Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.
Note – All sessions are free and in Pacific Time.
Tech talks featured this month:
Analytics & Big Data
May 21, 2018 | 11:00 AM – 11:45 AM PT – Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.
May 24, 2018 | 11:00 AM – 11:45 AM PT – Data Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.
May 30, 2018 | 01:00 PM – 01:45 PM PT – Accelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.
Containers
May 24, 2018 | 01:00 PM – 01:45 PM PT –Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.
Databases
May 21, 2018 | 01:00 PM – 01:45 PM PT – How to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.
May 23, 2018 | 01:00 PM – 01:45 PM PT – 5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.
DevOps
May 23, 2018 | 09:00 AM – 09:45 AM PT – .NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.
Enterprise & Hybrid
May 22, 2018 | 11:00 AM – 11:45 AM PT – Hybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.
IoT
May 31, 2018 | 11:00 AM – 11:45 AM PT – Using AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.
Machine Learning
May 22, 2018 | 09:00 AM – 09:45 AM PT – Using Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.
May 24, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.
May 30, 2018 | 09:00 AM – 09:45 AM PT– Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.
June 1, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.
May 30, 2018 | 11:00 AM – 11:45 AM PT – Accelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.
If you build (or want to build) data-driven web and mobile apps and need real-time updates and the ability to work offline, you should take a look at AWS AppSync. Announced in preview form at AWS re:Invent 2017 and described in depth here, AWS AppSync is designed for use in iOS, Android, JavaScript, and React Native apps. AWS AppSync is built around GraphQL, an open, standardized query language that makes it easy for your applications to request the precise data that they need from the cloud.
I’m happy to announce that the preview period is over and that AWS AppSync is now generally available and production-ready, with six new features that will simplify and streamline your application development process:
Console Log Access – You can now see the CloudWatch Logs entries that are created when you test your GraphQL queries, mutations, and subscriptions from within the AWS AppSync Console.
Console Testing with Mock Data – You can now create and use mock context objects in the console for testing purposes.
Subscription Resolvers – You can now create resolvers for AWS AppSync subscription requests, just as you can already do for query and mutate requests.
Batch GraphQL Operations for DynamoDB – You can now make use of DynamoDB’s batch operations (BatchGetItem and BatchWriteItem) across one or more tables. in your resolver functions.
CloudWatch Support – You can now use Amazon CloudWatch Metrics and CloudWatch Logs to monitor calls to the AWS AppSync APIs.
CloudFormation Support – You can now define your schemas, data sources, and resolvers using AWS CloudFormation templates.
A Brief AppSync Review Before diving in to the new features, let’s review the process of creating an AWS AppSync API, starting from the console. I click Create API to begin:
I enter a name for my API and (for demo purposes) choose to use the Sample schema:
The schema defines a collection of GraphQL object types. Each object type has a set of fields, with optional arguments:
If I was creating an API of my own I would enter my schema at this point. Since I am using the sample, I don’t need to do this. Either way, I click on Create to proceed:
The GraphQL schema type defines the entry points for the operations on the data. All of the data stored on behalf of a particular schema must be accessible using a path that begins at one of these entry points. The console provides me with an endpoint and key for my API:
It also provides me with guidance and a set of fully functional sample apps that I can clone:
When I clicked Create, AWS AppSync created a pair of Amazon DynamoDB tables for me. I can click Data Sources to see them:
I can also see and modify my schema, issue queries, and modify an assortment of settings for my API.
Let’s take a quick look at each new feature…
Console Log Access The AWS AppSync Console already allows me to issue queries and to see the results, and now provides access to relevant log entries.In order to see the entries, I must enable logs (as detailed below), open up the LOGS, and check the checkbox. Here’s a simple mutation query that adds a new event. I enter the query and click the arrow to test it:
I can click VIEW IN CLOUDWATCH for a more detailed view:
Console Testing with Mock Data You can now create a context object in the console where it will be passed to one of your resolvers for testing purposes. I’ll add a testResolver item to my schema:
Then I locate it on the right-hand side of the Schema page and click Attach:
I choose a data source (this is for testing and the actual source will not be accessed), and use the Put item mapping template:
Then I click Select test context, choose Create New Context, assign a name to my test content, and click Save (as you can see, the test context contains the arguments from the query along with values to be returned for each field of the result):
After I save the new Resolver, I click Test to see the request and the response:
Subscription Resolvers Your AWS AppSync application can monitor changes to any data source using the @aws_subscribe GraphQL schema directive and defining a Subscription type. The AWS AppSync client SDK connects to AWS AppSync using MQTT over Websockets and the application is notified after each mutation. You can now attach resolvers (which convert GraphQL payloads into the protocol needed by the underlying storage system) to your subscription fields and perform authorization checks when clients attempt to connect. This allows you to perform the same fine grained authorization routines across queries, mutations, and subscriptions.
Batch GraphQL Operations Your resolvers can now make use of DynamoDB batch operations that span one or more tables in a region. This allows you to use a list of keys in a single query, read records multiple tables, write records in bulk to multiple tables, and conditionally write or delete related records across multiple tables.
In order to use this feature the IAM role that you use to access your tables must grant access to DynamoDB’s BatchGetItem and BatchPutItem functions.
CloudWatch Logs Support You can now tell AWS AppSync to log API requests to CloudWatch Logs. Click on Settings and Enable logs, then choose the IAM role and the log level:
CloudFormation Support You can use the following CloudFormation resource types in your templates to define AWS AppSync resources:
AWS::AppSync::GraphQLApi – Defines an AppSync API in terms of a data source (an Amazon Elasticsearch Service domain or a DynamoDB table).
AWS::AppSync::ApiKey – Defines the access key needed to access the data source.
AWS::AppSync::GraphQLSchema – Defines a GraphQL schema.
AWS::AppSync::DataSource – Defines a data source.
AWS::AppSync::Resolver – Defines a resolver by referencing a schema and a data source, and includes a mapping template for requests.
Here’s a simple schema definition in YAML form:
AppSyncSchema:
Type: "AWS::AppSync::GraphQLSchema"
DependsOn:
- AppSyncGraphQLApi
Properties:
ApiId: !GetAtt AppSyncGraphQLApi.ApiId
Definition: |
schema {
query: Query
mutation: Mutation
}
type Query {
singlePost(id: ID!): Post
allPosts: [Post]
}
type Mutation {
putPost(id: ID!, title: String!): Post
}
type Post {
id: ID!
title: String!
}
Available Now These new features are available now and you can start using them today! Here are a couple of blog posts and other resources that you might find to be of interest:
Today we’re releasing a new machine learning feature in Amazon Kinesis Data Analytics for detecting “hotspots” in your streaming data. We launched Kinesis Data Analytics in August of 2016 and we’ve continued to add features since. As you may already know, Kinesis Data Analytics is a fully managed real-time processing engine for streaming data that lets you write SQL queries to derive meaning from your data and output the results to Kinesis Data Firehose, Kinesis Data Streams, or even an AWS Lambda function. The new HOTSPOT function adds to the existing machine learning capabilities in Kinesis that allow customers to leverage unsupervised streaming based machine learning algorithms. Customers don’t need to be experts in data science or machine learning to take advantage of these capabilities.
Hotspots
The HOTSPOTS function is a new Kinesis Data Analytics SQL function you can use to idenitfy relatively dense regions in your data without having to explicity build and train complicated machine learning models. You can identify subsections of your data that need immediate attention and take action programatically by streaming the hotspots out to a Kinesis Data stream, to a Firehose delivery stream, or by invoking a AWS Lambda function.
There are a ton of really cool scenarios where this could make your operations easier. Imagine a ride-share program or autonomous vehicle fleet communicating spatiotemporal data about traffic jams and congestion, or a datacenter where a number of servers start to overheat indicating an HVAC issue. HOTSPOTS is not limited to spatiotemporal data and you could apply it across many problem domains.
The function follows some simple syntax and accepts the DOUBLE, INTEGER, FLOAT, TINYINT, SMALLINT, REAL, and BIGINT data types.
The HOTSPOT function takes a cursor as input and returns a JSON string describing the hotspot. This will be easier to understand with an example.
Using Kinesis Data Analytics to Detect Hotspots
Let’s take a simple data set from NY Taxi and Limousine Commission that tracks yellow cab pickup and dropoff locations. Most of this data is already on S3 and publicly accessible at s3://nyc-tlc/. We will create a small python script to load our Kinesis Data Stream with Taxi records which will feed our Kinesis Data Analytics. Finally we’ll output all of this to a Kinesis Data Firehose connected to an Amazon Elasticsearch Service cluster for visualization with Kibana. I know from living in New York for 5 years that we’ll probably find a hotspot or two in this data.
First, we’ll create an input Kinesis stream and start sending our NYC Taxi Ride data into it. I just wrote a quick python script to read from one of the CSV files and used boto3 to push the records into Kinesis. You can put the record in whatever way works for you.
import csv
import json
import boto3
def chunkit(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
kinesis = boto3.client("kinesis")
with open("taxidata2.csv") as f:
reader = csv.DictReader(f)
records = chunkit([{"PartitionKey": "taxis", "Data": json.dumps(row)} for row in reader], 500)
for chunk in records:
kinesis.put_records(StreamName="TaxiData", Records=chunk)
Next, we’ll create the Kinesis Data Analytics application and add our input stream with our taxi data as the source.
Next we’ll automatically detect the schema.
Now we’ll create a quick SQL Script to detect our hotspots and add that to the Real Time Analytics section of our application.
CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
"pickup_longitude" DOUBLE,
"pickup_latitude" DOUBLE,
HOTSPOTS_RESULT VARCHAR(10000)
);
CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
SELECT "pickup_longitude", "pickup_latitude", "HOTSPOTS_RESULT" FROM
TABLE(HOTSPOTS(
CURSOR(SELECT STREAM * FROM "SOURCE_SQL_STREAM_001"),
1000,
0.013,
20
)
);
Our HOTSPOTS function takes an input stream, a window size, scan radius, and a minimum number of points to count as a hotspot. The values for these are application dependent but you can tinker with them in the console easily until you get the results you want. There are more details about the parameters themselves in the documentation. The HOTSPOTS_RESULT returns some useful JSON that would let us plot bounding boxes around our hotspots:
When we have our desired results we can save the script and connect our application to our Amazon Elastic Search Service Firehose Delivery Stream. We can run an intermediate lambda function in the firehose to transform our record into a format more suitable for geographic work. Then we can update our mapping in Elasticsearch to index the hotspot objects as Geo-Shapes.
Finally, we can connect to Kibana and visualize the results.
Looks like Manhattan is pretty busy!
Available Now This feature is available now in all existing regions with Kinesis Data Analytics. I think this is a really interesting new feature of Kinesis Data Analytics that can bring immediate value to many applications. Let us know what you build with it on Twitter or in the comments!
Data that describe processes in a spatial context are everywhere in our day-to-day lives and they dominate big data problems. Map data, for instance, whether describing networks of roads or remote sensing data from satellites, get us where we need to go. Atmospheric data from simulations and sensors underlie our weather forecasts and climate models. Devices and sensors with GPS can provide a spatial context to nearly all mobile data.
In this post, we introduce the WIND toolkit, a huge (500 TB), open weather model dataset that’s available to the world on Amazon’s cloud services. We walk through how to access this data and some of the open-source software developed to make it easily accessible. Our solution considers a subset of geospatial data that exist on a grid (raster) and explores ways to provide access to large-scale raster data from weather models. The solution uses foundational AWS services and the Hierarchical Data Format (HDF), a well adopted format for scientific data.
The approach developed here can be extended to any data that fit in an HDF5 file, which can describe sparse and dense vectors and matrices of arbitrary dimensions. This format is already popular within the physical sciences for both experimental and simulation data. We discuss solutions to gridded data storage for a massive dataset of public weather model outputs called the Wind Integration National Dataset (WIND) toolkit. We also highlight strategies that are general to other large geospatial data management problems.
Wind Integration National Dataset
As variable renewable power penetration levels increase in power systems worldwide, the importance of renewable integration studies to ensure continued economic and reliable operation of the power grid is also increasing. The WIND toolkit is the largest freely available grid integration dataset to date.
The WIND toolkit was developed by 3TIER by Vaisala. They were under a subcontract to the National Renewable Energy Laboratory (NREL) to support studies on integration of wind energy into the existing US grid. NREL is a part of a network of national laboratories for the US Department of Energy and has a mission to advance the science and engineering of energy efficiency, sustainable transportation, and renewable power technologies.
The toolkit has been used by consultants, research groups, and universities worldwide to support grid integration studies. Less traditional uses also include resource assessments for wind plants (such as those powering Amazon data centers), and studying the effects of weather on California condor migrations in the Baja peninsula.
The diversity of applications highlights the value of accessible, open public data. Yet, there’s a catch: the dataset is huge. The WIND toolkit provides simulated atmospheric (weather) data at a two-km spatial resolution and five-minute temporal resolution at multiple heights for seven years. The entire dataset is half a petabyte (500 TB) in size and is stored in the NREL High Performance Computing data center in Golden, Colorado. Making this dataset publicly available easily and in a cost-effective manner is a major challenge.
As other laboratories and public institutions work to release their data to the world, they may face similar challenges to those that we experienced. Some prior, well-intentioned efforts to release huge datasets as-is have resulted in data resources that are technically available but fundamentally unusable. They may be stored in an unintuitive format or indexed and organized to support only a subset of potential uses. Downloading hundreds of terabytes of data is often impractical. Most users don’t have access to a big data cluster (or super computer) to slice and dice the data as they need after it’s downloaded.
We aim to provide a large amount of data (50 terabytes) to the public in a way that is efficient, scalable, and easy to use. In many cases, researchers can access these huge cloud-located datasets using the same software and algorithms they have developed for smaller datasets stored locally. Only the pieces of data they need for their individual analysis must be downloaded. To make this work in practice, we worked with the HDF Group and have built upon their forthcoming Highly Scalable Data Service.
In the rest of this post, we discuss how the HSDS software was developed to use Amazon EC2 and Amazon S3 resources to provide convenient and scalable access to these huge geospatial datasets. We describe how the HSDS service has been put to work for the WIND Toolkit dataset and demonstrate how to access it using the h5pyd Python library and the REST API. We conclude with information about our ongoing work to release more ‘open’ datasets to the public using AWS services, and ways to improve and extend the HSDS with newer Amazon services like Amazon ECS and AWS Lambda.
Developing a scalable service for big geospatial data
The HDF5 file format and API have been used for many years and is an effective means of storing large scientific datasets. For example, NASA’s Earth Observing System (EOS) satellites collect more than 16 TBs of data per day using HDF5.
With the rise of the cloud, there are new challenges and opportunities to rethink how HDF5 can be enhanced to work effectively as a component in a cloud-native architecture. For the HDF Group, working with NREL has been a great opportunity to put ideas into practice with a production-size dataset.
An HDF5 file consists of a directed graph of group and dataset objects. Datasets can be thought of as a multidimensional array with support for user-defined metadata tags and compression. Typical operations on datasets would be reading or writing data to a regular subregion (a hyperslab) or reading and writing individual elements (a point selection). Also, group and dataset objects may each contain an arbitrary number of the user-defined metadata elements known as attributes.
Many people have used the HDF library in applications developed or ported to run on EC2 instances, but there are a number of constraints that often prove problematic:
The HDF5 library can’t read directly from HDF5 files stored as S3 objects. The entire file (often many GB in size) would need to be copied to local storage before the first byte can be read. Also, the instance must be configured with the appropriately sized EBS volume)
The HDF library only has access to the computational resources of the instance itself (as opposed to a cluster of instances), so many operations are bottlenecked by the library.
