute force attacks against another EC2 instance.

Console screenshot.

Here, I choose Investigate with Detective and, directly from the GuardDuty console, I go to the Detective console to visualize AWS CloudTrail and Amazon Virtual Private Cloud (Amazon VPC) flow data for the EC2 instance, the AWS account, and the IP address affected by the finding. Using Detective, I can analyze, investigate, and identify the root cause of suspicious activities found by GuardDuty.

Console screenshot.

When I select the finding related to the ECS cluster, I have more information on the resource affected, such as the details of the ECS cluster, the task, the containers, and the container images.

Console screenshot.

Using the GuardDuty tester scripts makes it easier to test the overall integration of GuardDuty with other security frameworks you use so that you can be ready when a real threat is detected.

Comparing GuardDuty Malware Protection with Amazon Inspector
At this point, you might ask yourself how GuardDuty Malware Protection relates to Amazon Inspector, a service that scans AWS workloads for software vulnerabilities and unintended network exposure. The two services complement each other and offer different layers of protection:

  • Amazon Inspector offers proactive protection by identifying and remediating known software and application vulnerabilities that serve as an entry point for attackers to compromise resources and install malware.
  • GuardDuty Malware Protection detects malware that is found to be present on actively running workloads. At that point, the system has already been compromised, but GuardDuty can limit the time of an infection and take action before a system compromise results in a business-impacting event.

Availability and Pricing
Amazon GuardDuty Malware Protection is available today in all AWS Regions where GuardDuty is available, excluding the AWS China (Beijing), AWS China (Ningxia), AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions.

At launch, GuardDuty Malware Protection is integrated with these partner offerings:

With GuardDuty, you don’t need to deploy security software or agents to monitor for malware. You only pay for the amount of GB scanned in the file systems (not for the size of the EBS volumes) and for the EBS snapshots during the time they are kept in your account. All EBS snapshots created by GuardDuty are automatically deleted after they are scanned unless you enable snapshot retention when malware is found. For more information, see GuardDuty pricing and EBS pricing. Note that GuardDuty only scans EBS volumes less than 1 TB in size. To help you control costs and avoid repeating alarms, the same volume is not scanned more often than once every 24 hours.

Detect malicious activity and protect your applications from malware with Amazon GuardDuty.

Danilo