Amazon Aurora PostgreSQL and Amazon DynamoDB zero-ETL integrations with Amazon Redshift now generally available

Today, I am excited to announce the general availability of Amazon Aurora PostgreSQL-Compatible Edition and Amazon DynamoDB zero-ETL integrations with Amazon Redshift. Zero-ETL integration seamlessly makes transactional or operational data available in Amazon Redshift, removing the need to build and manage complex data pipelines that perform extract, transform, and load (ETL) operations. It automates the replication of source data to Amazon Redshift, simultaneously updating source data for you to use in Amazon Redshift for analytics and machine learning (ML) capabilities to derive timely insights and respond effectively to critical, time-sensitive events.

Using these new zero-ETL integrations, you can run unified analytics on your data from different applications without having to build and manage different data pipelines to write data from multiple relational and non-relational data sources into a single data warehouse. In this post, I provide two step-by-step walkthroughs on how to get started with both Amazon Aurora PostgreSQL and Amazon DynamoDB zero-ETL integrations with Amazon Redshift.

To create a zero-ETL integration, you specify a source and Amazon Redshift as the target. The integration replicates data from the source to the target data warehouse, making it available in Amazon Redshift seamlessly, and monitors the pipeline’s health.

Let’s explore how these new integrations work. In this post, you will learn how to create zero-ETL integrations to replicate data from different source databases (Aurora PostgreSQL and DynamoDB) to the same Amazon Redshift cluster. You will also learn how to select multiple tables or databases from Aurora PostgreSQL source databases to replicate data to the same Amazon Redshift cluster. You will observe how zero-ETL integrations provide flexibility without the operational burden of building and managing multiple ETL pipelines.

Getting started with Aurora PostgreSQL zero-ETL integration with Amazon Redshift
Before creating a database, I create a custom cluster parameter group because Aurora PostgreSQL zero-ETL integration with Amazon Redshift requires specific values for the Aurora DB cluster parameters. In the Amazon RDS console, I go to Parameter groups in the navigation pane. I choose Create parameter group.

I enter custom-pg-aurora-postgres-zero-etl for Parameter group name and Description. I choose Aurora PostgreSQL for Engine type and aurora-postgresql16 for Parameter group family (zero-ETL integration works with PostgreSQL 16.4 or above versions). Finally, I choose DB Cluster Parameter Group for Type and choose Create.

Next, I edit the newly created cluster parameter group by choosing it on the Parameter groups page. I choose Actions and then choose Edit. I set the following cluster parameter settings:

• rds.logical_replication=1
• aurora.enhanced_logical_replication=1
• aurora.logical_replication_backup=0
• aurora.logical_replication_globaldb=0

I choose Save Changes.

Next, I create an Aurora PostgreSQL database. When creating the database, you can set the configurations according to your needs. Remember to choose Aurora PostgreSQL (compatible with PostgreSQL 16.4 or above) from Available versions and the custom cluster parameter group (custom-pg-aurora-postgres-zero-etl in this case) for DB cluster parameter group in the Additional configuration section.

After the database becomes available, I connect to the Aurora PostgreSQL cluster, create a database named books, create a table named book_catalog in the default schema for this database and insert sample data to use with zero-ETL integration.

To get started with zero-ETL integration, I use an existing Amazon Redshift data warehouse. To create and manage Amazon Redshift resources, visit the Amazon Redshift Getting Started Guide.

In the Amazon RDS console, I go to the Zero-ETL integrations tab in the navigation pane and choose Create zero-ETL integration. I enter postgres-redshift-zero-etl for Integration identifier and Amazon Aurora zero-ETL integration with Amazon Redshift for Integration description. I choose Next.

On the next page, I choose Browse RDS databases to select the source database. For the Data filtering options, I use database.schema.table pattern. I include my table called book_catalog in Aurora PostgreSQL books database. The * in filters will replicate all book_catalog tables in all schemas within books database. I choose Include as filter type and enter books.*.book_catalog into the Filter expression field. I choose Next.

On the next page, I choose Browse Redshift data warehouses and select the existing Amazon Redshift data warehouse as the target. I must specify authorized principals and integration source on the target to enable Amazon Aurora to replicate into the data warehouse and enable case sensitivity. Amazon RDS can complete these steps for me during setup, or I can configure them manually in Amazon Redshift. For this demo, I choose Fix it for me and choose Next.

After the case sensitivity parameter and the resource policy for data warehouse are fixed, I choose Next on the next Add tags and encryption page. After I review the configuration, I choose Create zero-ETL integration.

After the integration succeeded, I choose the integration name to check the details.

Now, I need to create a database from integration to finish setting up. I go to the Amazon Redshift console, choose Zero-ETL integrations in the navigation pane and select the Aurora PostgreSQL integration I just created. I choose Create database from integration.

I choose books as Source named database and I enter zeroetl_aurorapg as the Destination database name. I choose Create database.

After the database is created, I return to the Aurora PostgreSQL integration page. On this page, I choose Query data to connect to the Amazon Redshift data warehouse to observe if the data is replicated. When I run a select query in the zeroetl_aurorapg database, I see that the data in book_catalog table is replicated to Amazon Redshift successfully.

As I said in the beginning, you can select multiple tables or databases from the Aurora PostgreSQL source database to replicate the data to the same Amazon Redshift cluster. To add another database to the same zero-ETL integration, all I have to do is to add another filter to the Data filtering options in the form of database.schema.table, replacing the database part with the database name I want to replicate. For this demo, I will select multiple tables to be replicated to the same data warehouse. I create another table named publisher in the Aurora PostgreSQL cluster and insert sample data to it.