Any modifications to the HDF5 file would somehow have to be synchronized with changes that other instances have made to same file before writing back to S3.
Using a pattern common to many offerings from AWS, the solution to these constraints is to develop a service framework around the HDF data model. Using this model, the HDF Group has created the Highly Scalable Data Service (HSDS) that provides all the functionality that traditionally was provided by the HDF5 library. By using the service, you don’t need to manage your own file volumes, but can just read and write whatever data that you need.
Because the service manages the actual data persistence to a durable medium (S3, in this case), you don’t need to worry about disk management. Simply stream the data you need from the service as you need it. Secondly, putting the functionality behind a service allows some tricks to increase performance (described in more detail later). And lastly, HSDS allows any number of clients to access the data at the same time, enabling HDF5 to be used as a coordination mechanism for multiple readers and writers.
In designing the HSDS architecture, we gave much thought to how to achieve scalability of the HSDS service. For accessing HDF5 data, there are two different types of scaling to consider:
Multiple clients making many requests to the service
Single requests that require a significant amount of data processing
To deal with the first scaling challenge, as with most services, we considered how the service responds as the request rate increases. AWS provides some great tools that help in this regard:
Auto Scaling groups
Elastic Load Balancing load balancers
The ability of S3 to handle large aggregate throughput rates
By using a cluster of EC2 instances behind a load balancer, you can handle different client loads in a cost-effective manner.
The second scaling challenge concerns single requests that would take significant processing time with just one compute node. One example of this from the WIND toolkit would be extracting all the values in the seven-year time span for a given geographic point and dataset.
In HDF5, large datasets are typically stored as “chunks”; that is, a regular partition of the array. In HSDS, each chunk is stored as a binary object in S3. The sequential approach to retrieving the time series values would be for the service to read each chunk needed from S3, extract the needed elements, and go on to the next chunk. In this case, that would involve processing 2557 chunks, and would be quite slow.
Fortunately, with HSDS, you can speed this up quite a bit by exploiting the compute and I/O capabilities of the cluster. Upon receiving the request, the receiving node can use other nodes in the cluster to read different portions of the selection. With multiple nodes reading from S3 in parallel, performance improves as the cluster size increases.
The diagram below illustrates how this works in simplified case of four chunks and four nodes.
This architecture has worked in well in practice. In testing with the WIND toolkit and time series extraction, we observed a request latency of ~60 seconds using four nodes vs. ~5 seconds with 40 nodes. Performance roughly scales with the size of the cluster.
A planned enhancement to this is to use AWS Lambda for the worker processing. This enables 1000-way parallel reads at a reasonable cost, as you only pay for the milliseconds of CPU time used with AWS Lambda.
Public access to atmospheric data using HSDS and AWS
An early challenge in releasing the WIND toolkit data was in deciding how to subset the data for different use cases. In general, few researchers need access to the entire 0.5 PB of data and a great deal of efficiency and cost reduction can be gained by making directed constituent datasets.
NREL grid integration researchers initially extracted a 2-TB subset by selecting 120,000 points where the wind resource seemed appropriate for development. They also chose only those data important for wind applications (100-m wind speed, converted to power), the most interesting locations for those performing grid studies. To support the remaining users who needed more data resolution, we down-sampled the data to a 60-minute temporal resolution, keeping all the other variables and spatial resolution intact. This reduced dataset is 50 TB of data describing 30+ atmospheric variables of data for 7 years at a 60-minute temporal resolution.
The WindViz browser-based Gridded Wind Toolkit Visualizer was created as an example implementation of the HSDS REST API in JavaScript. The visualizer is written in the style of ECMAScript 2016 using a modern development toolchain that includes webpack and Babel. The source code is available through our GitHub repository. The demo page is hosted via GitHub pages, and we use a cross-origin AJAX request to fetch data from the HSDS service running on the EC2 infrastructure. The visualizer can be used to explore the gridded wind toolkit data on a map. Achieve full spatial resolution by zooming in to a specific region.
Programmatic access is possible using the h5pyd Python library, a distributed analog to the widely used h5py library. Users interact with the datasets (variables) and slice the data from its (time x longitude x latitude) cube form as they see fit.
Examples and use cases are described in a set of Jupyter notebooks and available on GitHub:
Now you have a Jupyter notebook server running on your EC2 server.
From your laptop, create an SSH tunnel:
$ ssh –L 8888:localhost:8888 (IP address of the EC2 server)
Now, you can browse to localhost:8888 using the correct token, and interact with the notebooks as if they were local. Within the directory, there are examples for accessing the HSDS API and plotting wind and weather data using matplotlib.
Controlling access and defraying costs
A final concern is rate limiting and access control. Although the HSDS service is scalable and relatively robust, we had a few practical concerns:
How can we protect from malicious or accidental use that may lead to high egress fees (for example, someone who attempts to repeatedly download the entire dataset from S3)?
How can we keep track of who is using the data both to document the value of the data resource and to justify the costs?
If costs become too high, can we charge for some or all API use to help cover the costs?
To approach these problems, we investigated using Amazon API Gateway and its simplified integration with the AWS Marketplace for SaaS monetization as well as third-party API proxies.
In the end, we chose to use API Umbrella due to its close involvement with http://data.gov. While AWS Marketplace is a compelling option for future datasets, the decision was made to keep this dataset entirely open, at least for now. As community use and associated costs grow, we’ll likely revisit Marketplace. Meanwhile, API Umbrella provides controls for rate limiting and API key registration out of the box and was simple to implement as a front-end proxy to HSDS. Those applications that may want to charge for API use can accomplish a similar strategy using Amazon API Gateway and AWS Marketplace.
Ongoing work and other resources
As NREL and other government research labs, municipalities, and organizations try to share data with the public, we expect many of you will face similar challenges to those we have tried to approach with the architecture described in this post. Providing large datasets is one challenge. Doing so in a way that is affordable and convenient for users is an entirely more difficult goal. Using AWS cloud-native services and the existing foundation of the HDF file format has allowed us to tackle that challenge in a meaningful way.
Dr. Caleb Phillips is a senior scientist with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Caleb comes from a background in computer science systems, applied statistics, computational modeling, and optimization. His work at NREL spans the breadth of renewable energy technologies and focuses on applying modern data science techniques to data problems at scale.
Dr. Caroline Draxl is a senior scientist at NREL. She supports the research and modeling activities of the US Department of Energy from mesoscale to wind plant scale. Caroline uses mesoscale models to research wind resources in various countries, and participates in on- and offshore boundary layer research and in the coupling of the mesoscale flow features (kilometer scale) to the microscale (tens of meters). She holds a M.S. degree in Meteorology and Geophysics from the University of Innsbruck, Austria, and a PhD in Meteorology from the Technical University of Denmark.
John Readey has been a Senior Architect at The HDF Group since he joined in June 2014. His interests include web services related to HDF, applications that support the use of HDF and data visualization.Before joining The HDF Group, John worked at Amazon.com from 2006–2014 where he developed service-based systems for eCommerce and AWS.
Jordan Perr-Sauer is an RPP intern with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Jordan hopes to use his professional background in software engineering and his academic training in applied mathematics to solve the challenging problems facing America and the world.
Can you believe it’s already the month of March? With some great new Tech Talks available this month, there’s no better time to grow your knowledge about AWS services and solutions.
AWS Online Tech Talks
March 2018– Schedule
Below is the full schedule for the live, online technical sessions being held during the month of March. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.
March 28, 2018 | 11:00 AM – 12:00 PM PT – Deep Dive on Amazon Athena (300) – Dive deep into the most common Amazon Athena use cases, including working with other AWS services.
Compute
March 26, 2018 | 01:00 PM – 01:45 PM PT – High Performance Computing in the Cloud (200) – Learn how AWS is enabling faster time to results and higher ROI when it comes to solving the big problems in science, engineering and business with high performance computing in the cloud.
March 27, 2018 | 01:00 PM – 01:45 PM PT – Introduction to Hybrid Cloud on AWS (200) – Learn how AWS is building the industry’s broadest capabilities for Hybrid Cloud deployments.
March 28, 2018 | 01:00 PM – 02:00 PM PT – Media Processing Workflows at High Velocity and Scale using AI and ML (200) – Hear how AWS customers have improved media supply chains using AI in areas such as metadata tagging (Rekognition and Comprehend), translations, transcriptions, and cloud services (Elemental).
March 22, 2018 | 09:00 AM – 09:45 AM PT – New Mobile CLI and Console Experience (200) – Learn how AWS Mobile Services has introduced a new CLI and streamlined console experience in order to simplify and speed up the development of mobile applications with innovative AWS features and back-end functionality.
Networking
March 28, 2018 | 09:00 AM – 09:45 AM PT –Deep Dive on New AWS Networking Features (300) – Learn how AWS PrivateLink, Direct Connect gateway, and new features with Elastic Load Balancers (ELB) come together to meet the needs of a modern enterprise.
March 29, 2018 | 09:00 AM – 09:45 AM PT – Navigating GDPR Compliance on AWS (300) – Get a walkthrough of potential General Data Protection Regulation (GDPR) obligations and see how the AWS cloud offers services and features that are consistent with GDPR considerations in the ramp-up to the May 25th, 2018 enforcement date.
March 27, 2018 | 11:00 AM – 11:45 AM PT–Enterprise Applications with Amazon EFS (300) – Join us for a technical deep dive on Amazon EFS, where you’ll learn tips and tricks for integrating your enterprise applications with Amazon EFS.
March 29, 2018 | 11:00 AM – 11:45 AM PT – Transforming Data Lakes with Amazon S3 Select & Amazon Glacier Select (300) – Join us for a webinar where we’ll demonstrate how Amazon S3 Select can increase analytics query performance up to 400%, and Amazon Glacier Select makes it practical to extend queries to archive storage, significantly reducing data lake storage costs.
A customer has been successfully creating and running multiple Amazon Elasticsearch Service (Amazon ES) domains to support their business users’ search needs across products, orders, support documentation, and a growing suite of similar needs. The service has become heavily used across the organization. This led to some domains running at 100% capacity during peak times, while others began to run low on storage space. Because of this increased usage, the technical teams were in danger of missing their service level agreements. They contacted me for help.
This post shows how you can set up automated alarms to warn when domains need attention.
Solution overview
Amazon ES is a fully managed service that delivers Elasticsearch’s easy-to-use APIs and real-time analytics capabilities along with the availability, scalability, and security that production workloads require. The service offers built-in integrations with a number of other components and AWS services, enabling customers to go from raw data to actionable insights quickly and securely.
One of these other integrated services is Amazon CloudWatch. CloudWatch is a monitoring service for AWS Cloud resources and the applications that you run on AWS. You can use CloudWatch to collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in your AWS resources.
CloudWatch collects metrics for Amazon ES. You can use these metrics to monitor the state of your Amazon ES domains, and set alarms to notify you about high utilization of system resources. For more information, see Amazon Elasticsearch Service Metrics and Dimensions.
While the metrics are automatically collected, the missing piece is how to set alarms on these metrics at appropriate levels for each of your domains. This post includes sample Python code to evaluate the current state of your Amazon ES environment, and to set up alarms according to AWS recommendations and best practices.
There are two components to the sample solution:
es-check-cwalarms.py: This Python script checks the CloudWatch alarms that have been set, for all Amazon ES domains in a given account and region.
es-create-cwalarms.py: This Python script sets up a set of CloudWatch alarms for a single given domain.
The sample code can also be found in the amazon-es-check-cw-alarms GitHub repo. The scripts are easy to extend or combine, as described in the section “Extensions and Adaptations”.
Assessing the current state
The first script, es-check-cwalarms.py, is used to give an overview of the configurations and alarm settings for all the Amazon ES domains in the given region. The script takes the following parameters:
python es-checkcwalarms.py -h
usage: es-checkcwalarms.py [-h] [-e ESPREFIX] [-n NOTIFY] [-f FREE][-p PROFILE] [-r REGION]
Checks a set of recommended CloudWatch alarms for Amazon Elasticsearch Service domains (optionally, those beginning with a given prefix).
optional arguments:
-h, --help show this help message and exit
-e ESPREFIX, --esprefix ESPREFIX Only check Amazon Elasticsearch Service domains that begin with this prefix.
-n NOTIFY, --notify NOTIFY List of CloudWatch alarm actions; e.g. ['arn:aws:sns:xxxx']
-f FREE, --free FREE Minimum free storage (MB) on which to alarm
-p PROFILE, --profile PROFILE IAM profile name to use
-r REGION, --region REGION AWS region for the domain. Default: us-east-1
The script first identifies all the domains in the given region (or, optionally, limits them to the subset that begins with a given prefix). It then starts running a set of checks against each one.
The script can be run from the command line or set up as a scheduled Lambda function. For example, for one customer, it was deemed appropriate to regularly run the script to check that alarms were correctly set for all domains. In addition, because configuration changes—cluster size increases to accommodate larger workloads being a common change—might require updates to alarms, this approach allowed the automatic identification of alarms no longer appropriately set as the domain configurations changed.
The output shown below is the output for one domain in my account.
Starting checks for Elasticsearch domain iotfleet , version is 53
Iotfleet Automated snapshot hour (UTC): 0
Iotfleet Instance configuration: 1 instances; type:m3.medium.elasticsearch
Iotfleet Instance storage definition is: 4 GB; free storage calced to: 819.2 MB
iotfleet Desired free storage set to (in MB): 819.2
iotfleet WARNING: Not using VPC Endpoint
iotfleet WARNING: Does not have Zone Awareness enabled
iotfleet WARNING: Instance count is ODD. Best practice is for an even number of data nodes and zone awareness.
iotfleet WARNING: Does not have Dedicated Masters.
iotfleet WARNING: Neither index nor search slow logs are enabled.
iotfleet WARNING: EBS not in use. Using instance storage only.
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-ClusterStatus.yellow-Alarm ClusterStatus.yellow
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-ClusterStatus.red-Alarm ClusterStatus.red
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-CPUUtilization-Alarm CPUUtilization
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-JVMMemoryPressure-Alarm JVMMemoryPressure
iotfleet WARNING: Missing alarm!! ('ClusterIndexWritesBlocked', 'Maximum', 60, 5, 'GreaterThanOrEqualToThreshold', 1.0)
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-AutomatedSnapshotFailure-Alarm AutomatedSnapshotFailure
iotfleet Alarm: Threshold does not match: Test-Elasticsearch-iotfleet-FreeStorageSpace-Alarm Should be: 819.2 ; is 3000.0
The output messages fall into the following categories:
System overview, Informational: The Amazon ES version and configuration, including instance type and number, storage, automated snapshot hour, etc.
Free storage: A calculation for the appropriate amount of free storage, based on the recommended 20% of total storage.
Warnings: best practices that are not being followed for this domain. (For more about this, read on.)
Alarms: An assessment of the CloudWatch alarms currently set for this domain, against a recommended set.
The script contains an array of recommended CloudWatch alarms, based on best practices for these metrics and statistics. Using the array allows alarm parameters (such as free space) to be updated within the code based on current domain statistics and configurations.
For a given domain, the script checks if each alarm has been set. If the alarm is set, it checks whether the values match those in the array esAlarms. In the output above, you can see three different situations being reported:
Alarm ok; definition matches. The alarm set for the domain matches the settings in the array.
Alarm: Threshold does not match. An alarm exists, but the threshold value at which the alarm is triggered does not match.
WARNING: Missing alarm!! The recommended alarm is missing.
All in all, the list above shows that this domain does not have a configuration that adheres to best practices, nor does it have all the recommended alarms.
Setting up alarms
Now that you know that the domains in their current state are missing critical alarms, you can correct the situation.
To demonstrate the script, set up a new domain named “ver”, in us-west-2. Specify 1 node, and a 10-GB EBS disk. Also, create an SNS topic in us-west-2 with a name of “sendnotification”, which sends you an email.
Run the second script, es-create-cwalarms.py, from the command line. This script creates (or updates) the desired CloudWatch alarms for the specified Amazon ES domain, “ver”.
python es-create-cwalarms.py -r us-west-2 -e test -c ver -n "['arn:aws:sns:us-west-2:xxxxxxxxxx:sendnotification']"
EBS enabled: True type: gp2 size (GB): 10 No Iops 10240 total storage (MB)
Desired free storage set to (in MB): 2048.0
Creating Test-Elasticsearch-ver-ClusterStatus.yellow-Alarm
Creating Test-Elasticsearch-ver-ClusterStatus.red-Alarm
Creating Test-Elasticsearch-ver-CPUUtilization-Alarm
Creating Test-Elasticsearch-ver-JVMMemoryPressure-Alarm
Creating Test-Elasticsearch-ver-FreeStorageSpace-Alarm
Creating Test-Elasticsearch-ver-ClusterIndexWritesBlocked-Alarm
Creating Test-Elasticsearch-ver-AutomatedSnapshotFailure-Alarm
Successfully finished creating alarms!
As with the first script, this script contains an array of recommended CloudWatch alarms, based on best practices for these metrics and statistics. This approach allows you to add or modify alarms based on your use case (more on that below).
After running the script, navigate to Alarms on the CloudWatch console. You can see the set of alarms set up on your domain.
Because the “ver” domain has only a single node, cluster status is yellow, and that alarm is in an “ALARM” state. It’s already sent a notification that the alarm has been triggered.
In most cases, the alarm triggers due to an increased workload. The likely action is to reconfigure the system to handle the increased workload, rather than reducing the incoming workload. Reconfiguring any backend store—a category of systems that includes Elasticsearch—is best performed when the system is quiescent or lightly loaded. Reconfigurations such as setting zone awareness or modifying the disk type cause Amazon ES to enter a “processing” state, potentially disrupting client access.
Other changes, such as increasing the number of data nodes, may cause Elasticsearch to begin moving shards, potentially impacting search performance on these shards while this is happening. These actions should be considered in the context of your production usage. For the same reason I also do not recommend running a script that resets all domains to match best practices.
Avoid the need to reconfigure during heavy workload by setting alarms at a level that allows a considered approach to making the needed changes. For example, if you identify that each weekly peak is increasing, you can reconfigure during a weekly quiet period.
While Elasticsearch can be reconfigured without being quiesced, it is not a best practice to automatically scale it up and down based on usage patterns. Unlike some other AWS services, I recommend against setting a CloudWatch action that automatically reconfigures the system when alarms are triggered.
There are other situations where the planned reconfiguration approach may not work, such as low or zero free disk space causing the domain to reject writes. If the business is dependent on the domain continuing to accept incoming writes and deleting data is not an option, the team may choose to reconfigure immediately.
Extensions and adaptations
You may wish to modify the best practices encoded in the scripts for your own environment or workloads. It’s always better to avoid situations where alerts are generated but routinely ignored. All alerts should trigger a review and one or more actions, either immediately or at a planned date. The following is a list of common situations where you may wish to set different alarms for different domains:
Dev/test vs. production You may have a different set of configuration rules and alarms for your dev environment configurations than for test. For example, you may require zone awareness and dedicated masters for your production environment, but not for your development domains. Or, you may not have any alarms set in dev. For test environments that mirror your potential peak load, test to ensure that the alarms are appropriately triggered.
Differing workloads or SLAs for different domains You may have one domain with a requirement for superfast search performance, and another domain with a heavy ingest load that tolerates slower search response. Your reaction to slow response for these two workloads is likely to be different, so perhaps the thresholds for these two domains should be set at a different level. In this case, you might add a “max CPU utilization” alarm at 100% for 1 minute for the fast search domain, while the other domain only triggers an alarm when the average has been higher than 60% for 5 minutes. You might also add a “free space” rule with a higher threshold to reflect the need for more space for the heavy ingest load if there is danger that it could fill the available disk quickly.
“Normal” alarms versus “emergency” alarms If, for example, free disk space drops to 25% of total capacity, an alarm is triggered that indicates action should be taken as soon as possible, such as cleaning up old indexes or reconfiguring at the next quiet period for this domain. However, if free space drops below a critical level (20% free space), action must be taken immediately in order to prevent Amazon ES from setting the domain to read-only. Similarly, if the “ClusterIndexWritesBlocked” alarm triggers, the domain has already stopped accepting writes, so immediate action is needed. In this case, you may wish to set “laddered” alarms, where one threshold causes an alarm to be triggered to review the current workload for a planned reconfiguration, but a different threshold raises a “DefCon 3” alarm that immediate action is required.
The sample scripts provided here are a starting point, intended for you to adapt to your own environment and needs.
Running the scripts one time can identify how far your current state is from your desired state, and create an initial set of alarms. Regularly re-running these scripts can capture changes in your environment over time and adjusting your alarms for changes in your environment and configurations. One customer has set them up to run nightly, and to automatically create and update alarms to match their preferred settings.
Removing unwanted alarms
Each CloudWatch alarm costs approximately $0.10 per month. You can remove unwanted alarms in the CloudWatch console, under Alarms. If you set up a “ver” domain above, remember to remove it to avoid continuing charges.
Conclusion
Setting CloudWatch alarms appropriately for your Amazon ES domains can help you avoid suboptimal performance and allow you to respond to workload growth or configuration issues well before they become urgent. This post gives you a starting point for doing so. The additional sleep you’ll get knowing you don’t need to be concerned about Elasticsearch domain performance will allow you to focus on building creative solutions for your business and solving problems for your customers.
Dr. Veronika Megler is a senior consultant at Amazon Web Services. She works with our customers to implement innovative big data, AI and ML projects, helping them accelerate their time-to-value when using AWS.
This is a guest post by Yukinori Koide, an the head of development for the Newspass department at Gunosy.
Gunosy is a news curation application that covers a wide range of topics, such as entertainment, sports, politics, and gourmet news. The application has been installed more than 20 million times.
Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users.
Users need fresh and personalized news. There are two constraints to consider when delivering appropriate articles:
Time: Articles have freshness—that is, they lose value over time. New articles need to reach users as soon as possible.
Frequency (volume): Only a limited number of articles can be shown. It’s unreasonable to display all articles in the application, and users can’t read all of them anyway.
To deliver fresh articles with a high probability that the user is interested in them, it’s necessary to include not only past user activity logs and some feature values of articles, but also the most recent (real-time) user activity logs.
We optimize the delivery of articles with these two steps.
Personalization: Deliver articles based on each user’s attributes, past activity logs, and feature values of each article—to account for each user’s interests.
Trends analysis/identification: Optimize delivering articles using recent (real-time) user activity logs—to incorporate the latest trends from all users.
Optimizing the delivery of articles is always a cold start. Initially, we deliver articles based on past logs. We then use real-time data to optimize as quickly as possible. In addition, news has a short freshness time. Specifically, day-old news is past news, and even the news that is three hours old is past news. Therefore, shortening the time between step 1 and step 2 is important.
To tackle this issue, we chose AWS for processing streaming data because of its fully managed services, cost-effectiveness, and so on.
Solution
The following diagrams depict the architecture for optimizing article delivery by processing real-time user activity logs
There are three processing flows:
Process real-time user activity logs.
Store and process all user-based and article-based logs.
Execute ad hoc or heavy queries.
In this post, I focus on the first processing flow and explain how it works.
Process real-time user activity logs
The following are the steps for processing user activity logs in real time using Kinesis Data Streams and Kinesis Data Analytics.
The Fluentd server sends the following user activity logs to Kinesis Data Streams:
b. Insert the joined source stream and application reference data source into the temporary stream.
CREATE OR REPLACE PUMP "TMP_PUMP" AS
INSERT INTO "TMP_SQL_STREAM"
SELECT STREAM
R.GENDER, R.SEGMENT_ID, S.ARTICLE_ID, S.ACTION
FROM "SOURCE_SQL_STREAM_001" S
LEFT JOIN "REFERENCE_DATA_SOURCE" R
ON S.USER_ID = R.USER_ID;
c. Define the destination stream named DESTINATION_SQL_STREAM.
CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
TIME TIMESTAMP, GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER,
IMPRESSION INTEGER, CLICK INTEGER
);
d. Insert the processed temporary stream, using a tumbling window, into the destination stream per minute.
CREATE OR REPLACE PUMP "STREAM_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
ROW_TIME AS TIME,
GENDER, SEGMENT_ID, ARTICLE_ID,
SUM(CASE ACTION WHEN 'impression' THEN 1 ELSE 0 END) AS IMPRESSION,
SUM(CASE ACTION WHEN 'click' THEN 1 ELSE 0 END) AS CLICK
FROM "TMP_SQL_STREAM"
GROUP BY
GENDER, SEGMENT_ID, ARTICLE_ID,
FLOOR("TMP_SQL_STREAM".ROWTIME TO MINUTE);
Batch servers get results from Amazon ES every minute. They then optimize delivering articles with other data sources using a proprietary optimization algorithm.
How to connect a stream to another stream in another AWS Region
When we built the solution, Kinesis Data Analytics was not available in the Asia Pacific (Tokyo) Region, so we used the US West (Oregon) Region. The following shows how we connected a data stream to another data stream in the other Region.
There is no need to continue containing all components in a single AWS Region, unless you have a situation where a response difference at the millisecond level is critical to the service.
Benefits
The solution provides benefits for both our company and for our users. Benefits for the company are cost savings—including development costs, operational costs, and infrastructure costs—and reducing delivery time. Users can now find articles of interest more quickly. The solution can process more than 500,000 records per minute, and it enables fast and personalized news curating for our users.
Conclusion
In this post, I showed you how we optimize trending user activities to personalize news using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services in Gunosy.
AWS gives us a quick and economical solution and a good experience.
If you have questions or suggestions, please comment below.
Yukinori Koide is the head of development for the Newspass department at Gunosy. He is working on standardization of provisioning and deployment flow, promoting the utilization of serverless and containers for machine learning and AI services. His favorite AWS services are DynamoDB, Lambda, Kinesis, and ECS.
Akihiro Tsukada is a start-up solutions architect with AWS. He supports start-up companies in Japan technically at many levels, ranging from seed to later-stage.
Yuta Ishii is a solutions architect with AWS. He works with our customers to provide architectural guidance for building media & entertainment services, helping them improve the value of their services when using AWS.
The following 10 posts were the most viewed AWS Security Blog posts that we published during 2017. You can use this list as a guide to catch up on your AWS Security Blog reading or read a post again that you found particularly useful.
Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.
The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.
All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.
AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.
From Our Customers Many AWS customers are preparing to use this new Region. Here’s a small sample:
Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.
SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.
Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.
Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.
AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.
AWS Consulting and Technology Partners We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:
AWS in France We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.
As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.
Use it Today The EU (Paris) Region is open for business now and you can start using it today!
In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.
With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.
Push vs. Pull data ingestion
Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:
Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.
The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.
On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.
How about getting the best of both worlds?
Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.
By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.
You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.
You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.
Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.
In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).
Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.
How-to guide
To process VPC flow logs, we implement the following architecture.
Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.
Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).
If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.
When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.
Walkthrough
Install the Splunk Add-on for Amazon Kinesis Data Firehose
The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.
HTTP Event Collector (HEC)
Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.
When prompted to select a source type, select aws:cloudwatch:vpcflow.
Create an S3 backsplash bucket
To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.
Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.
Create an IAM role for the Lambda transform function
Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.
Note: You can also set this role up when creating your Lambda function.
$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json
After the role is created, attach the managed Lambda basic execution policy to it.
$ aws iam attach-role-policy
--policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
--role-name LambdaBasicRole
Create a Firehose Stream
On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.
In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.
From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.
Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.
Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.
Select Splunk as the destination, and enter your Splunk Http Event Collector information.
Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.
In this example, we only back up logs that fail during delivery.
To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.
Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.
If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.
You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.
Create a VPC Flow Log
To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.
On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.
Once active, your VPC flow log should look like the following.
Publish CloudWatch to Kinesis Data Firehose
When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.
At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.
To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.
$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json
Here is the content for TrustPolicyForCWLToFireHose.json.
When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.
As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.
In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.
Conclusion
Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.
Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.
Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.
Introduced at AWS re:Invent 2017, Amazon GuardDuty is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. In an AWS Blog post, Jeff Barr shows you how to enable GuardDuty to monitor your AWS resources continuously. That blog post shows how to get started with a single GuardDuty account and provides an overview of the features of the service. Your security team, though, will probably want to use GuardDuty to monitor a group of AWS accounts continuously.
In this post, I demonstrate how to use GuardDuty to monitor a group of AWS accounts and have their findings routed to another AWS account—the master account—that is owned by a security team. The method I demonstrate in this post is especially useful if your security team is responsible for monitoring a group of AWS accounts over which it does not have direct access—known as member accounts. In this solution, I simplify the work needed to enable GuardDuty in member accounts and configure findings by simplifying the process, which I do by enabling GuardDuty in the master account and inviting member accounts.
Enable GuardDuty in a master account and invite member accounts
To get started, you must enable GuardDuty in the master account, which will receive GuardDuty findings. The master account should be managed by your security team, and it will display the findings from all member accounts. The master account can be reverted later by removing any member accounts you add to it. Adding member accounts is a two-way handshake mechanism to ensure that administrators from both the master and member accounts formally agree to establish the relationship.
To enable GuardDuty in the master account and add member accounts:
To designate this account as the GuardDuty master account, start adding member accounts:
You can add individual accounts by choosing Add Account, or you can add a list of accounts by choosing Upload List (.csv).
Now, add the account ID and email address of the member account, and choose Add. (If you are uploading a list of accounts, choose Browse, choose the .csv file with the member accounts [one email address and account ID per line], and choose Add accounts.)
For security reasons, AWS checks to make sure each account ID is valid and that you’ve entered each member account’s email address that was used to create the account. If a member account’s account ID and email address do not match, GuardDuty does not send an invitation.
After you add all the member accounts you want to add, you will see them listed in the Member accounts table with a Status of Invite. You don’t have to individually invite each account—you can choose a group of accounts and when you choose to invite one account in the group, all accounts are invited.
When you choose Invite for each member account:
AWS checks to make sure the account ID is valid and the email address provided is the email address of the member account.
AWS sends an email to the member account email address with a link to the GuardDuty console, where the member account owner can accept the invitation. You can add a customized message from your security team. Account owners who receive the invitation must sign in to their AWS account to accept the invitation. The service also sends an invitation through the AWS Personal Health Dashboard in case the member email address is not monitored. This invitation appears in the member account under the AWS Personal Health Dashboard alert bell on the AWS Management Console.
A pending-invitation indicator is shown on the GuardDuty console of the member account, as shown in the following screenshot.
When the invitation is sent by email, it is sent to the account owner of the GuardDuty member account.
The account owner can click the link in the email invitation or the AWS Personal Health Dashboard message, or the account owner can sign in to their account and navigate to the GuardDuty console. In all cases, the member account displays the pending invitation in the member account’s GuardDuty console with instructions for accepting the invitation. The GuardDuty console walks the account owner through accepting the invitation, including enabling GuardDuty if it is not already enabled.
If you prefer to work in the AWS CLI, you can enable GuardDuty and accept the invitation. To do this, call CreateDetector to enable GuardDuty, and then call AcceptInvitation, which serves the same purpose as accepting the invitation in the GuardDuty console.
After the member account owner accepts the invitation, the Status in the master account is changed to Monitored. The status helps you track the status of each AWS account that you invite.