I edit the Data filtering options to include publisher table for replication. To do this, I go to the postgres-redshift-zero-etl details page and choose Modify. I append books.*.publisher using comma in the Filter expression field. I choose Continue. I review the changes and choose Save changes. I observe that the Filtered data tables section on the integration details page has now 2 tables included for replication.

When I switch to the Amazon Redshift Query editor and refresh the tables, I can see that the new publisher table and its records are replicated to the data warehouse.

Now that I completed the Aurora PostgreSQL zero-ETL integration with Amazon Redshift, let’s create a DynamoDB zero-ETL integration with the same data warehouse.

Getting started with DynamoDB zero-ETL integration with Amazon Redshift
In this part, I proceed to create an Amazon DynamoDB zero-ETL integration using an existing Amazon DynamoDB table named Book_Catalog. The table has 2 items in it:

I go to the Amazon Redshift console and choose Zero-ETL integrations in the navigation pane. Then, I choose the arrow next to the Create zero-ETL integration and choose Create DynamoDB integration. I enter dynamodb-redshift-zero-etl for Integration name and Amazon DynamoDB zero-ETL integration with Amazon Redshift for Description. I choose Next.

On the next page, I choose Browse DynamoDB tables and select the Book_Catalog table. I must specify a resource policy with authorized principals and integration sources, and enable point-in-time recovery (PITR) on the source table before I create an integration. Amazon DynamoDB can do it for me, or I can change the configuration manually. I choose Fix it for me to automatically apply the required resource policies for the integration and enable PITR on the DynamoDB table. I choose Next.

Then, I choose my existing Amazon Redshift Serverless data warehouse as the target and choose Next.

I choose Next again in the Add tags and encryption page and choose Create DynamoDB integration in the Review and create page.

Now, I need to create a database from integration to finish setting up just like I did with Aurora PostgreSQL zero-ETL integration. In the Amazon Redshift console, I choose the DynamoDB integration and I choose Create database from integration. In the popup screen, I enter zeroetl_dynamodb as the Destination database name and choose Create database.

After the database is created, I go to the Amazon Redshift Zero-ETL integrations page and choose the DynamoDB integration I created. On this page, I choose Query data to connect to the Amazon Redshift data warehouse to observe if the data from DynamoDB Book_Catalog table is replicated. When I run a select query in the zeroetl_dynamodb database, I see that the data is replicated to Amazon Redshift successfully. Note that the data from DynamoDB is replicated in SUPER datatype column and can be accessed using PartiQL sql.

I insert another entry to the DynamoDB Book_Catalog table.

When I switch to the Amazon Redshift Query editor and refresh the select query, I can see that the new record is replicated to the data warehouse.

Zero-ETL integrations between Aurora PostgreSQL and DynamoDB with Amazon Redshift help you unify data from multiple database clusters and unlock insights in your data warehouse. Amazon Redshift allows cross-database queries and materialized views based off the multiple tables, giving you the opportunity to consolidate and simplify your analytics assets, improve operational efficiency, and optimize cost. You no longer have to worry about setting up and managing complex ETL pipelines.

Now available
Aurora PostgreSQL zero-ETL integration with Amazon Redshift is now available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) AWS Regions.

Amazon DynamoDB zero-ETL integration with Amazon Redshift is now available in all commercial, China and GovCloud AWS Regions.

For pricing information, visit the Amazon Aurora and Amazon DynamoDB pricing pages.

To get started with this feature, visit Working with Aurora zero-ETL integrations with Amazon Redshift and Amazon Redshift Zero-ETL integrations documentation.

— Esra

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AWS Weekly Roundup: What’s App, AWS Lambda, Load Balancers, AWS Console, and more (Oct 14, 2024).

Last week, AWS hosted free half-day conferences in London and Paris. My colleagues and I demonstrated how developers can use generative AI tools to speed up their design, analysis, code writing, debugging, and deployment workflows. These events were held at the GenAI Lofts. These lofts are open until October 25 (London) and November 5 (Paris). They will be packed with events, conferences, workshops, and meetups. If you’re around, be sure to check the agenda (London, Paris).

Our well-known AWS News blog co-author Veliswa did an amazing demo. She live-coded a Duolingo-like app from scratch, just using suggestions and reviews from Amazon Q Developer.

Now, let’s turn to other exciting news in the AWS universe from last week.

Last week’s launches
Here are some launches that got my attention:

Bring your conversations to WhatsApp — AWS has added support for What’sApp to AWS End User Messaging, so developers can reach users on WhatsApp with multimedia and interactive messaging options. This feature integrates with SMS and push notifications already available. Developers can get started quickly using AWS Management Console.

Amazon Redshift data sharing with data lake tables — This offers a secure and convenient way to share live data lake tables across different Amazon Redshift warehouses. Data sharing of data lake tables in AWS Glue Data Catalog provides live access to the data, so you always see the most up-to-date and consistent information as it’s updated in the data lake.

Zonal shift and zonal autoshift for cross zoned Network Load Balancer — Network Load Balancer (NLB) now supports the Amazon Application Recovery Controller zonal shift and zonal autoshift features on load balancers that are enabled across zones. With Zonal shift, you can quickly shift traffic away from an impaired Availability Zone and recover from events such as bad application deployment and gray failures. Zonal autoshift safely and automatically shifts your traffic away from an Availability Zone when AWS identifies a potential impact to it.