You have enabled GuardDuty on the member account, and all findings will be forwarded to the master account. You can now monitor the findings about GuardDuty member accounts from the GuardDuty console in the master account.
The member account owner can see GuardDuty findings by default and can control all aspects of the experience in the member account with AWS Identity and Access Management (IAM) permissions. Users with the appropriate permissions can end the multi-account relationship at any time by toggling the Accept button on the Accounts page. Note that ending the relationship changes the Status of the account to Resigned and also triggers a security finding on the side of the master account so that the security team knows the member account is no longer linked to the master account.
Working with GuardDuty findings
Most security teams have ticketing systems, chat operations, security information event management (SIEM) systems, or other security automation systems to which they would like to push GuardDuty findings. For this purpose, GuardDuty sends all findings as JSON-based messages through Amazon CloudWatch Events, a scalable service to which you can subscribe and to which AWS services can stream system events. To access these events, navigate to the CloudWatch Events console and create a rule that subscribes to the GuardDuty-related findings. You then can assign a target such as Amazon Kinesis Data Firehose that can place the findings in a number of services such as Amazon S3. The following screenshot is of the CloudWatch Events console, where I have a rule that pulls all events from GuardDuty and pushes them to a preconfigured AWS Lambda function.
The following example is a subset of GuardDuty findings that includes relevant context and information about the nature of a threat that was detected. In this example, the instanceId, i-00bb62b69b7004a4c, is performing Secure Shell (SSH) brute-force attacks against IP address 172.16.0.28. From a Lambda function, you can access any of the following fields such as the title of the finding and its description, and send those directly to your ticketing system.
You can use other AWS services to build custom analytics and visualizations of your security findings. For example, you can connect Kinesis Data Firehose to CloudWatch Events and write events to an S3 bucket in a standard format, which can be encrypted with AWS Key Management Service and then compressed. You also can use Amazon QuickSight to build ad hoc dashboards by using AWS Glue and Amazon Athena. Similarly, you can place the data from Kinesis Data Firehose in Amazon Elasticsearch Service, with which you can use tools such as Kibana to build your own visualizations and dashboards.
Like most other AWS services, GuardDuty is a regional service. This means that when you enable GuardDuty in an AWS Region, all findings are generated and delivered in that region. If you are regulated by a compliance regime, this is often an important requirement to ensure that security findings remain in a specific jurisdiction. Because customers have let us know they would prefer to be able to enable GuardDuty globally and have all findings aggregated in one place, we intend to give the choice of regional or global isolation as we evolve this new service.
Summary
In this blog post, I have demonstrated how to use GuardDuty to monitor a group of GuardDuty member accounts and aggregate security findings in a central master GuardDuty account. You can use this solution whether or not you have direct control over the member accounts.
If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about using GuardDuty, start a thread in the GuardDuty forum or contact AWS Support.
Today we launched our 17th Region globally, and the second in China. The AWS China (Ningxia) Region, operated by Ningxia Western Cloud Data Technology Co. Ltd. (NWCD), is generally available now and provides customers another option to run applications and store data on AWS in China.
Operating Partner To comply with China’s legal and regulatory requirements, AWS has formed a strategic technology collaboration with NWCD to operate and provide services from the AWS China (Ningxia) Region. Founded in 2015, NWCD is a licensed datacenter and cloud services provider, based in Ningxia, China. NWCD joins Sinnet, the operator of the AWS China China (Beijing) Region, as an AWS operating partner in China. Through these relationships, AWS provides its industry-leading technology, guidance, and expertise to NWCD and Sinnet, while NWCD and Sinnet operate and provide AWS cloud services to local customers. While the cloud services offered in both AWS China Regions are the same as those available in other AWS Regions, the AWS China Regions are different in that they are isolated from all other AWS Regions and operated by AWS’s Chinese partners separately from all other AWS Regions. Customers using the AWS China Regions enter into customer agreements with Sinnet and NWCD, rather than with AWS.
Use it Today The AWS China (Ningxia) Region, operated by NWCD, is open for business, and you can start using it now! Starting today, Chinese developers, startups, and enterprises, as well as government, education, and non-profit organizations, can leverage AWS to run their applications and store their data in the new AWS China (Ningxia) Region, operated by NWCD. Customers already using the AWS China (Beijing) Region, operated by Sinnet, can select the AWS China (Ningxia) Region directly from the AWS Management Console, while new customers can request an account at www.amazonaws.cn to begin using both AWS China Regions.
In this day and age, it is almost impossible to do without our mobile devices and the applications that help make our lives easier. As our dependency on our mobile phone grows, the mobile application market has exploded with millions of apps vying for our attention. For mobile developers, this means that we must ensure that we build applications that provide the quality, real-time experiences that app users desire. Therefore, it has become essential that mobile applications are developed to include features such as multi-user data synchronization, offline network support, and data discovery, just to name a few. According to several articles, I read recently about mobile development trends on publications like InfoQ, DZone, and the mobile development blog AlleviateTech, one of the key elements in of delivering the aforementioned capabilities is with cloud-driven mobile applications. It seems that this is especially true, as it related to mobile data synchronization and data storage.
That being the case, it is a perfect time for me to announce a new service for building innovative mobile applications that are driven by data-intensive services in the cloud; AWS AppSync. AWS AppSync is a fully managed serverless GraphQL service for real-time data queries, synchronization, communications and offline programming features. For those not familiar, let me briefly share some information about the open GraphQL specification. GraphQL is a responsive data query language and server-side runtime for querying data sources that allow for real-time data retrieval and dynamic query execution. You can use GraphQL to build a responsive API for use in when building client applications. GraphQL works at the application layer and provides a type system for defining schemas. These schemas serve as specifications to define how operations should be performed on the data and how the data should be structured when retrieved. Additionally, GraphQL has a declarative coding model which is supported by many client libraries and frameworks including React, React Native, iOS, and Android.
Now the power of the GraphQL open standard query language is being brought to you in a rich managed service with AWS AppSync. With AppSync developers can simplify the retrieval and manipulation of data across multiple data sources with ease, allowing them to quickly prototype, build and create robust, collaborative, multi-user applications. AppSync keeps data updated when devices are connected, but enables developers to build solutions that work offline by caching data locally and synchronizing local data when connections become available.
Let’s discuss some key concepts of AWS AppSync and how the service works.
AppSync Concepts
AWS AppSync Client: service client that defines operations, wraps authorization details of requests, and manage offline logic.
Data Source: the data storage system or a trigger housing data
Identity: a set of credentials with permissions and identification context provided with requests to GraphQL proxy
GraphQL Proxy: the GraphQL engine component for processing and mapping requests, handling conflict resolution, and managing Fine Grained Access Control
Operation: one of three GraphQL operations supported in AppSync
Query: a read-only fetch call to the data
Mutation: a write of the data followed by a fetch,
Subscription: long-lived connections that receive data in response to events.
Action: a notification to connected subscribers from a GraphQL subscription.
Resolver: function using request and response mapping templates that converts and executes payload against data source
How It Works
A schema is created to define types and capabilities of the desired GraphQL API and tied to a Resolver function. The schema can be created to mirror existing data sources or AWS AppSync can create tables automatically based the schema definition. Developers can also use GraphQL features for data discovery without having knowledge of the backend data sources. After a schema definition is established, an AWS AppSync client can be configured with an operation request, like a Query operation. The client submits the operation request to GraphQL Proxy along with an identity context and credentials. The GraphQL Proxy passes this request to the Resolver which maps and executes the request payload against pre-configured AWS data services like an Amazon DynamoDB table, an AWS Lambda function, or a search capability using Amazon Elasticsearch. The Resolver executes calls to one or all of these services within a single network call minimizing CPU cycles and bandwidth needs and returns the response to the client. Additionally, the client application can change data requirements in code on demand and the AppSync GraphQL API will dynamically map requests for data accordingly, allowing prototyping and faster development.
In order to take a quick peek at the service, I’ll go to the AWS AppSync console. I’ll click the Create API button to get started.
When the Create new API screen opens, I’ll give my new API a name, TarasTestApp, and since I am just exploring the new service I will select the Sample schema option. You may notice from the informational dialog box on the screen that in using the sample schema, AWS AppSync will automatically create the DynamoDB tables and the IAM roles for me.It will also deploy the TarasTestApp API on my behalf. After review of the sample schema provided by the console, I’ll click the Create button to create my test API.
After the TaraTestApp API has been created and the associated AWS resources provisioned on my behalf, I can make updates to the schema, data source, or connect my data source(s) to a resolver. I also can integrate my GraphQL API into an iOS, Android, Web, or React Native application by cloning the sample repo from GitHub and downloading the accompanying GraphQL schema. These application samples are great to help get you started and they are pre-configured to function in offline scenarios.
If I select the Schema menu option on the console, I can update and view the TarasTestApp GraphQL API schema.
Additionally, if I select the Data Sources menu option in the console, I can see the existing data sources. Within this screen, I can update, delete, or add data sources if I so desire.
Next, I will select the Query menu option which takes me to the console tool for writing and testing queries. Since I chose the sample schema and the AWS AppSync service did most of the heavy lifting for me, I’ll try a query against my new GraphQL API.
I’ll use a mutation to add data for the event type in my schema. Since this is a mutation and it first writes data and then does a read of the data, I want the query to return values for name and where.
If I go to the DynamoDB table created for the event type in the schema, I will see that the values from my query have been successfully written into the table. Now that was a pretty simple task to write and retrieve data based on a GraphQL API schema from a data source, don’t you think.
Summary
AWS AppSync is currently in AWS AppSync is in Public Preview and you can sign up today. It supports development for iOS, Android, and JavaScript applications. You can take advantage of this managed GraphQL service by going to the AWSAppSync console or learn more by reviewing more details about the service by reading a tutorial in the AWS documentation for the service or checking out our AWS AppSync Developer Guide.
We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. As in previous years, you can find these sessions in various tracks, including Analytics & Big Data, Deep Learning Summit, Artificial Intelligence & Machine Learning, Architecture, and Databases.
We have great sessions from organizations and companies like Vanguard, Cox Automotive, Pinterest, Netflix, FINRA, Amtrak, AmazonFresh, Sysco Foods, Twilio, American Heart Association, Expedia, Esri, Nextdoor, and many more. All sessions are recorded and made available on YouTube. In addition, all slide decks from the sessions will be available on SlideShare.net after the conference.
This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT. If you’re interested in Machine Learning sessions, don’t forget to check out our Guide to Machine Learning at re:Invent 2017.
This year’s session catalog contains the following breakout sessions.
Raju Gulabani, VP, Database, Analytics and AI at AWS will discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.
Deep dive customer use cases
ABD401 – How Netflix Monitors Applications in Near Real-Time with Amazon Kinesis Thousands of services work in concert to deliver millions of hours of video streams to Netflix customers every day. These applications vary in size, function, and technology, but they all make use of the Netflix network to communicate. Understanding the interactions between these services is a daunting challenge both because of the sheer volume of traffic and the dynamic nature of deployments. In this session, we first discuss why Netflix chose Kinesis Streams to address these challenges at scale. We then dive deep into how Netflix uses Kinesis Streams to enrich network traffic logs and identify usage patterns in real time. Lastly, we cover how Netflix uses this system to build comprehensive dependency maps, increase network efficiency, and improve failure resiliency. From this session, you will learn how to build a real-time application monitoring system using network traffic logs and get real-time, actionable insights.
In this session, learn how Nextdoor replaced their home-grown data pipeline based on a topology of Flume nodes with a completely serverless architecture based on Kinesis and Lambda. By making these changes, they improved both the reliability of their data and the delivery times of billions of records of data to their Amazon S3–based data lake and Amazon Redshift cluster. Nextdoor is a private social networking service for neighborhoods.
ABD205 – Taking a Page Out of Ivy Tech’s Book: Using Data for Student Success Data speaks. Discover how Ivy Tech, the nation’s largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech’s model can be applied to your own complex business problems.
ABD207 – Leveraging AWS to Fight Financial Crime and Protect National Security Banks aren’t known to share data and collaborate with one another. But that is exactly what the Mid-Sized Bank Coalition of America (MBCA) is doing to fight digital financial crime—and protect national security. Using the AWS Cloud, the MBCA developed a shared data analytics utility that processes terabytes of non-competitive customer account, transaction, and government risk data. The intelligence produced from the data helps banks increase the efficiency of their operations, cut labor and operating costs, and reduce false positive volumes. The collective intelligence also allows greater enforcement of Anti-Money Laundering (AML) regulations by helping members detect internal risks—and identify the challenges to detecting these risks in the first place. This session demonstrates how the AWS Cloud supports the MBCA to deliver advanced data analytics, provide consistent operating models across financial institutions, reduce costs, and strengthen national security.
ABD208 – Cox Automotive Empowered to Scale with Splunk Cloud & AWS and Explores New Innovation with Amazon Kinesis Firehose In this session, learn how Cox Automotive is using Splunk Cloud for real time visibility into its AWS and hybrid environments to achieve near instantaneous MTTI, reduce auction incidents by 90%, and proactively predict outages. We also introduce a highly anticipated capability that allows you to ingest, transform, and analyze data in real time using Splunk and Amazon Kinesis Firehose to gain valuable insights from your cloud resources. It’s now quicker and easier than ever to gain access to analytics-driven infrastructure monitoring using Splunk Enterprise & Splunk Cloud.
ABD209 – Accelerating the Speed of Innovation with a Data Sciences Data & Analytics Hub at Takeda Historically, silos of data, analytics, and processes across functions, stages of development, and geography created a barrier to R&D efficiency. Gathering the right data necessary for decision-making was challenging due to issues of accessibility, trust, and timeliness. In this session, learn how Takeda is undergoing a transformation in R&D to increase the speed-to-market of high-impact therapies to improve patient lives. The Data and Analytics Hub was built, with Deloitte, to address these issues and support the efficient generation of data insights for functions such as clinical operations, clinical development, medical affairs, portfolio management, and R&D finance. In the AWS hosted data lake, this data is processed, integrated, and made available to business end users through data visualization interfaces, and to data scientists through direct connectivity. Learn how Takeda has achieved significant time reductions—from weeks to minutes—to gather and provision data that has the potential to reduce cycle times in drug development. The hub also enables more efficient operations and alignment to achieve product goals through cross functional team accountability and collaboration due to the ability to access the same cross domain data.
ABD210 – Modernizing Amtrak: Serverless Solution for Real-Time Data Capabilities As the nation’s only high-speed intercity passenger rail provider, Amtrak needs to know critical information to run their business such as: Who’s onboard any train at any time? How are booking and revenue trending? Amtrak was faced with unpredictable and often slow response times from existing databases, ranging from seconds to hours; existing booking and revenue dashboards were spreadsheet-based and manual; multiple copies of data were stored in different repositories, lacking integration and consistency; and operations and maintenance (O&M) costs were relatively high. Join us as we demonstrate how Deloitte and Amtrak successfully went live with a cloud-native operational database and analytical datamart for near-real-time reporting in under six months. We highlight the specific challenges and the modernization of architecture on an AWS native Platform as a Service (PaaS) solution. The solution includes cloud-native components such as AWS Lambda for microservices, Amazon Kinesis and AWS Data Pipeline for moving data, Amazon S3 for storage, Amazon DynamoDB for a managed NoSQL database service, and Amazon Redshift for near-real time reports and dashboards. Deloitte’s solution enabled “at scale” processing of 1 million transactions/day and up to 2K transactions/minute. It provided flexibility and scalability, largely eliminate the need for system management, and dramatically reduce operating costs. Moreover, it laid the groundwork for decommissioning legacy systems, anticipated to save at least $1M over 3 years.
ABD211 – Sysco Foods: A Journey from Too Much Data to Curated Insights In this session, we detail Sysco’s journey from a company focused on hindsight-based reporting to one focused on insights and foresight. For this shift, Sysco moved from multiple data warehouses to an AWS ecosystem, including Amazon Redshift, Amazon EMR, AWS Data Pipeline, and more. As the team at Sysco worked with Tableau, they gained agile insight across their business. Learn how Sysco decided to use AWS, how they scaled, and how they became more strategic with the AWS ecosystem and Tableau.
ABD217 – From Batch to Streaming: How Amazon Flex Uses Real-time Analytics to Deliver Packages on Time Reducing the time to get actionable insights from data is important to all businesses, and customers who employ batch data analytics tools are exploring the benefits of streaming analytics. Learn best practices to extend your architecture from data warehouses and databases to real-time solutions. Learn how to use Amazon Kinesis to get real-time data insights and integrate them with Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon S3. The Amazon Flex team describes how they used streaming analytics in their Amazon Flex mobile app, used by Amazon delivery drivers to deliver millions of packages each month on time. They discuss the architecture that enabled the move from a batch processing system to a real-time system, overcoming the challenges of migrating existing batch data to streaming data, and how to benefit from real-time analytics.
ABD218 – How EuroLeague Basketball Uses IoT Analytics to Engage Fans IoT and big data have made their way out of industrial applications, general automation, and consumer goods, and are now a valuable tool for improving consumer engagement across a number of industries, including media, entertainment, and sports. The low cost and ease of implementation of AWS analytics services and AWS IoT have allowed AGT, a leader in IoT, to develop their IoTA analytics platform. Using IoTA, AGT brought a tailored solution to EuroLeague Basketball for real-time content production and fan engagement during the 2017-18 season. In this session, we take a deep dive into how this solution is architected for secure, scalable, and highly performant data collection from athletes, coaches, and fans. We also talk about how the data is transformed into insights and integrated into a content generation pipeline. Lastly, we demonstrate how this solution can be easily adapted for other industries and applications.
ABD222 – How to Confidently Unleash Data to Meet the Needs of Your Entire Organization Where are you on the spectrum of IT leaders? Are you confident that you’re providing the technology and solutions that consistently meet or exceed the needs of your internal customers? Do your peers at the executive table see you as an innovative technology leader? Innovative IT leaders understand the value of getting data and analytics directly into the hands of decision makers, and into their own. In this session, Daren Thayne, Domo’s Chief Technology Officer, shares how innovative IT leaders are helping drive a culture change at their organizations. See how transformative it can be to have real-time access to all of the data that’ is relevant to YOUR job (including a complete view of your entire AWS environment), as well as understand how it can help you lead the way in applying that same pattern throughout your entire company
ABD303 – Developing an Insights Platform – Sysco’s Journey from Disparate Systems to Data Lake and Beyond Sysco has nearly 200 operating companies across its multiple lines of business throughout the United States, Canada, Central/South America, and Europe. As the global leader in food services, Sysco identified the need to streamline the collection, transformation, and presentation of data produced by the distributed units and systems, into a central data ecosystem. Sysco’s Business Intelligence and Analytics team addressed these requirements by creating a data lake with scalable analytics and query engines leveraging AWS services. In this session, Sysco will outline their journey from a hindsight reporting focused company to an insights driven organization. They will cover solution architecture, challenges, and lessons learned from deploying a self-service insights platform. They will also walk through the design patterns they used and how they designed the solution to provide predictive analytics using Amazon Redshift Spectrum, Amazon S3, Amazon EMR, AWS Glue, Amazon Elasticsearch Service and other AWS services.
ABD309 – How Twilio Scaled Its Data-Driven Culture As a leading cloud communications platform, Twilio has always been strongly data-driven. But as headcount and data volumes grew—and grew quickly—they faced many new challenges. One-off, static reports work when you’re a small startup, but how do you support a growth stage company to a successful IPO and beyond? Today, Twilio’s data team relies on AWS and Looker to provide data access to 700 colleagues. Departments have the data they need to make decisions, and cloud-based scale means they get answers fast. Data delivers real-business value at Twilio, providing a 360-degree view of their customer, product, and business. In this session, you hear firsthand stories directly from the Twilio data team and learn real-world tips for fostering a truly data-driven culture at scale.
ABD310 – How FINRA Secures Its Big Data and Data Science Platform on AWS FINRA uses big data and data science technologies to detect fraud, market manipulation, and insider trading across US capital markets. As a financial regulator, FINRA analyzes highly sensitive data, so information security is critical. Learn how FINRA secures its Amazon S3 Data Lake and its data science platform on Amazon EMR and Amazon Redshift, while empowering data scientists with tools they need to be effective. In addition, FINRA shares AWS security best practices, covering topics such as AMI updates, micro segmentation, encryption, key management, logging, identity and access management, and compliance.
ABD331 – Log Analytics at Expedia Using Amazon Elasticsearch Service Expedia uses Amazon Elasticsearch Service (Amazon ES) for a variety of mission-critical use cases, ranging from log aggregation to application monitoring and pricing optimization. In this session, the Expedia team reviews how they use Amazon ES and Kibana to analyze and visualize Docker startup logs, AWS CloudTrail data, and application metrics. They share best practices for architecting a scalable, secure log analytics solution using Amazon ES, so you can add new data sources almost effortlessly and get insights quickly
ABD316 – American Heart Association: Finding Cures to Heart Disease Through the Power of Technology Combining disparate datasets and making them accessible to data scientists and researchers is a prevalent challenge for many organizations, not just in healthcare research. American Heart Association (AHA) has built a data science platform using Amazon EMR, Amazon Elasticsearch Service, and other AWS services, that corrals multiple datasets and enables advanced research on phenotype and genotype datasets, aimed at curing heart diseases. In this session, we present how AHA built this platform and the key challenges they addressed with the solution. We also provide a demo of the platform, and leave you with suggestions and next steps so you can build similar solutions for your use cases
ABD319 – Tooling Up for Efficiency: DIY Solutions @ Netflix At Netflix, we have traditionally approached cloud efficiency from a human standpoint, whether it be in-person meetings with the largest service teams or manually flipping reservations. Over time, we realized that these manual processes are not scalable as the business continues to grow. Therefore, in the past year, we have focused on building out tools that allow us to make more insightful, data-driven decisions around capacity and efficiency. In this session, we discuss the DIY applications, dashboards, and processes we built to help with capacity and efficiency. We start at the ten thousand foot view to understand the unique business and cloud problems that drove us to create these products, and discuss implementation details, including the challenges encountered along the way. Tools discussed include Picsou, the successor to our AWS billing file cost analyzer; Libra, an easy-to-use reservation conversion application; and cost and efficiency dashboards that relay useful financial context to 50+ engineering teams and managers.
ABD312 – Deep Dive: Migrating Big Data Workloads to AWS Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.
ABD402 – How Esri Optimizes Massive Image Archives for Analytics in the Cloud Petabyte scale archives of satellites, planes, and drones imagery continue to grow exponentially. They mostly exist as semi-structured data, but they are only valuable when accessed and processed by a wide range of products for both visualization and analysis. This session provides an overview of how ArcGIS indexes and structures data so that any part of it can be quickly accessed, processed, and analyzed by reading only the minimum amount of data needed for the task. In this session, we share best practices for structuring and compressing massive datasets in Amazon S3, so it can be analyzed efficiently. We also review a number of different image formats, including GeoTIFF (used for the Public Datasets on AWS program, Landsat on AWS), cloud optimized GeoTIFF, MRF, and CRF as well as different compression approaches to show the effect on processing performance. Finally, we provide examples of how this technology has been used to help image processing and analysis for the response to Hurricane Harvey.
ABD329 – A Look Under the Hood – How Amazon.com Uses AWS Services for Analytics at Massive Scale Amazon’s consumer business continues to grow, and so does the volume of data and the number and complexity of the analytics done in support of the business. In this session, we talk about how Amazon.com uses AWS technologies to build a scalable environment for data and analytics. We look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel, scalable compute engines such as Amazon EMR and Amazon Redshift.
ABD327 – Migrating Your Traditional Data Warehouse to a Modern Data Lake In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base. MCL202 – Ally Bank & Cognizant: Transforming Customer Experience Using Amazon Alexa Given the increasing popularity of natural language interfaces such as Voice as User technology or conversational artificial intelligence (AI), Ally® Bank was looking to interact with customers by enabling direct transactions through conversation or voice. They also needed to develop a capability that allows third parties to connect to the bank securely for information sharing and exchange, using oAuth, an authentication protocol seen as the future of secure banking technology. Cognizant’s Architecture team partnered with Ally Bank’s Enterprise Architecture group and identified the right product for oAuth integration with Amazon Alexa and third-party technologies. In this session, we discuss how building products with conversational AI helps Ally Bank offer an innovative customer experience; increase retention through improved data-driven personalization; increase the efficiency and convenience of customer service; and gain deep insights into customer needs through data analysis and predictive analytics to offer new products and services.
MCL317 – Orchestrating Machine Learning Training for Netflix Recommendations At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora
MCL350 – Humans vs. the Machines: How Pinterest Uses Amazon Mechanical Turk’s Worker Community to Improve Machine Learning Ever since the term “crowdsourcing” was coined in 2006, it’s been a buzzword for technology companies and social institutions. In the technology sector, crowdsourcing is instrumental for verifying machine learning algorithms, which, in turn, improves the user’s experience. In this session, we explore how Pinterest adapted to an increased reliability on human evaluation to improve their product, with a focus on how they’ve integrated with Mechanical Turk’s platform. This presentation is aimed at engineers, analysts, program managers, and product managers who are interested in how companies rely on Mechanical Turk’s human evaluation platform to better understand content and improve machine learning algorithms. The discussion focuses on the analysis and product decisions related to building a high quality crowdsourcing system that takes advantage of Mechanical Turk’s powerful worker community.
ABD201 – Big Data Architectural Patterns and Best Practices on AWS In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost
ABD202 – Best Practices for Building Serverless Big Data Applications Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.
ABD206 – Building Visualizations and Dashboards with Amazon QuickSight Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.
ABD203 – Real-Time Streaming Applications on AWS: Use Cases and Patterns To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.
ABD213 – How to Build a Data Lake with AWS Glue Data Catalog As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
ABD214 – Real-time User Insights for Mobile and Web Applications with Amazon Pinpoint With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.
ABD223 – IT Innovators: New Technology for Leveraging Data to Enable Agility, Innovation, and Business Optimization Companies of all sizes are looking for technology to efficiently leverage data and their existing IT investments to stay competitive and understand where to find new growth. Regardless of where companies are in their data-driven journey, they face greater demands for information by customers, prospects, partners, vendors and employees. All stakeholders inside and outside the organization want information on-demand or in “real time”, available anywhere on any device. They want to use it to optimize business outcomes without having to rely on complex software tools or human gatekeepers to relevant information. Learn how IT innovators at companies such as MasterCard, Jefferson Health, and TELUS are using Domo’s Business Cloud to help their organizations more effectively leverage data at scale.
ABD301 – Analyzing Streaming Data in Real Time with Amazon Kinesis Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system
ABD302 – Real-Time Data Exploration and Analytics with Amazon Elasticsearch Service and Kibana In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution. First, we cover how to configure an Amazon ES cluster and ingest data using Amazon Kinesis Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data. Then we demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we review approaches for generating custom, ad-hoc reports.
ABD304 – Best Practices for Data Warehousing with Amazon Redshift & Redshift Spectrum Most companies are over-run with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we take an in-depth look at how modern data warehousing blends and analyzes all your data, inside and outside your data warehouse without moving the data, to give you deeper insights to run your business. We will cover best practices on how to design optimal schemas, load data efficiently, and optimize your queries to deliver high throughput and performance.
ABD305 – Design Patterns and Best Practices for Data Analytics with Amazon EMR Amazon EMR is one of the largest Hadoop operators in the world, enabling customers to run ETL, machine learning, real-time processing, data science, and low-latency SQL at petabyte scale. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about lowering cost with Auto Scaling and Spot Instances, and security best practices for encryption and fine-grained access control. Finally, we dive into some of our recent launches to keep you current on our latest features.
ABD307 – Deep Analytics for Global AWS Marketing Organization To meet the needs of the global marketing organization, the AWS marketing analytics team built a scalable platform that allows the data science team to deliver custom econometric and machine learning models for end user self-service. To meet data security standards, we use end-to-end data encryption and different AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon EMR with Apache Spark and Auto Scaling. In this session, you see real examples of how we have scaled and automated critical analysis, such as calculating the impact of marketing programs like re:Invent and prioritizing leads for our sales teams.
ABD311 – Deploying Business Analytics at Enterprise Scale with Amazon QuickSight One of the biggest tradeoffs customers usually make when deploying BI solutions at scale is agility versus governance. Large-scale BI implementations with the right governance structure can take months to design and deploy. In this session, learn how you can avoid making this tradeoff using Amazon QuickSight. Learn how to easily deploy Amazon QuickSight to thousands of users using Active Directory and Federated SSO, while securely accessing your data sources in Amazon VPCs or on-premises. We also cover how to control access to your datasets, implement row-level security, create scheduled email reports, and audit access to your data.
ABD315 – Building Serverless ETL Pipelines with AWS Glue Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
ABD318 – Architecting a data lake with Amazon S3, Amazon Kinesis, and Amazon Athena Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users – from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways. In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users.
Companies have valuable data that they may not be analyzing due to the complexity, scalability, and performance issues of loading the data into their data warehouse. However, with the right tools, you can extend your analytics to query data in your data lake—with no loading required. Amazon Redshift Spectrum extends the analytic power of Amazon Redshift beyond data stored in your data warehouse to run SQL queries directly against vast amounts of unstructured data in your Amazon S3 data lake. This gives you the freedom to store your data where you want, in the format you want, and have it available for analytics when you need it. Join a discussion with AWS solution architects to ask question.
ABD330 – Combining Batch and Stream Processing to Get the Best of Both Worlds Today, many architects and developers are looking to build solutions that integrate batch and real-time data processing, and deliver the best of both approaches. Lambda architecture (not to be confused with the AWS Lambda service) is a design pattern that leverages both batch and real-time processing within a single solution to meet the latency, accuracy, and throughput requirements of big data use cases. Come join us for a discussion on how to implement Lambda architecture (batch, speed, and serving layers) and best practices for data processing, loading, and performance tuning
ABD335 – Real-Time Anomaly Detection Using Amazon Kinesis Amazon Kinesis Analytics offers a built-in machine learning algorithm that you can use to easily detect anomalies in your VPC network traffic and improve security monitoring. Join us for an interactive discussion on how to stream your VPC flow Logs to Amazon Kinesis Streams and identify anomalies using Kinesis Analytics.
ABD339 – Deep Dive and Best Practices for Amazon Athena Amazon Athena is an interactive query service that enables you to process data directly from Amazon S3 without the need for infrastructure. Since its launch at re:invent 2016, several organizations have adopted Athena as the central tool to process all their data. In this talk, we dive deep into the most common use cases, including working with other AWS services. We review the best practices for creating tables and partitions and performance optimizations. We also dive into how Athena handles security, authorization, and authentication. Lastly, we hear from a customer who has reduced costs and improved time to market by deploying Athena across their organization.
We look forward to meeting you at re:Invent 2017!
About the Author
Roy Ben-Alta is a solution architect and principal business development manager at Amazon Web Services in New York. He focuses on Data Analytics and ML Technologies, working with AWS customers to build innovative data-driven products.