Console to Code to generate infrastructure as a service code — This is by far my favorite launch of the week. Console to Code makes it simple, fast, and cost-effective to move from prototyping in the AWS Management Console to building code for production deployments. You can generate code for their console actions in their preferred format with a single click. The generated code helps you get started and bootstrap your automation pipelines for tasks. Console to Code is powered by Amazon Q Developer.

A new getting started experience for AWS CodePipeline — AWS Data Pipeline introduces a simplified and new getting started experience so you can quickly create new pipelines. When you create a new pipeline using the CodePipeline console, you can now select from a list of pipeline templates across build, automation, and deployment use cases. After selecting a pipeline template, you will be prompted to enter values for the action configuration fields in the pipeline definition, and completing the process will render a fully configured pipeline that’s ready to run.

AWS Lambda detects and stops recursive loops between Lambda and Amazon S3 — Lambda recursive loop detection can now automatically detect and stop recursive loops between AWS Lambda and Amazon Simple Storage Service (Amazon S3). Lambda recursive loop detection, which is enabled by default, is a preventative guardrail that automatically detects and stops recursive invocations between Lambda and other supported services, preventing unintended usage and billing from runaway workloads.

Amazon MemoryDB for Valkey — Amazon MemoryDB for Redis is a fully managed, Valkey– and Redis OSS-compatible database service, which provides multi-AZ durability, microsecond read and single-digit millisecond write latency, and high throughput. It is ideal for use cases such as caching, leaderboards, and session stores. With MemoryDB for Valkey, you can benefit from a fully managed experience built on open-source technology while using the security, operational excellence, and reliability that AWS provides. MemoryDB for Valkey also delivers the fastest vector search performance at the highest recall rates among popular vector databases on AWS.

Amazon Polly adds four wew English voices for the generative engine and expands to three Regions — Polly is a managed service that turns text into lifelike speech, so you can create applications that talk and to build speech-enabled products depending on your business needs. The generative engine is the most advanced Amazon Polly text-to-speech (TTS) model. With this launch, we add a variety of new synthetic generative English voices to the Amazon Polly portfolio: an Australian English voice Olivia and three US English voices Joanna, Danielle, and Stephen. These voices have more natural pronunciation and prosody. You can use this high-tier product in various industries and for different purposes such as education, publishing, or marketing.

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

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS Cloud Day Prague — Join us for a free technical conferences in Prague on October 23. I will be there and share with attendees “The Art of Transforming a Foundation Model into a Domain Expert”. Be sure to register today!

Innovate Migrate, Modernize, and Build — Whether you are new to the cloud or an experienced user, you will learn something new at AWS Innovate. This is a free online conference. Register for a time and region convenient to North America (October 15), or Europe, Middle East & Africa (October 24).

AWS Community Days — Join community-led conferences featuring technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. Don’t miss out on the AWS Community Days happening on October 19 in Vadodara, Spain, and Guatemala.

AWS re:Invent 2024 — Registration is now open for the annual tech extravaganza, taking place December 2 – 6 in Las Vegas. Beside recording podcast episodes, I will present three sessions:

• CMP410 | Accelerate testing cycles of CI/CD pipelines with EC2 Mac instances (with Vishal)
• DEV301 | The art of transforming foundation models into domain experts (with Gregory)
• DEV334 | Swift, server-side, serverless

There are just a few seats left for these three sessions, so be sure to book your seat today!

Browse more upcoming AWS led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

-- seb

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

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Convert AWS console actions to reusable code with AWS Console-to-Code, now generally available

Today, we are announcing the general availability (GA) of AWS Console-to-Code that makes it easy to convert AWS console actions to reusable code. You can use AWS Console-to-Code to record your actions and workflows in the console, such as launching an Amazon Elastic Compute Cloud (Amazon EC2) instance, and review the AWS Command Line Interface (AWS CLI) commands for your console actions. With just a few clicks, Amazon Q can generate code for you using the infrastructure-as-code (IaC) format of your choice, including AWS CloudFormation template (YAML or JSON), and AWS Cloud Development Kit (AWS CDK) (TypeScript, Python or Java). This can be used as a starting point for infrastructure automation and further customized for your production workloads, included in pipelines, and more.

Since we announced the preview last year, AWS Console-to-Code has garnered positive response from customers. It has now been improved further in this GA version, because we have continued to work backwards from customer feedback.

New features in GA

• Support for more services – During preview, the only supported service was Amazon EC2. At GA, AWS Console-to-Code has extended support to include Amazon Relational Database Service (RDS) and Amazon Virtual Private Cloud (Amazon VPC).
• Simplified experience – The new user experience makes it easier for customers to manage the prototyping, recording and code generation workflows.
• Preview code – The launch wizards for EC2 instances and Auto Scaling groups have been updated to allow customers to generate code for these resources without actually creating them.
• Advanced code generation – AWS CDK and CloudFormation code generation is powered by Amazon Q machine learning models.

Getting started with AWS Console-to-Code
Let’s begin with a simple scenario of launching an Amazon EC2 instance. Start by accessing the Amazon EC2 console. Locate the AWS Console-to-Code widget on the right and choose Start recording to initiate the recording.

Now, launch an Amazon EC2 instance using the launch instance wizard in the Amazon EC2 console. After the instance is launched, choose Stop to complete the recording.

In the Recorded actions table, review the actions that were recorded. Use the Type dropdown list to filter by write actions (Write). Choose the RunInstances action. Select Copy CLI to copy the corresponding AWS CLI command.

This is the CLI command that I got from AWS Console-to-Code:

aws ec2 run-instances \
  --image-id "ami-066784287e358dad1" \
  --instance-type "t2.micro" \
  --network-interfaces '{"AssociatePublicIpAddress":true,"DeviceIndex":0,"Groups":["sg-1z1c11zzz1c11zzz1"]}' \
  --credit-specification '{"CpuCredits":"standard"}' \
  --tag-specifications '{"ResourceType":"instance","Tags":[{"Key":"Name","Value":"c2c-demo"}]}' \
  --metadata-options '{"HttpEndpoint":"enabled","HttpPutResponseHopLimit":2,"HttpTokens":"required"}' \
  --private-dns-name-options '{"HostnameType":"ip-name","EnableResourceNameDnsARecord":true,"EnableResourceNameDnsAAAARecord":false}' \
  --count "1"

This command can be easily modified. For this example, I updated it to launch two instances (--count 2) of type t3.micro (--instance-type). This is a simplified example, but the same technique can be applied to other workflows.

I executed the command using AWS CloudShell and it worked as expected, launching two t3.micro EC2 instances:

The single-click CLI code generation experience is based on the API commands that were used when actions were executed (while launching the EC2 instance). Its interesting to note that the companion screen surfaces recorded actions as you complete them in console. And thanks to the interactive UI with start and stop functionality, its easy to clearly scope actions for prototyping.

IaC generation using AWS CDK
AWS CDK is an open-source framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. With AWS Console-to-Code, you can generate AWS CDK code (currently in Java, Python and TypeScript) for your infrastructure workflows.

Lets continue with the EC2 launch instance use case. If you haven’t done it already, in the Amazon EC2 console, locate the AWS Console-to-Code widget on the right, choose Start recording, and launch an EC2 instance. After the instance is launched, choose Stop to complete the recording and choose the RunInstances action from the Recorded actions table.

To generate AWS CDK Python code, choose the Generate CDK Python button from the dropdown list.

You can use the code as a starting point, customizing it to make it production-ready for your specific use case.

I already had the AWS CDK installed, so I created a new Python CDK project:

mkdir c2c_cdk_demo
cd c2c_cdk_demo
cdk init app --language python

Then, I plugged in the generated code in the Python CDK project. For this example, I refactored the code into a AWS CDK Stack, changed the EC2 instance type, and made other minor changes to ensure that the code was correct. I successfully deployed it using cdk deploy.

I was able to go from the console action to launch an EC2 instance and then all the way to AWS CDK to reproduce the same result.

from aws_cdk import (
    Stack,
    aws_ec2 as ec2,
)
from constructs import Construct

class MyProjectStack(Stack):

    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
        super().__init__(scope, construct_id, **kwargs)

        existing_vpc = ec2.Vpc.from_lookup(self, "ExistingVPC",
            is_default=True
        )

        instance = ec2.Instance(self, "Instance",
                instance_type=ec2.InstanceType("t3.micro"),
                machine_image=ec2.AmazonLinuxImage(),
                vpc=existing_vpc,
                vpc_subnets=ec2.SubnetSelection(
                    subnet_type=ec2.SubnetType.PUBLIC
                )
        )

You can also generate CloudFormation template in YAML or JSON format:

Preview code
You can also directly access AWS Console-to-Code from Preview code feature in Amazon EC2 and Amazon EC2 Auto Scaling group launch experience. This means that you don’t have to actually create the resource in order to get the infrastructure code.

To try this out, follow the steps to create an Auto Scaling group using a launch template. However, instead of Create Auto Scaling group, click Preview code. You should now see the options to generate infrastructure code or copy the AWS CLI command.

Things to know
Here are a few things you should consider while using AWS Console-to-Code:

• Anyone can use AWS Console-to-Code to generate AWS CLI commands for their infrastructure workflows. The code generation feature for AWS CDK and CloudFormation formats has a free quota of 25 generations per month, after which you will need an Amazon Q Developer subscription.
• It’s recommended that you test and verify the generated IaC code code before deployment.
• At GA, AWS Console-to-Code only records actions in Amazon EC2, Amazon VPC and Amazon RDS consoles.
• The Recorded actions table in AWS Console-to-Code only display actions taken during the current session within the specific browser tab, and it does not retain actions from previous sessions or other tabs. Note that refreshing the browser tab will result in the loss of all recorded actions.

Now available
AWS Console-to-Code is available in all commercial Regions. You can learn more about it in the Amazon EC2 documentation. Give it a try in the Amazon EC2 console and send feedback to the AWS re:Post for Amazon EC2 or through your usual AWS Support contacts.

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AWS Weekly Roundup: HIPAA eligible with Amazon Q Business, Amazon DCV, AWS re:Post Agent, and more (Oct 07, 2024)

Last Friday, I had the privilege of attending China Engineer’s Day 2024(CED 2024) in Hangzhou as the Amazon Web Services (AWS) speaker. The event was organized by the China Computer Federation (CCF), one of the most influential professional developer communities in China.

At CED 2024, I spoke about how AI development tools can improve developer productivity. I was honored to receive a certificate of excellence from CCF, and Amazon Q garnered significant attention from the attendees.

Now, let’s turn to other exciting news in the AWS universe from last week.

Last week’s launches
Here are some launches that got my attention:

Amazon Q Business is now HIPAA eligible – Amazon Q business has received Health Insurance Portability and Accountability Act (HIPAA) certification. This means healthcare and life sciences organizations such as health insurance companies and healthcare providers can now use Amazon Q Business to run sensitive workloads regulated under the US HIPAA law.