In 2015, the Centers for Medicare and Medicaid Services (CMS) reported that healthcare spending made up 17.8% of the U.S. GDP – that’s almost $3.2 trillion or $9,990 per person. By 2025, the CMS estimates this number will increase to nearly 20%. As cloud technology evolves in the healthcare and life science industries, we are seeing how companies of all sizes are using AWS to provide powerful and innovative solutions to customers across the globe. This month we are excited to feature the following startups:
ClearCare – helping home care agencies operate efficiently and grow their business.
DNAnexus – providing a cloud-based global network for sharing and managing genomic data.
ClearCare (San Francisco, CA)
ClearCare envisions a future where home care is the only choice for aging in place. Home care agencies play a critical role in the economy and their communities by significantly lowering the overall cost of care, reducing the number of hospital admissions, and bending the cost curve of aging. Patients receiving home care typically have multiple chronic conditions and functional limitations, driving over $190 billion in healthcare spending in the U.S. each year. To offset these costs, health insurance payers are developing in-home care management programs for patients. ClearCare’s goal is to help home care agencies leverage technology to improve costs, outcomes, and quality of life for the aging population. The company’s powerful software platform is specifically designed for use by non-medical, in-home care agencies to manage their businesses.
Founder and CEO Geoff Nudd created ClearCare because of his own grandmother’s need for care. Keeping family members and caregivers up to date on a loved one’s well being can be difficult, so Geoff created what is now ClearCare’s Family Room, which enables caregivers and agency staff to check schedules and receive real-time updates about what’s happening in the home. Since then, agencies have provided feedback on others areas of their businesses that could be streamlined. ClearCare has now built over 20 modules to help home care agencies optimize operations with services including a telephony service, billing and payroll, and more. ClearCare now serves over 4,000 home care agencies, representing 500,000 caregivers and 400,000 seniors.
Using AWS, ClearCare is able to spin up reliable infrastructure for proofs of concept and iterate on those systems to quickly get value to market. The company runs many AWS services including Amazon Elasticsearch Service, Amazon RDS, and Amazon CloudFront. Amazon EMR and Amazon Athena have enabled ClearCare to build a Hadoop-based ETL and data warehousing system that processes terabytes of data each day. By utilizing these managed services, ClearCare has been able to go from concept to customer delivery in less than three months.
DNAnexus is accelerating the application of genomic data in precision medicine by providing a cloud-based platform for sharing and managing genomic and biomedical data and analysis tools. The company was founded in 2009 by Stanford graduate student Andreas Sundquist and two Stanford professors Arend Sidow and Serafim Batzoglou, to address the need for scaling secondary analysis of next-generation sequencing (NGS) data in the cloud. The founders quickly learned that users needed a flexible solution to build complex analysis workflows and tools that enable them to share and manage large volumes of data. DNAnexus is optimized to address the challenges of security, scalability, and collaboration for organizations that are pursuing genomic-based approaches to health, both in clinics and research labs. DNAnexus has a global customer base – spanning North America, Europe, Asia-Pacific, South America, and Africa – that runs a million jobs each month and is doubling their storage year-over-year. The company currently stores more than 10 petabytes of biomedical and genomic data. That is equivalent to approximately 100,000 genomes, or in simpler terms, over 50 billion Facebook photos!
DNAnexus is working with its customers to help expand their translational informatics research, which includes expanding into clinical trial genomic services. This will help companies developing different medicines to better stratify clinical trial populations and develop companion tests that enable the right patient to get the right medicine. In collaboration with Janssen Human Microbiome Institute, DNAnexus is also launching Mosaic – a community platform for microbiome research.
AWS provides DNAnexus and its customers the flexibility to grow and scale research programs. Building the technology infrastructure required to manage these projects in-house is expensive and time-consuming. DNAnexus removes that barrier for labs of any size by using AWS scalable cloud resources. The company deploys its customers’ genomic pipelines on Amazon EC2, using Amazon S3 for high-performance, high-durability storage, and Amazon Glacier for low-cost data archiving. DNAnexus is also an AWS Life Sciences Competency Partner.
Leaves are crunching under my boots, Halloween is tomorrow, and pumpkin is having its annual moment in the sun – it’s fall everybody! And just in time to celebrate, we have whipped up a fresh batch of pumpkin spice Tech Talks. Grab your planner (Outlook calendar) and pencil these puppies in. This month we are covering re:Invent, serverless, and everything in between.
November 2017 – Schedule
Noted below are the upcoming scheduled live, online technical sessions being held during the month of November. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.
Now that you can reserve seating in AWS re:Invent 2017 breakout sessions, workshops, chalk talks, and other events, the time is right to review the list of introductory, advanced, and expert content being offered this year. To learn more about breakout content types and levels, see Breakout Content.
SID201 – IAM for Enterprises: How Vanguard Strikes the Balance Between Agility, Governance, and Security For Vanguard, managing the creation of AWS Identity and Access Management (IAM) objects is key to balancing developer velocity and compliance. In this session, you learn how Vanguard designs IAM roles to control the blast radius of AWS resources and maintain simplicity for developers. Vanguard will also share best practices to help you manage governance and improve your visibility across your AWS resources.
SID202 – Deep dive about how Capital One automates the delivery of directory services across AWS accounts Traditional solutions for using Microsoft Active Directory across on-premises and AWS Cloud Windows workloads can require complex networking or syncing identities across multiple systems. AWS Directory Service for Microsoft Active Directory, also known as AWS Managed AD, offers you actual Microsoft Active Directory in the AWS Cloud as a managed service. In this session, you will learn how Capital One uses AWS Managed AD to provide highly available authentication and authorization services for its Windows workloads, such as Amazon RDS for SQL Server.
SID205 – Building the Largest Repo for Serverless Compliance-as-Code When you use the cloud to enable speed and agility, how do you know if you’ve done it correctly? We are on a mission to help builders follow industry best practices within security guardrails by creating the largest compliance-as-code repository, available to all. Compliance-as-code is the idea to translate best practices, guardrails, policies, and standards into codified unit testing. Apply this to your AWS environment to provide insights about what can or must be improved. Learn why compliance-as-code matters to gain speed (by getting developers, architects, and security pros on the same page), how it is currently used (demo), and how to start using it or being part of building it.
SID206 – Best Practices for Managing Security Operation on AWS To help prevent unexpected access to your AWS resources, it is critical to maintain strong identity and access policies and track, detect, and react to changes. In this session, you will learn how to use AWS Identity and Access Management (IAM) to control access to AWS resources and integrate your existing authentication system with IAM. We will cover how to deploy and control AWS infrastructure using code templates, including change management policies with AWS CloudFormation.
SID207 – Feedback Security in the Cloud Like many security teams, Riot has been challenged by new paradigms that came with the move to the cloud. We discuss how our security team has developed a security culture based on feedback and self-service to best thrive in the cloud. We detail how the team assessed the security gaps and challenges in our move to AWS, and then describe how the team works within Riot’s unique feedback culture.
SID208 – Less (Privilege) Is More: Getting Least Privilege Right in AWS AWS services are designed to enable control through AWS Identity and Access Management (IAM) and Amazon Virtual Private Cloud (VPC). Join us in this chalk talk to learn how to apply these toward the security principal of least privilege for applications and data and how to practically integrate them in your security operations.
SID209 – Designing and Deploying an AWS Account Factory AWS customers start off with one AWS account, but quickly realize the benefits of having multiple AWS accounts. A common learning curve for customers is how to securely baseline and set up new accounts at scale. This talk helps you understand how to use AWS Organizations, AWS Identity and Access Management (IAM), AWS CloudFormation, and other tools to baseline new accounts, set them up for federation, and make a secure and repeatable account factory to create new AWS accounts. Walk away with demos and tools to use in your own environment.
SID210 – A CISO’s Journey at Vonage: Achieving Unified Security at Scale Making sense of the risks of IT deployments that sit in hybrid environments and span multiple countries is a major challenge. When you add in multiple toolsets and global compliance requirements, including GDPR, it can get overwhelming. Listen to Vonage’s Chief Information Security Officer, Johan Hybinette, share his experiences tackling these challenges.
SID212 – Maximizing Your Move to AWS – Five Key Lessons from Vanguard and Cloud Technology Partners CTP’s Robert Christiansen and Mike Kavis describe how to maximize the value of your AWS initiative. From building a Minimum Viable Cloud to establishing a cloud robust security and compliance posture, we walk through key client success stories and lessons learned. We also explore how CTP has helped Vanguard, the leading provider of investor communications and technology, take advantage of AWS to delight customers, drive new revenue streams, and transform their business.
SID213 – Managing Regulator Expectations – Lessons Learned on Positioning AWS Services from an Audit Perspective Cloud migration in highly regulated industries can stall without a solid understanding of how (and when) to address regulatory expectations. This session provides a guide to explaining the aspects of AWS services that are most frequently the subject of an internal or regulatory audit. Because regulatory agencies and internal auditors might not share a common understanding of the cloud, this session is designed to help you to help them, regardless of their level of technical fluency.
SID214 – Best Security Practices in the Intelligence Community Executives from the Intelligence community discuss cloud security best practices in a field where security is imperative to operations. CIA security cloud chief John Nicely and NGA security cloud chief Scot Kaplan share success stories of migrating mass data to the cloud from a security perspective. Hear how they migrated their IT portfolios while managing their organizations’ unique blend of constraints, budget issues, politics, culture, and security pressures. Learn how these institutions overcame barriers to migration, and ask these panelists what actions you can take to better prepare yourself for the journey of mass migration to the cloud.
SID216 – Defending Diverse Applications Against Common Threats In this session, you learn how to adapt application defenses and operational responses based on your unique requirements. You also hear directly from customers about how they architected their applications on AWS to protect their applications. There are many ways to build secure, high-availability applications in the cloud. Services such as Amazon API Gateway, Amazon VPC, ALB, ELB, and Amazon EC2 are the basic building blocks that enable you to address a wide range of use cases. Best practices for defending your applications against Distributed Denial of Service (DDoS) attacks, exploitation attempts, and bad bots can vary with your choices in architecture.
Advanced level
SID301 – Using AWS Lambda as a Security Team Operating a security practice on AWS brings many new challenges that haven’t been faced in data center environments. The dynamic nature of infrastructure, the relationship between development team members and their applications, and the architecture paradigms have all changed as a result of building software on top of AWS. In this session, learn how your security team can leverage AWS Lambda as a tool to monitor, audit, and enforce your security policies within an AWS environment.
SID302 – Force Multiply Your Security Team with Automation and Alexa Adversaries automate. Who says the good guys can’t as well? By combining AWS offerings like AWS CloudTrail, Amazon Cloudwatch, AWS Config, and AWS Lambda with the power of Amazon Alexa, you can do more security tasks faster, with fewer resources. Force multiplying your security team is all about automation! Last year, we showed off penetration testing at the push of an (AWS IoT) button, and surprise-previewed how to ask Alexa to run Inspector as-needed. Want to see other ways to ask Alexa to be your cloud security sidekick? We have crazy new demos at the ready to show security geeks how to sling security automation solutions for their AWS environments (and impress and help your boss, too).
SID303 – How You Can Use AWS’s Identity Services to be Successful on Your AWS Cloud Journey Every journey to the AWS Cloud is unique. Some customers are migrating existing applications, while others are building new applications using cloud-native services. Along each of these journeys, identity and access management helps customers protect their applications and resources. In this session, you will learn how AWS’s identity services provide you a secure, flexible, and easy solution for managing identities and access on the AWS Cloud. With AWS’’s Identity Services, you do not have to adapt to AWS. Instead, you have a choice of services designed to meet you anywhere along your journey to the AWS Cloud. Every journey to the AWS Cloud is unique. Some customers are migrating existing applications, while others are building new applications using cloud-native services.
SID304 – SecOps 2021 Today: Using AWS Services to Deliver SecOps This talk dives deep on how to build end-to-end security capabilities using AWS. Our goal is orchestrating AWS Security services with other AWS building blocks to deliver enhanced security. We cover working with AWS CloudWatch Events as a queueing mechanism for processing security events, using Amazon DynamoDB to provide a stateful layer to provide tailored response to events and other ancillary functions, using DynamoDB as an attack signature engine, and the use of analytics to derive tailored signatures for detection with AWS Lambda.
SID305 – How CrowdStrike Built a Real-time Security Monitoring Service on AWS The CrowdStrike motto is “We Stop Breaches.” To do that, it needed to build a real-time security monitoring service to detect threats. Join this session to learn how Crowdstrike uses Amazon EC2 and Amazon EBS to help its customers identify vulnerabilities before they become large-scale problems.
SID306 – How Chick-fil-A Embraces DevSecOps on AWS As Chick-fil-A became a cloud-first organization, their security team didn’t want to become the bottleneck for agility. But the security team also wanted to raise the bar for their security posture on AWS. Robert Davis, security architect at Chick-fil-A, provides an overview about how he and his team recognized that writing code was the best way for their security policies to scale across the many AWS accounts that Chick-fil-A operates.
SID307 – Serverless for Security Officers: Paradigm Walkthrough and Comprehensive Security Best Practices For security practitioners, serverless represents a context switch from the familiar servers and networks to a decentralized set of code snippets and AWS platform constructs. This new ecosystem also represents new operational teams, data flows, security tooling, and faster-then-ever change velocity. In this talk, we perform live demos and provide code samples for a wide array of security best practices aligned to industry standards such as NIST 800-53 and ISO 27001.
SID308 – Multi-Account Strategies We will explore a multi-account architecture and how to approach the design/thought process around it. This chalk talk will allow attendees to dive deep into the topic and discuss the nuances of the architecture as well as provide feedback around the approach.
SID309 – Credentials, Credentials, Credentials, Oh My! For new and experienced customers alike, understanding the various credential forms and exchange mechanisms within AWS can be a daunting exercise. In this chalk talk, we clear up the confusion by performing a cartography exercise. We visually depict the right source credentials (for example, enterprise user name and password, IAM keys, AWS STS tokens, and so on) and transformation mechanisms (for example, AssumeRole and so on) to use depending on what you’re trying to do and where you’re coming from.
SID310 – Moving from the Shadows to the Throne What do you do when leadership embraces what was called “shadow IT” as the new path forward? How do you onboard new accounts while simultaneously pushing policy to secure all existing accounts? This session walks through Cisco’s journey consolidating over 700 existing accounts in the Cisco organization, while building and applying Cisco’s new cloud policies.
SID311 – Designing Security and Governance Across a Multi-Account Strategy When organizations plan their journey to cloud adoption at scale, they quickly encounter questions such as: How many accounts do we need? How do we share resources? How do we integrate with existing identity solutions? In this workshop, we present best practices and give you the hands-on opportunity to test and develop best practices. You will work in teams to set up and create an AWS environment that is enterprise-ready for application deployment and integration into existing operations, security, and procurement processes. You will get hands-on experience with cross-account roles, consolidated logging, account governance and other challenges to solve.
SID312 – DevSecOps Capture the Flag In this Capture the Flag workshop, we divide groups into teams and work on AWS CloudFormation DevSecOps. The AWS Red Team supplies an AWS DevSecOps Policy that needs to be enforced via CloudFormation static analysis. Participant Blue Teams are provided with an AWS Lambda-based reference architecture to be used to inspect CloudFormation templates against that policy. Interesting items need to be logged, and made visible via ChatOps. Dangerous items need to be logged, and recorded accurately as a template fail. The secondary challenge is building a CloudFormation template to thwart the controls being created by the other Blue teams.
SID313 – Continuous Compliance on AWS at Scale In cloud migrations, the cloud’s elastic nature is often touted as a critical capability in delivering on key business initiatives. However, you must account for it in your security and compliance plans or face some real challenges. Always counting on a virtual host to be running, for example, causes issues when that host is rebooted or retired. Managing security and compliance in the cloud is continuous, requiring forethought and automation. Learn how a leading, next generation managed cloud provider uses automation and cloud expertise to manage security and compliance at scale in an ever-changing environment.
SID314 – IAM Policy Ninja Are you interested in learning how to control access to your AWS resources? Have you wondered how to best scope permissions to achieve least-privilege permissions access control? If your answer is “yes,” this session is for you. We look at the AWS Identity and Access Management (IAM) policy language, starting with the basics of the policy language and how to create and attach policies to IAM users, groups, and roles. We explore policy variables, conditions, and tools to help you author least privilege policies. We cover common use cases, such as granting a user secure access to an Amazon S3 bucket or to launch an Amazon EC2 instance of a specific type.
SID315 – Security and DevOps: Agility and Teamwork In this session, you learn pragmatic steps to integrate security controls into DevOps processes in your AWS environment at scale. Cybersecurity expert and founder of Alert Logic Misha Govshteyn shares insights from high performing teams who are embracing the reality that an agile security program can enable faster and more secure workload deployments. Joining Misha is Joey Peloquin, Director of Cloud Security Operations at Citrix, who discusses Citrix’s DevOps experiences and how they manage their cybersecurity posture within the AWS Cloud. Session sponsored by Alert Logic.
SID316 – Using Access Advisor to Strike the Balance Between Security and Usability AWS provides a killer feature for security operations teams: Access Advisor. In this session, we discuss how Access Advisor shows the services to which an IAM policy grants access and provides a timestamp for the last time that the role authenticated against that service. At Netflix, we use this valuable data to automatically remove permissions that are no longer used. By continually removing excess permissions, we can achieve a balance of empowering developers and maintaining a best-practice, secure environment.
SID317 – Automating Security and Compliance Testing of Infrastructure-as-Code for DevSecOps Infrastructure-as-Code (IaC) has emerged as an essential element of organizational DevOps practices. Tools such as AWS CloudFormation and Terraform allow software-defined infrastructure to be deployed quickly and repeatably to AWS. But the agility of CI/CD pipelines also creates new challenges in infrastructure security hardening. This session provides a foundation for how to bring proven software hardening practices into the world of infrastructure deployment. We discuss how to build security and compliance tests for infrastructure analogous to unit tests for application code, and showcase how security, compliance and governance testing fit in a modern CI/CD pipeline.
SID318 – From Obstacle to Advantage: The Changing Role of Security & Compliance in Your Organization A surprising trend is starting to emerge among organizations who are progressing through the cloud maturity lifecycle: major improvements in revenue growth, customer satisfaction, and mission success are being directly attributed to improvements in security and compliance. At one time thought of as speed bumps in the path to deployment, security and compliance are now seen as critical ingredients that help organizations differentiate their offerings in the market, win more deals, and achieve mission-critical goals faster. This session explores how organizations like Jive Software and the National Geospatial Agency use the Evident Security Platform, AWS, and AWS Quick Starts to automate security and compliance processes in their organization to accomplish more, do it faster, and deliver better results.
SID319 – Incident Response in the Cloud In this session, we walk you through a hypothetical incident response managed on AWS. Learn how to apply existing best practices as well as how to leverage the unique security visibility, control, and automation that AWS provides. We cover how to set up your AWS environment to prevent a security event and how to build a cloud-specific incident response plan so that your organization is prepared before a security event occurs. This session also covers specific environment recovery steps available on AWS.
SID320 – Fraud Prevention, Detection, Lessons Learned, and Best Practices Fighting fraud means countering human actors that quickly adapt to whatever you do to stop them. In this presentation, we discuss the key components of a fraud prevention program in the cloud. Additionally, we provide techniques for detecting known and unknown fraud activity and explore different strategies for effectively preventing detected patterns. Finally, we discuss lessons learned from our own prevention activities as well as the best practices that you can apply to manage risk.
SID321 – How Capital One Applies AWS Organizations Best Practices to Manage Multiple AWS Accounts In this session, we review best practices for managing multiple AWS accounts using AWS Organizations. We cover how to think about the master account and your account strategy, as well as how to roll out changes. You learn how Capital One applies these best practices to manage its AWS accounts, which number over 160, and PCI workloads.
SID322 – The AWS Philosophy of Security AWS distinguished engineer Eric Brandwine speaks with hundreds of customers each year, and noticed one question coming up more than any other, “How does AWS operationalize its own security?” In this session, Eric details both strategic and tactical considerations, along with an insider’s look at AWS tooling and processes.
SID324 – Automating DDoS Response in the Cloud If left unmitigated, Distributed Denial of Service (DDoS) attacks have the potential to harm application availability or impair application performance. DDoS attacks can also act as a smoke screen for intrusion attempts or as a harbinger for attacks against non-cloud infrastructure. Accordingly, it’s crucial that developers architect for DDoS resiliency and maintain robust operational capabilities that allow for rapid detection and engagement during high-severity events. In this session, you learn how to build a DDoS-resilient application and how to use services like AWS Shield and Amazon CloudWatch to defend against DDoS attacks and automate response to attacks in progress.
SID325 – Amazon Macie: Data Visibility Powered by Machine Learning for Security and Compliance Workloads In this session, Edmunds discusses how they create workflows to manage their regulated workloads with Amazon Macie, a newly-released security and compliance management service that leverages machine learning to classify your sensitive data and business-critical information. Amazon Macie uses recurrent neural networks (RNN) to identify and alert potential misuse of intellectual property. They do a deep dive into machine learning within the security ecosystem.
SID326 – AWS Security State of the Union Steve Schmidt, chief information security officer at AWS, addresses the current state of security in the cloud, with a particular focus on feature updates, the AWS internal “secret sauce,” and what’s on horizon in terms of security, identity, and compliance tooling.
SID327 – How Zocdoc Achieved Security and Compliance at Scale With Infrastructure as Code In less than 12 months, Zocdoc became a cloud-first organization, diversifying their tech stack and liberating data to help drive rapid product innovation. Brian Lozada, CISO at Zocdoc, and Zhen Wang, Director of Engineering, provide an overview on how their teams recognized that infrastructure as code was the most effective approach for their security policies to scale across their AWS infrastructure. They leveraged tools such as AWS CloudFormation, hardened AMIs, and hardened containers. The use of DevSecOps within Zocdoc has enhanced data protection with the use of AWS services such as AWS KMS and AWS CloudHSM and auditing capabilities, and event-based policy enforcement with Amazon Elasticsearch Service and Amazon CloudWatch, all built on top of AWS.
SID328 – Cloud Adoption in Regulated Financial Services Macquarie, a global provider of financial services, identified early on that it would require strong partnership between its business, technology and risk teams to enable the rapid adoption of AWS cloud technologies. As a result, Macquarie built a Cloud Governance Platform to enable its risk functions to move as quickly as its development teams. This platform has been the backbone of Macquarie’s adoption of AWS over the past two years and has enabled Macquarie to accelerate its use of cloud technologies for the benefit of clients across multiple global markets. This talk will outline the strategy that Macquarie embarked on, describe the platform they built, and provide examples of other organizations who are on a similar journey.
SID329 – A Deep Dive into AWS Encryption Services AWS Encryption Services provide an easy and cost-effective way to protect your data in AWS. In this session, you learn about leveraging the latest encryption management features to minimize risk for your data.
SID330 – Best Practices for Implementing Your Encryption Strategy Using AWS Key Management Service AWS Key Management Service (KMS) is a managed service that makes it easy for you to create and manage the encryption keys used to encrypt your data. In this session, we will dive deep into best practices learned by implementing AWS KMS at AWS’s largest enterprise clients. We will review the different capabilities described in the AWS Cloud Adoption Framework (CAF) Security Perspective and how to implement these recommendations using AWS KMS. In addition to sharing recommendations, we will also provide examples that will help you protect sensitive information on the AWS Cloud.
SID331 – Architecting Security and Governance Across a Multi-Account Strategy Whether it is per business unit or per application, many AWS customers use multiple accounts to meet their infrastructure isolation, separation of duties, and billing requirements. In this session, we discuss considerations, limitations, and security patterns when building out a multi-account strategy. We explore topics such as identity federation, cross-account roles, consolidated logging, and account governance. Thomson Reuters shared their journey and their approach to a multi-account strategy. At the end of the session, we present an enterprise-ready, multi-account architecture that you can start leveraging today.
SID332 – Identity Management for Your Users and Apps: A Deep Dive on Amazon Cognito Learn how to set up an end-user directory, secure sign-up and sign-in, manage user profiles, authenticate and authorize your APIs, federate from enterprise and social identity providers, and use OAuth to integrate with your app—all without any server setup or code. With clear blueprints, we show you how to leverage Amazon Cognito to administer and secure your end users and enable identity for the applied patterns of mobile, web, and enterprise apps.
SID334 – How Amazon Business Uses Amazon Cloud Directory as the Data Store for Its Account Management Platform Join the Amazon Business team to learn how it uses Amazon Cloud Directory as the data store for its account management platform. You will learn how Amazon Business uses Amazon DynamoDB with Cloud Directory to manage user authorization and business process workflows. You also will learn how Cloud Directory helps to manage hierarchical datasets and how to get started modeling these datasets in Cloud Directory.
SID335 – Implementing Security and Governance across a Multi-Account Strategy As existing or new organizations expand their AWS footprint, managing multiple accounts while maintaining security quickly becomes a challenge. In this chalk talk, we will demonstrate how AWS Organizations, IAM roles, identity federation, and cross-account manager can be combined to build a scalable multi-account management platform. By the end of this session, attendees will have the understanding and deployment patterns to bring a secure, flexible and automated multi-account management platform to their organizations.
SID336 – Use AWS to Effectively Manage GDPR Compliance The General Data Protection Regulation (GDPR) is considered to be the most stringent privacy regulation ever enacted. Complying with GDPR could be a challenge for organizations, and AWS services can help get you ahead of the May 2018 enforcement deadline. In this chalk talk, the Legal and Compliance GDPR leadership at AWS discusses what enforcement of GDPR might mean for you and your customer’s compliance programs.
SID337 – Best Practices for Managing Access to AWS Resources Using IAM Roles In this chalk talk, we discuss why using temporary security credentials to manage access to your AWS resources is an AWS Identity and Access Management (IAM) best practice. IAM roles help you follow this best practice by delivering and rotating temporary credentials automatically. We discuss the different types of IAM roles, the assume role functionality, and how to author fine-grained trust and access policies that limit the scope of IAM roles. We then show you how to attach IAM roles to your AWS resources, such as Amazon EC2 instances and AWS Lambda functions. We also discuss migrating applications that use long-term AWS access keys to temporary credentials managed by IAM roles.
SID338 – [email protected] Once a customer achieves success with using AWS in a few pilot projects, most look to rapidly adopt an “all-in” enterprise migration strategy. Along this journey, several new challenges emerge that quickly become blockers and slow down migrations if they are not addressed properly. At this scale, customers will deal with the governance of hundreds of accounts, as well as thousands of IT resources residing within those accounts. Humans and traditional IT management processes cannot scale at the same pace and inevitably challenging questions emerge. In this session, we discuss those questions about governance at scale.
SID339 – Deep Dive on AWS CloudHSM Organizations building applications that handle confidential or sensitive data are subject to many types of regulatory requirements and often rely on hardware security modules (HSMs) to provide validated control of encryption keys and cryptographic operations. AWS CloudHSM is a cloud-based hardware security module (HSM) that enables you to easily generate and use your own encryption keys on the AWS Cloud using FIPS 140-2 Level 3 validated HSMs. This chalk talk will provide you a deep-dive on CloudHSM, and demonstrate how you can quickly and easily use CloudHSM to help secure your data and meet your compliance requirements.
SID340 – Using Infrastructure as Code to Inject Security Best Practices as Part of the Software Deployment Lifecycle A proactive approach to security is key to securing your applications as part of software deployment. In this chalk talk, T. Rowe Price, a financial asset management institution, outlines how they built their security automation process in enabling their numerous developer teams to rapidly and securely build and deploy applications at scale on AWS. Learn how they use services like AWS Identity and Access Management (IAM), HashiCorp tools, Terraform for automation, and Vault for secrets management, and incorporate certificate management and monitoring as part of the deployment process. T. Rowe Price discusses lessons learned and best practices to move from a tightly controlled legacy environment to an agile, automated software development process on AWS.
SID341 – Using AWS CloudTrail Logs for Scalable, Automated Anomaly Detection This workshop gives you an opportunity to develop a solution that can continuously monitor for and detect a realistic threat by analyzing AWS CloudTrail log data. Participants are provided with a CloudTrail data source and some clues to get started. Then you have to design a system that can process the logs, detect the threat, and trigger an alarm. You can make use of any AWS services that can assist in this endeavor, such as AWS Lambda for serverless detection logic, Amazon CloudWatch or Amazon SNS for alarming and notification, Amazon S3 for data and configuration storage, and more.
SID342 – Protect Your Web Applications from Common Attack Vectors Using AWS WAF As attacks and attempts to exploit vulnerabilities in web applications become more sophisticated, having an effective web request filtering solution becomes key to keeping your users’ data safe. In this workshop, discover how the OWASP Top 10 list of application security risks can help you secure your web applications. Learn how to use AWS services, such as AWS WAF, to mitigate vulnerabilities. This session includes hands-on labs to help you build a solution. Key learning goals include understanding the breadth and complexity of vulnerabilities customers need to protect from, understanding the AWS tools and capabilities that can help mitigate vulnerabilities, and learning how to configure effective HTTP request filtering rules using AWS WAF.
SID343 – User Management and App Authentication with Amazon Cognito Are you curious about how to authenticate and authorize your applications on AWS? Have you thought about how to integrate AWS Identity and Access Management (IAM) with your app authentication? Have you tried to integrate third-party SAML providers with your app authentication? Look no further. This workshop walks you through step by step to configure and create Amazon Cognito user pools and identity pools. This workshop presents you with the framework to build an application using Java, .NET, and serverless. You choose the stack and build the app with local users. See the service being used not only with mobile applications but with other stacks that normally don’t include Amazon Cognito.
SID344 – Soup to Nuts: Identity Federation for AWS AWS offers customers multiple solutions for federating identities on the AWS Cloud. In this session, we will embark on a tour of these solutions and the use cases they support. Along the way, we will dive deep with demonstrations and best practices to help you be successful managing identities on the AWS Cloud. We will cover how and when to use Security Assertion Markup Language 2.0 (SAML), OpenID Connect (OIDC), and other AWS native federation mechanisms. You will learn how these solutions enable federated access to the AWS Management Console, APIs, and CLI, AWS Infrastructure and Managed Services, your web and mobile applications running on the AWS Cloud, and much more.
SID345 – AWS Encryption SDK: The Busy Engineer’s Guide to Client-Side Encryption You know you want client-side encryption for your service but you don’t know exactly where to start. Join us for a hands-on workshop where we review some of your client-side encryption options and explore implementing client-side encryption using the AWS Encryption SDK. In this session, we cover the basics of client-side encryption, perform encrypt and decrypt operations using AWS KMS and the AWS Encryption SDK, and discuss security and performance considerations when implementing client-side encryption in your service.
Expert level
SID401 – Let’s Dive Deep Together: Advancing Web Application Security Beginning with a recap of best practices in CloudFront, AWS WAF, Route 53, and Amazon VPC security, we break into small teams to work together on improving the security of a typical web application. How can we creatively use the services? What additional features would help us? This technically advanced chalk talk requires certification at the solutions architect associate level or greater.