NICE DCV renames to Amazon DCV – NICE DCV is rebranded to Amazon DCV. This high performance remote display protocol allows secure delivery of remote desktops and application streaming from any cloud or data center to any device, even over varying network conditions. Amazon DCV supports both Windows and major Linux distributions on the server side. Clients can use native DCV client for Windows, Linux, or macOS, as well as web browsers, to receive desktops and application streamings. The DCV server and client only transfer encrypted pixels, not data, ensuring no confidential information is downloaded. When using Amazon DCV on AWS with Amazon Elastic Compute Cloud (Amazon EC2), you can take advantage of the AWS 108 Availability Zones across the 33 geographic Regions and 31 local zones. The 2024.0 release now supports the latest Ubuntu 24.04 LTS. For more details, check out Sébastien Stormacq’s new launch blog post.

AWS re:Post launches re:Post Agent – AWS re:Post provides access to curated knowledge and a vibrant community that helps users become even more successful on AWS. re:Post Agent is a generative AI assistant designed to provide rapid, intelligent responses to questions in the re:Post community. It expands the available AWS knowledge base, and community experts will earn reputation points by reviewing the AI-generated answers.

Advanced configuration with Amazon Timestream for InfluxDB – This new launch introduces a feature that allows uses to monitor instance CPU, memory, and disk utilization metrics directly from the AWS Management Console.

A new stop ingestion API of Amazon Bedrock Knowledge Bases – This new API allows users to halt ongoing ingestion jobs at will. Providing greater control over data ingestion workflows, users can quickly stop accidental or unwanted ingestion processes without waiting for completion. By using the new StopIngestionJob API, you can respond rapidly to evolving needs and potentially reduce costs. This capability is available across all AWS Regions where Amazon Bedrock Knowledge Bases are offered.

Higher storage limit of Amazon AppStream 2.0 – Amazon AppStream 2.0 has expanded the default size limit for application settings persistence from 1 GB to 5 GB. This increase allows end users to store more application data and settings without manual intervention and without affecting performance or session setup time.

There were over 40 launches and releases last week. It was difficult for me to select the important ones. In addition to those already mentioned, here’s a list of potentially important feature updates:

• Amazon Aurora Serverless v2 now supports up to 256 ACUs
• Amazon Aurora MySQL now supports RDS Data API
• Amazon Aurora supports PostgreSQL 16.4, 15.8, 14.13, 13.16, and 12.20
• Amazon Redshift now offers the RA3.large instance.

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

Other AWS news
Here are some other noteworthy items from last week.

Amazon WorkSpaces Thin Client – Amazon WorkSpaces Thin Client inventory is now available to purchase in the UK on Amazon Business, in addition to the US, France, Germany, Italy, and Spain. It’s a sleek, cost-effective device that brings secure access to AWS end user computing services right to your fingertips. This nifty gadget is like a digital fortress, preventing unauthorized data storage and applications, while giving IT admins the tools to manage and monitor their fleet of thin clients with ease.

Helping communities impacted by Hurricane Helene – AWS Disaster Response team is working closely with local partners and humanitarian organizations to deliver critical supplies to those in need in the Southeast. We’re also deploying AWS technology to help with re-connectivity, aid relief operations on the ground, and support food distribution needs in the region.

The life of a prescription at Amazon Pharmacy – Read the Amazon Pharmacy AI use case to remove the complexity of the process of dispensing medications and improve patients’ experiences. The system transcribes raw prescription data into standardized formats, transforms medical abbreviations into full-text equivalents, and validates medication details against an industry database. This automated process, followed by pharmacist review, has reduced potential medication errors by 50 percent and improved processing speed by up to 90 percent, allowing pharmacists to focus on critical tasks and personalized care.

A thought leadership article on generative AI in the WIRED magazine – Read Antje‘s news column in Wired. It discusses how AWS opens the transformative power of AI to organizations of any size and level of experience. I recommend it to all AI enthusiasts and business innovators. AWS is on a mission to bring generative AI magic to businesses of all sizes, offering a buffet of AI tools for tech wizards and newcomers alike. Whether you’re a startup with big dreams or a corporate giant looking to stay ahead, AWS is rolling out the red carpet to the AI revolution. Don’t miss this chance to turn your wildest tech fantasies into reality!

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2024 – Registration is now open for the annual tech extravaganza, taking place December 2 – 6 in Las Vegas. I’m eager to learn about the new launches and excited to contribute to two chalk talks focusing on security topics (Dev311 – Enhance code security with generative AI and SEC228 – Navigate multi-level protection scheme compliance in AWS China Regions).

AWS Innovate Migrate, Modernize, and Build – Whether you are new to the cloud or an experienced user, you will learn something new at AWS Innovate. This is a free online conference. Register at a time and region convenient to North America (October 15), or Europe, Middle East & Africa (October 24).

AWS Community Days – Join community-led conferences featuring technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. Don’t miss out on the AWS Community Days happening on October 12 in Sofia and October 19 in Vadodara, Spain, and Guatemala.

Browse more upcoming AWS led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Betty

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

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NICE DCV is now Amazon DCV with 2024.0 release

Today, NICE DCV has a new name. So long NICE DCV, welcome Amazon DCV. Today, with the 2024.0 release, along with enhancements and bug fixes, NICE DCV is rebranded to Amazon DCV.

The new name is now also used to consistently refer to the DCV protocol powering AWS managed services such as Amazon AppStream 2.0 and Amazon WorkSpaces.

What is Amazon DCV
Amazon DCV is a high-performance remote display protocol. It lets you securely deliver remote desktops and application streaming from any cloud or data center to any device, over varying network conditions. By using Amazon DCV with Amazon Elastic Compute Cloud (Amazon EC2), you can run graphics-intensive applications remotely on EC2 instances. You can then stream the results to more modest client machines, which eliminates the need for expensive dedicated workstations.

Amazon DCV supports both Windows and major flavors of Linux operating systems on the server side, providing you flexibility to fit your organization’s needs. The client-side that receives the desktops and application streamings could be the native DCV client for Windows, Linux, or macOS or web browsers. The DCV remote server and client transfer only encrypted pixels, not data, so no confidential data is downloaded from the DCV server. When you choose to use Amazon DCV on Amazon Web Services (AWS) with EC2 instances, you can take advantage of the AWS 108 Availability Zones across the 33 geographic Regions and 31 local zones, allowing your remote streaming services to scale globally.

Since Amazon acquired NICE 8 years ago, we’ve witnessed a diverse range of customers adopting DCV. From general-purpose users visualizing business applications to industry-specific professionals, DCV has proven to be versatile. For instance, artists have employed DCV to access powerful cloud workstations for their digital content creation and rendering tasks. In the healthcare sector, medical imaging professionals have used DCV for remote visualization and analysis of patient data. Geoscientists have used DCV to analyze reservoir simulation results, while engineers in manufacturing have used it to visualize computational fluid dynamics experiments. The education and IT support industries have benefited from collaborative sessions in DCV, in which multiple users can share a single desktop.

Notable customers include Quantic Dream, an award-winning game development studio that has harnessed DCV to create high-resolution, low-latency streaming services for their artists and developers. Tally Solutions, an enterprise resource planning (ERP) services provider, has employed DCV to securely stream its ERP software to thousands of customers. Volkswagen has used DCV to provide remote access to computer-aided engineering (CAE) applications for over 1,000 automotive engineers. Amazon Kuiper, an initiative to bring broadband connectivity to underserved communities, has used DCV for designing complex chips.

Within AWS, DCV has been adopted by several services to provide managed solutions to customers. For example, AppStream 2.0 uses DCV to offer secure, reliable, and scalable application streaming. Additionally, since 2020, Amazon WorkSpaces Streaming Protocol (WSP), which is built on DCV and optimized for high performance, is available for Amazon WorkSpaces customers. Today, we’re also phasing out the WSP name and replacing it with DCV. Going forward, you will have DCV as a primary protocol choice in Amazon WorkSpaces.

What’s new with version 2024.0
Amazon DCV 2024.0 introduces several fixes and enhancements for improved performance, security, and ease of use. The 2024.0 release now supports the latest Ubuntu 24.04 LTS, bringing the latest security updates and extended long-term support to simplify system maintenance. The DCV client on Ubuntu 24.04 has built in support for Wayland, offering better graphical rendering efficiency and enhanced application isolation. Additionally, DCV 2024.0 now enables the QUIC UDP protocol by default, allowing clients to benefit from an optimized streaming experience. The release also introduces the capability to blank the Linux host screen when a remote user is connected, preventing local access and interaction with the remote session.

How to get started
The easiest way to test DCV is to spin up a WorkSpaces instance from the WorkSpaces console, selecting one of the DCV-powered bundles, or creating an AppStream session. For this demo however, I want to show you how to install DCV server on an EC2 instance.

I installed DCV server on two servers running on Amazon EC2, one running Windows Server 2022 and one running Ubuntu 24.04. I also installed the client on my macOS laptop. The client and server packages are available to download on our website. For both servers, make sure the security group authorizes inbound connection on UDP or TCP port 8443, the default port DCV uses.

The Windows installation is straightforward: start the msi file, select Next at each step and voilà. It was installed in less time than it took me to write this sentence.

The installation on Linux deserves a bit more care. Amazon Machine Images (AMI) for EC2 servers don’t include any desktop or graphical components. As a prerequisite, I had to install the X Window System and a window manager, and configure X to let users connect and start a graphical user interface session on the server. Fortunately, all these steps are well documented. Here is a summary of the commands I used.

# install desktop packages 
$ sudo apt install ubuntu-desktop

# install a desktop manager 
$ sudo apt install gdm3

# reboot
$ sudo reboot

After the reboot, I installed the DCV server package

# Install the server 
$ sudo apt install ./nice-dcv-server_2024.0.17794-1_amd64.ubuntu2404.deb
$ sudo apt install ./nice-xdcv_2024.0.625-1_amd64.ubuntu2404.deb

# (optional) install the DCV web viewer to allow clients to connect from a web browser
$ sudo apt install ./nice-dcv-web-viewer_2024.0.17794-1_amd64.ubuntu2404.deb

Because my server had no GPU, I also followed these steps to install X11 Dummy driver and configure X11 to use it.

Then, I started the service:

$ sudo systemctl enable dcvserver.service 
$ sudo systemctl start dcvserver.service 
$ sudo systemctl status dcvserver.service 

I created a user at the operating system level and assigned a password and a home directory. Then, I checked my setup on the server before trying to connect from the server.

$ sudo dcv list-sessions
There are no sessions available.

$ sudo dcv create-session console --type virtual --owner seb

$ sudo dcv list-sessions
Session: 'console' (owner:seb type:virtual)

Once my server configuration was ready, I started the DCV client on my laptop. I only had to enter the IP address of the server and the username and password of the user to initiate a session.

On my laptop, I opened a new DCV client window and connected to the other EC2 server. After a few seconds, I was able to remotely work with the Windows and the Ubuntu machine running in the cloud.

In this example, I focus on installing Amazon DCV on a single EC2 instance. However, when building your own service infrastructure, you may want to explore the other components that are part of the DCV offering: Amazon DCV Session Manager, Amazon DCV Access Console, and Amazon DCV Connection Gateway.