SID402 – An AWS Security Odyssey: Implementing Security Controls in the World of Internet, Big Data, IoT and E-Commerce Platforms This workshop will give participants the opportunity to take a security-focused journey across various AWS services and implement automated controls along the way. You will learn how to apply AWS security controls to services such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon VPC. In short, you will learn how to use the cloud to protect the cloud. We will talk about how to: Adopt a workload-centric approach to your security strategy, Address security issues in a cost-effective manner Automate your security responses to promote maturity and auditability. In order to complete this workshop, attendees will need a laptop with wireless access, an AWS account and an IAM user that has full administrative privileges within their account. AWS credits will be provided as attendees depart the session to cover the cost of running the workshop in their own account.
SID404 – Amazon Inspector – Automating the “Sec” in DevSecOps Adopting DevSecOps can be challenging using traditional security tools that are designed for on-premises infrastructure. Amazon Inspector is an automated security assessment service that helps you adopt DevSecOps by integrating security assessments directly into the development process of applications running on Amazon EC2. We dive deep on how to use Inspector to automate host security assessments. We show you how to integrate Inspector with other AWS Cloud services to provide automated security assessments throughout your development process. We demo installing the AWS agent, setting up assessment targets and templates, and running assessments. We review the findings and discuss how you can automate the management and remediation of those findings with your available AWS services.
SID405 – Five New Security Automation Improvements You Can Make by Using Amazon CloudWatch Events and AWS Config Rules This presentation will include a deep dive into the code behind multiple security automation and remediation functions. This session will consider potential use cases, as well as feature a demonstration of a proposed script, and then walk through the code set to explain the various challenges and solutions of the intended script. All examples of code will be previously unreleased and will feature integration with services such as Trusted Advisor and Macie. All code will be released as OSS after re:Invent.
Starting today, you can connect to your Amazon Elasticsearch Service domains from within an Amazon VPC without the need for NAT instances or Internet gateways. VPC support for Amazon ES is easy to configure, reliable, and offers an extra layer of security. With VPC support, traffic between other services and Amazon ES stays entirely within the AWS network, isolated from the public Internet. You can manage network access using existing VPC security groups, and you can use AWS Identity and Access Management (IAM) policies for additional protection. VPC support for Amazon ES domains is available at no additional charge.
Getting Started
Creating an Amazon Elasticsearch Service domain in your VPC is easy. Follow all the steps you would normally follow to create your cluster and then select “VPC access”.
That’s it. There are no additional steps. You can now access your domain from within your VPC!
Things To Know
To support VPCs, Amazon ES places an endpoint into at least one subnet of your VPC. Amazon ES places an Elastic Network Interface (ENI) into the VPC for each data node in the cluster. Each ENI uses a private IP address from the IPv4 range of your subnet and receives a public DNS hostname. If you enable zone awareness, Amazon ES creates endpoints in two subnets in different availability zones, which provides greater data durability.
You need to set aside three times the number of IP addresses as the number of nodes in your cluster. You can divide that number by two if Zone Awareness is enabled. Ideally, you would create separate subnets just for Amazon ES.
A few notes:
Currently, you cannot move existing domains to a VPC or vice-versa. To take advantage of VPC support, you must create a new domain and migrate your data.
Currently, Amazon ES does not support Amazon Kinesis Firehose integration for domains inside a VPC.
Amazon Cognito user pools are full-fledged identity providers (IdP) that you can use to maintain a user directory. The directory can scale to hundreds of millions of users and also add sign-up and sign-in support to your mobile or web applications.
You can provide access from an Amazon Cognito user pool to Amazon QuickSight with IAM identity federation in a serverless way. This solution uses Amazon API Gateway and AWS Lambda as a backend fronted by a simple web app. New features such as Amazon Cognito user pools app Integration make it even easier to add sign-in and sign-up logic to your application and federation use cases.
Solution architecture
Here’s how you can accomplish this solution.
In this scenario, your web app hosted on Amazon S3 integrates with Amazon Cognito User Pools to authenticate users. It uses Amazon Cognito Federated Identities to authorize access to Amazon QuickSight on behalf of the authenticated user, with temporary AWS credentials and appropriate permissions. The app then uses an ID token generated by Amazon Cognito to call API Gateway and Lambda to obtain a sign-in token for Amazon QuickSight from AWS Sign-In Federation. With this token, the app redirects access to Amazon QuickSight:
The Amazon Cognito hosted UI provided by the app integration domain performs all sign-in, sign-up, verification, and authentication logic for the web app. This allows you to register and authenticate users.
After a user is authenticated with a valid user name and password, an OpenID Connect token (ID token) is sent to Amazon Cognito Federated Identities. The token retrieves temporary AWS credentials based on an IAM role with “quickSight:CreateUser” permissions. These credentials are used to build a session string that is encoded into the URL https://signin.aws.amazon.com/federation?Action=getSigninToken.
The ID token, along with the encoded URL, is sent to API Gateway, which in turn verifies the token with a user pool authorizer to authorize the API call.
The URL is passed on to a Lambda function that calls the AWS SSO federation endpoint to retrieve a sign-In token.
AWS SSO processes the federation request, authenticates the user, and forwards the authentication token to Amazon QuickSight, which then uses the authentication token and authorizes user access.
How can you use, configure and test this serverless solution in your own AWS account? I created a simple SAM (Serverless Application Model) template that can be used to spin up all the resources needed for the solution.
Implementation steps
Using the AWS CLI, create an S3 bucket in the same region in which to deploy all resources:
Then, execute a couple of commands in a terminal or command prompt from inside the uncompressed folder where all files are located, referring to the S3 bucket created earlier:
AWS CloudFormation automatically creates and configures the following resources in your account:
Amazon CloudFront distribution
S3 static website
Amazon Cognito user pool
Amazon Cognito identity pool
IAM role for authenticated users
API Gateway API
Lambda function
You can follow the progress of the stack creation from the CloudFormation console. View the Outputs tab for the completed stack to get the identifiers of all created resources. You could also execute the following command with the AWS CLI:
aws cloudformation describe-stacks --query 'Stacks[0].[Outputs[].[OutputKey,OutputValue]]|[]' --output text --stack-name CognitoQuickSight
Use the information from the console or CLI command to replace the related resource identifiers in the file “auth.js”.
In the Amazon Cognito User Pools console, select the pool named QuickSightUsers generated by CloudFormation.
Under App integration, choose Domain name and create a domain. Domain names must be unique to the region. Add the domain to the “auth.js” file accordingly:
Choose App integration, App client settings and then select the option Cognito User Pool. Add the CloudFront distribution address (with https://, as SSL is a requirement for the callback/sign out URLs) and make sure that the address matches the related settings in the “auth.js” file exactly. For Allowed OAuth Flows, select implicit grant. For Allowed OAuth Scopes, select openid.
The app integration configuration is now done. Your “auth.js” file should look like the following:
The preceding resources don’t exist anymore. The CloudFormation stack that generated them was deleted. I recommend that you delete your stack after testing, for cleanup purposes. Deleting the stack also deletes all the resources.
Next, upload the four JS and HTML files to the S3 bucket named “cognitoquicksight-s3website-xxxxxxxxx”. Make sure that all files are publicly readable:
Congratulations, the configuration part is now finished!
It’s time to create your first user. Access your CloudFront distribution address in a browser and choose SIGN IN / SIGN UP.
On the Amazon Cognito hosted UI, choose SIGN UP and provide a user name, password and a valid email.
You receive a verification code in email to confirm the user.
In a production system, you might not want to allow open access to your dashboards. As you now have a confirmed user, you can disable the sign-up functionality altogether to avoid letting other users sign themselves up.
In the Amazon Cognito User Pools console, choose General settings, Policies and select Only allow administrators to create users.
In the web app, you can now sign in as the Amazon Cognito user to access the Amazon QuickSight console. Because this is the first time this user is accessing Amazon QuickSight with an IAM role, provide your email address and sign up as an Amazon QuickSight user.
Enjoy your federated access to Amazon QuickSight!
After you’re done testing, go to the CloudFormation console and delete the CognitoQuickSight stack to remove all the resources.
Extending and customizing the solution
Additionally, you could configure SAML federation for your user pool with a couple of clicks, following the instructions in the Amazon Cognito User Pools supports federation with SAML post. If you add more than one SAML IdP, Amazon Cognito can identify the provider to which to redirect the authentication request, based on the user’s corporate email address.
It’s important to understand that while Amazon Cognito User Pools is authenticating (AuthN) the user, the IAM role created for the identity pool is authorizing (AuthZ) the user to perform actions on specific resources. As it is configured, the role only allows “quickSight:CreateUser” permissions. For additional permissions, modify the role accordingly, as in Setting Your IAM Policy. If your users create datasets, remember to add access to data sources such as Amazon S3.
You can customize this solution further by adding multiple groups to your user pool and associating each group with different IAM roles. For more information, see Amazon Cognito Groups and Fine-Grained Role-Based Access Control. For instance, with role-based access control, it’s possible to have a group for Amazon QuickSight administrators and one for users.
You can also modify the sign-In URL (Step 6) to redirect your user to any other service console, provided that the IAM role has appropriate permissions. After receiving a valid sign-in token from the SSO federation endpoint, the user is redirected to https://quicksight.aws.amazon.com. However, you can change the redirection to the main AWS console at https://console.aws.amazon.com. You could also change it to specific services, such as Amazon Redshift, Amazon EMR, Amazon Elasticsearch Service and Kibana (In this particular case the application needs to return an AWS Signature Version 4 – Sigv4 – signed URL based on the temporary credentials received from Cognito instead of calling the AWS Sign-In Federation endpoint), Amazon Kinesis, or AWS Lambda. Or, you can even customize a frontend portal with separate links to multiple specific services and resources that the user is only allowed to access provided that the IAM role being assumed has access to those services and resources.
Conclusion
With the power, flexibility, security, scalability, and the new federation and application integration features of Amazon Cognito user pools, there’s no need to worry about the undifferentiated heavy lifting of maintaining your own identity servers. This allows you to focus on application logic and secure access to other great AWS services, such as Amazon QuickSight.
If you have questions or suggestions, please comment below.
About the Author
Ed Lima is a Solutions Architect who helps AWS customers with their journey in the cloud. He has provided thought leadership to define and drive strategic direction for adoption of Amazon platforms and technologies, skillfully adapting and blending business requirements with technical aspects to achieve the best outcome helping implement well architected solutions. In his spare time, he enjoys snowboarding.
When you use Amazon Relational Database Service (Amazon RDS), depending on the logging levels on the RDS instances and the volume of transactions, you could generate a lot of log data. To ensure that everything is running smoothly, many customers search for log error patterns using different log aggregation and visualization systems, such as Amazon Elasticsearch Service, Splunk, or other tool of their choice. A module needs to periodically retrieve the RDS logs using the SDK, and then send them to Amazon S3. From there, you can stream them to your log aggregation tool.
One option is writing an AWS Lambda function to retrieve the log files. However, because of the time that this function needs to execute, depending on the volume of log files retrieved and transferred, it is possible that Lambda could time out on many instances. Another approach is launching an Amazon EC2 instance that runs this job periodically. However, this would require you to run an EC2 instance continuously, not an optimal use of time or money.
Using the new Amazon CloudWatch integration with Amazon EC2 Container Service, you can trigger this job to run in a container on an existing Amazon ECS cluster. Additionally, this would allow you to improve costs by running containers on a fleet of Spot Instances.
In this post, I will show you how to use the new scheduled tasks (cron) feature in Amazon ECS and launch tasks using CloudWatch events, while leveraging Spot Fleet to maximize availability and cost optimization for containerized workloads.
Architecture
The following diagram shows how the various components described schedule a task that retrieves log files from Amazon RDS database instances, and deposits the logs into an S3 bucket.
Amazon ECS cluster container instances are using Spot Fleet, which is a perfect match for the workload that needs to run when it can. This improves cluster costs.
The task definition defines which Docker image to retrieve from the Amazon EC2 Container Registry (Amazon ECR) repository and run on the Amazon ECS cluster.
The container image has Python code functions to make AWS API calls using boto3. It iterates over the RDS database instances, retrieves the logs, and deposits them in the S3 bucket. Many customers choose these logs to be delivered to their centralized log-store. CloudWatch Events defines the schedule for when the container task has to be launched.
Walkthrough
To provide the basic framework, we have built an AWS CloudFormation template that creates the following resources:
Amazon ECR repository for storing the Docker image to be used in the task definition
S3 bucket that holds the transferred logs
Task definition, with image name and S3 bucket as environment variables provided via input parameter
CloudWatch Events rule
Amazon ECS cluster
Amazon ECS container instances using Spot Fleet
IAM roles required for the container instance profiles
Before you begin
Ensure that Git, Docker, and the AWS CLI are installed on your computer.
Run the CloudFormation stack and get the names for the Amazon ECR repo and S3 bucket. In the stack, choose Outputs.
Open the ECS console and choose Repositories. The rdslogs repo has been created. Choose View Push Commands and follow the instructions to connect to the repository and push the image for the code that you built in Step 2. The screenshot shows the final result:
Associate the CloudWatch scheduled task with the created Amazon ECS Task Definition, using a new CloudWatch event rule that is scheduled to run at intervals. The following rule is scheduled to run every 15 minutes: aws --profile default --region us-west-2 events put-rule --name demo-ecs-task-rule --schedule-expression "rate(15 minutes)"
CloudWatch requires IAM permissions to place a task on the Amazon ECS cluster when the CloudWatch event rule is executed, in addition to an IAM role that can be assumed by CloudWatch Events. This is done in three steps:
Create the IAM role to be assumed by CloudWatch. aws --profile default --region us-west-2 iam create-role --role-name Test-Role --assume-role-policy-document file://event-role.json
Create the IAM policy defining the ECS cluster and task definition. You need to get these values from the CloudFormation outputs and resources. aws --profile default --region us-west-2 iam create-policy --policy-name test-policy --policy-document file://event-policy.json
Attach the IAM policy to the role. aws --profile default --region us-west-2 iam attach-role-policy --role-name Test-Role --policy-arn arn:aws:iam::1234567890:policy/test-policy
Associate the CloudWatch rule created earlier to place the task on the ECS cluster. The following command shows an example. Replace the AWS account ID and region with your settings. aws events put-targets --rule demo-ecs-task-rule --targets "Id"="1","Arn"="arn:aws:ecs:us-west-2:12345678901:cluster/test-cwe-blog-ecsCluster-15HJFWCH4SP67","EcsParameters"={"TaskDefinitionArn"="arn:aws:ecs:us-west-2:12345678901:task-definition/test-cwe-blog-taskdef:8"},"RoleArn"="arn:aws:iam::12345678901:role/Test-Role"
{
"FailedEntries": [],
"FailedEntryCount": 0
}
That’s it. The logs now run based on the defined schedule.
To test this, open the Amazon ECS console, select the Amazon ECS cluster that you created, and then choose Tasks, Run New Task. Select the task definition created by the CloudFormation template, and the cluster should be selected automatically. As this runs, the S3 bucket should be populated with the RDS logs for the instance.
Conclusion
In this post, you’ve seen that the choices for workloads that need to run at a scheduled time include Lambda with CloudWatch events or EC2 with cron. However, sometimes the job could run outside of Lambda execution time limits or be not cost-effective for an EC2 instance.
In such cases, you can schedule the tasks on an ECS cluster using CloudWatch rules. In addition, you can use a Spot Fleet cluster with Amazon ECS for cost-conscious workloads that do not have hard requirements on execution time or instance availability in the Spot Fleet. For more information, see Powering your Amazon ECS Cluster with Amazon EC2 Spot Instances and Scheduled Events.
If you have questions or suggestions, please comment below.
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