Pricing and availability
Amazon DCV is free of charges when used on AWS. You only pay for the usage of AWS resources or services, such as EC2 instances, Amazon Workspace desktops, or Amazon App Stream 2.0. If you plan to use DCV with on-premises servers, check the list of license resellers on our website.

Now go build your own servers with DCV.

-- seb

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AWS Weekly Roundup: Jamba 1.5 family, Llama 3.2, Amazon EC2 C8g and M8g instances and more (Sep 30, 2024)

Every week, there’s a new Amazon Web Services (AWS) community event where you can network, learn something new, and immerse yourself in the community. When you’re in a community, everyone grows together, and no one is left behind. Last week was no exception. I can highlight the Dach AWS Community Day where Viktoria Semaan closed with a talk titled How to Create Impactful Content and Build a Strong Personal Brand, and the Peru User Group, who organized two days of talks and learning opportunities: UGCONF & SERVERLESSDAY 2024, featuring Jeff Barr, who spoke about how to Create Your Own Luck. The community events continue, so check them out at Upcoming AWS Community Days.

Last week’s launches
Here are the launches that got my attention.

Jamba 1.5 family of models by AI21 Labs is now available in Amazon Bedrock – The Jamba 1.5 Large and 1.5 Mini models feature a 256k context window, one of the longest on the market, enabling complex tasks like lengthy document analysis. With native support for structured JSON output, function calling, and document processing, they integrate into enterprise workflows for specialized AI solutions. To learn more, read Jamba 1.5 family of models by AI21 Labs is now available in Amazon Bedrock, visit the AI21 Labs in Amazon Bedrock page, and read the documentation.

AWS Lambda now supports Amazon Linux 2023 runtimes in AWS GovCloud (US) Regions – These runtimes offer the latest language features, including Python 3.12, Node.js 20, Java 21, .NET 8, Ruby 3.3, and Amazon Linux 2023. They have smaller deployment footprints, updated libraries, and a new package manager. Additionally, you can also use the container base images to build and deploy functions as a container image.

Amazon SageMaker Studio now supports automatic shutdown of idle applications – You can now enable automatic shutdown of inactive JupyterLab and CodeEditor applications using Amazon SageMaker Distribution image v2.0 or newer. Administrators can set idle shutdown times at domain or user profile levels, with optional user customization. This cost control mechanism helps avoid charges for unused instances and is available across all AWS Regions where SageMaker Studio is offered.

Amazon S3 is implementing a default 128 KB minimum object size for S3 Lifecycle transition rules to any S3 storage class – Reduce transition costs for datasets with many small objects by decreasing transition requests. Users can override the default and customize minimum object sizes. Existing rules remain unchanged, but the new default applies to new or modified configurations.

AWS Lake Formation centralized access control for Amazon Redshift data sharing is now available in 11 additional Regions – Enabling granular permissions management, including table, column, and row-level access to shared Amazon Redshift data. It also supports tag-based access control and trusted identity propagation with AWS IAM Identity Center for improved security and simplified management.

Llama 3.2 generative AI models now available in Amazon Bedrock – The collection includes 90B and 11B parameter multimodal models for sophisticated reasoning tasks, and 3B and 1B text-only models for edge devices. These models support vision tasks, offer improved performance, and are designed for responsible AI innovation across various applications. These models support a 128K context length and multilingual capabilities in eight languages. Learn more about it in Introducing Llama 3.2 models from Meta in Amazon Bedrock.

Share AWS End User Messaging SMS resources across multiple AWS accounts – You can use AWS Resource Access Manager (RAM), to share phone numbers, sender IDs, phone pools, and opt-out lists. Additionally, Amazon SNS now delivers SMS text messages through AWS End User Messaging, offering enhanced features like two-way messaging and granular permissions. These updates provide greater flexibility and control for SMS messaging across AWS services.

AWS Serverless Application Repository now supports AWS PrivateLink – Enabling direct connection from Amazon Virtual Private Cloud (VPC) without internet exposure. This enhances security by keeping communication within the AWS network. Available in all Regions where AWS Serverless Application Repository is offered, it can be set up using the AWS Management Console or AWS Command Line Interface (AWS CLI).

Amazon SageMaker with MLflow now supports AWS PrivateLink for secure traffic routing – Enabling secure data transfer from Amazon Virtual Private Cloud (VPC) to MLflow Tracking Servers within the AWS network. This enhances protection of sensitive information by avoiding public internet exposure. Available in most AWS Regions, it improves security for machine learning (ML) and generative AI experimentation using MLflow.

Introducing Amazon EC2 C8g and M8g Instances – Enhanced performance for compute-intensive and general-purpose workloads. With up to three times more vCPUs, three times more memory, 75 percent more memory bandwidth, and two times more L2 cache, these instances improve data processing, scalability, and cost-efficiency for various applications including high performance computing (HPC), batch processing, and microservices. Read more in Run your compute-
intensive and general purpose workloads sustainably with the new Amazon EC2 C8g, M8g instances.

Llama 3.2 models are now available in Amazon SageMaker JumpStart – These models offer various sizes from 1B to 90B parameters, support multimodal tasks, including image reasoning, and are more efficient for AI workloads. The 1B and 3B models can be fine-tuned, while Llama Guard 3 11B Vision supports responsible innovation and system-level safety. Learn more in Llama 3.2 models from Meta are now available in Amazon SageMaker JumpStart.

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

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases – This solution enables low-latency customer interactions, answering queries from a knowledge base. Features include conversation analytics, automated testing, and hallucination detection in a serverless architecture.

How AWS WAF threat intelligence features help protect the player experience for betting and gaming customers – AWS WAF enhances bot protection for betting and gaming. New features include browser fingerprinting, automation detection, and ML models to identify coordinated bots. These tools combat scraping, fraud, distributed denial of service (DDoS) attacks, and cheating, safeguarding player experiences.

How to migrate 3DES keys from a FIPS to a non-FIPS AWS CloudHSM cluster – Learn how to securely transfer Triple Data Encryption Algorithm (3DES) keys from Federal Information Processing Standard (FIPS) hsm1 to non-FIPS hsm2 clusters using RSA-AES wrapping, without backups. This enables using new hsm2.medium instances with FIPS 140-3 Level 3 support, non-FIPS mode, increased key capacity, and mutual TLS (mTLS).

Upcoming AWS events
Check your calendars and sign up for upcoming AWS events:

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. These events offer technical sessions, demonstrations, and workshops delivered by experts. There is only one event left that you can still register for: Ottawa (October 9).

AWS Community Days – Join community-led conferences featuring technical discussions, workshops, and hands-on labs driven by expert AWS users and industry leaders from around the world. Upcoming AWS Community Days are scheduled for October 3 in the Netherlands and Romania, and on October 5 in Jaipur, Mexico, Bolivia, Ecuador, and Panama. I’m happy to share with you that I will be joining the Panama community on October 5.

AWS GenAI Lofts – Collaborative spaces and immersive experiences that showcase AWS’s expertise with the cloud and AI, while providing startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register. I’ll be in the San Francisco lounge with some demos on October 15 at the Gen AI Developer Day. If you’re attending, feel free to stop by and say hello!

Browse all upcoming AWS led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

Thanks to Dmytro Hlotenko and Diana Alfaro for the photos of their community events.

— Eli

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

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Run your compute-intensive and general purpose workloads sustainably with the new Amazon EC2 C8g, M8g instances

Today we’re announcing general availability of the Amazon Elastic Compute Cloud (Amazon EC2) C8g and M8g instances.

C8g instances are AWS Graviton4 based and are ideal for compute-intensive workloads such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving.

Also Graviton4 based, M8g instances provide the best price performance for general purpose workloads. M8g instances are ideal for applications such as application servers, microservices, gaming servers, mid-size data stores, and caching fleets.

Now looking at some of the improvements that we have made available in both these instances. C8g and M8g instances offer larger instance sizes with up to three times more vCPUs (up to 48xl), three times the memory (up to 384GB for C8g and up to 768GB for M8g), 75 percent more memory bandwidth, and two times more L2 cache over equivalent 7g instances. This helps you to process larger amounts of data, scale up your workloads, improve time to results, and lower your total cost of ownership (TCO). These instances also offer up to 50 Gbps network bandwidth and up to 40 Gbps Amazon Elastic Block Storage (Amazon EBS) bandwidth compared to up to 30 Gbps network bandwidth and up to 20 Gbps Amazon EBS bandwidth on Graviton3-based instances. Similar to R8g instances, C8g and M8g instances offer two bare metal sizes (metal-24xl and metal-48xl). You can right size your instances and deploy workloads that benefit from direct access to physical resources.

The specs for the C8g instances are as follows.

Instance size	vCPUs	Memory (GiB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
c8g.medium	1	2	Up to 12.5	Up to 10
c8g.large	2	4	Up to 12.5	Up to 10
c8g.xlarge	4	8	Up to 12.5	Up to 10
c8g.2xlarge	8	16	Up to 15	Up to 10
c8g.4xlarge	16	32	Up to 15	Up to 10
c8g.8xlarge	32	64	15	10
c8g.12xlarge	48	96	22.5	15
c8g.16xlarge	64	128	30	20
c8g.24xlarge	96	192	40	30
c8g.48xlarge	192	384	50	40
c8g.metal-24xl	96	192	40	30
c8g.metal-48xl	192	384	50	40

The specs for the M8g instances are as follows.

Instance size	vCPUs	Memory (GiB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
m8g.medium	1	4	Up to 12.5	Up to 10
m8g.large	2	8	Up to 12.5	Up to 10
m8g.xlarge	4	16	Up to 12.5	Up to 10
m8g.2xlarge	8	32	Up to 15	Up to 10
m8g.4xlarge	16	64	Up to 15	Up to 10
m8g.8xlarge	32	128	15	10
m8g.12xlarge	48	192	22.5	15
m8g.16xlarge	64	256	30	20
m8g.24xlarge	96	384	40	30
m8g.48xlarge	192	768	50	40
m8g.metal-24xl	96	384	40	30
m8g.metal-48xl	192	768	50	40

Good to know

• AWS Graviton4 processors offer enhanced security with always-on memory encryption, dedicated caches for every vCPU, and support for pointer authentication.
• These instances are built on the AWS Nitro System which is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software. It delivers high performance, high availability, and high security, thus reducing virtualization overhead.
• The C8g and M8g instances are ideal for Linux-based workloads including containerized and microservices-based applications such as those running on Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS), as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.

Available now
C8g and M8g instances are available today in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) AWS Regions. As usual with Amazon EC2, you pay only for what you use. For more information, see Amazon EC2 Pricing. Check out the collection of AWS Graviton resources to help you start migrating your applications to Graviton instance types. You can also visit the AWS Graviton Fast Start program to begin your Graviton adoption journey.

To learn more, visit our Amazon EC2 instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

– Veliswa

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