Tag Archives: news

Build and modify apps using natural language with AWS App Studio, now generally available

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/build-and-modify-apps-using-natural-language-with-aws-app-studio-now-generally-available/

Announced as preview in July, AWS App Studio is a generative AI-powered application development service that enables users to create applications using natural language, without the need for professional software development skills. In that post, I covered how AWS App Studio helps you build secure, scalable applications and eliminates operational overhead by fully managing each application.

App Studio empowers a new set of builders to create business applications. Whether you are an IT Project Manager, Data Engineer, Enterprise Architect, or Solution Architect, simply describe your requirements in natural language, within minutes, App Studio generates fully functional applications complete with multipage UIs, data models, and custom business logic.

Today, we’re excited to announce that AWS App Studio is now generally available in the US West (Oregon) and Europe (Ireland) AWS Regions.

Building on feedback from the preview, we are introducing several new features to enhance your app building experience:

Modify your applications with natural language
During the preview period, customers shared with us that they enjoy and appreciate generating fully functional applications using natural language prompts. However, the development journey usually doesn’t stop there, and they asked if they could extend or modify their apps using natural language.

Now, with App Studio, you can modify your applications using natural language. After you’ve generated your applications, you can now describe your desired changes and the assistant will propose updates for you to review. Upon confirmation, it will automatically make the change. This feature makes it even faster and easier to customize your application.

Let’s see how it works in my IT inventory management application that I built with App Studio.

With this new feature, I can chat with the assistant to modify my applications.

To modify my application, I can provide a prompt to add another feature to my app. In this case, I need to add another text input for the web URL to get details of requested hardware, and I need to another text area to store notes.

The generative AI assistant will then process my input and provide a proposal. I can review this proposal and select Confirm to proceed.

Then, the assistant will automatically add the components and modify my application.

Add intelligence to your app with a new generative AI component
We’re also introducing a new component to make it even easier to add generative AI capabilities such as text summarization, content generation, and file analysis to your applications.

There are two ways to use this feature. First, with my canvas open, I can select the Gen AI component and drag and drop it onto the canvas. Then, while selecting the component, I can use the assistant to customize it.

Another way is to use the assistant directly. Let’s say I need a feature to analyze repair notes and provide a summary to make it easier for me to review. I can type what I need in the chat box or use the suggested prompts.

Then, the assistant will process my input and provide a proposal. I can review the proposal and select Confirm to proceed. 

App Studio will automatically add the required components. On the canvas, I see there’s a button that triggers an automation. If I need to change the underlying prompt, I can select the link that will redirect me to the respective automation. 

Under the hood, the Gen AI component is powered by a new action step called Gen AI Prompt. This new component provides an easy way to modify the prompt and input parameters to customize the output generated by the large language model (LLM).

Here’s my published app with the newly added generative AI feature to summarize repair notes.

Generate and add custom business logic with natural language
I can also use the assistant to help me add custom business logic with JavaScript in my automation.

Let’s say that I need a custom business logic to calculate repair duration and notify my stakeholders through email. Here’s the multi-step automation that I created. To add the custom logic to my automation, I choose the JavaScript component and then drag and drop it into the right spot.

Next, I need to select the action and, in the Properties panel, I select the Expand editor icon.

With this feature, I can now generate JavaScript code with natural language. Here, I provide a prompt and App Studio generates the source code for me along with comments. This generated source code provides a foundation that I can customize to suit my requirements. 

Next, I need to add the Send Email action into my automation to complete the flow.

Customize your app’s theme and style
Now, you can customize the look and feel of your application with App themes. With this feature, you can change the appearance of your application to Light mode or Dark mode. Additionally, you can specify custom colors for your app to match your company’s brand. To enable this feature, you need to turn on the Customize toggle.

Available today
Start building secure, intelligent, and scalable business applications with App Studio today. It’s free to build, and you’ll receive a 60-day (250 user hour) free trial.

Learn more about all these features and others in the AWS App Studio documentation and join the conversation in the #aws-app-studio channel in the AWS Developers Slack workspace.

Happy building,

— Donnie

AWS Lambda turns ten – looking back and looking ahead

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-lambda-turns-ten-the-first-decade-of-serverless-innovation/

I have a very vague memory of a 2013-era meeting with my then-colleague Tim Wagner. The term serverless did not exist, but we chatted about various ways to allow developers to focus on code instead of on infrastructure. At some I recall throwing my arms skyward and indicating that it would be cool to simply toss the code into the air and have the cloud grab, store, and run it. After many more such meetings, Tim wrote a PRFAQ proposing that we build a platform that did just that, and in 2014 I was able to announce AWS Lambda – Run Code in the Cloud.

From Startup to Enterprise
It is often the case that startups, with no installed base to worry about and the need to innovate, are the first to take a chance on something new such as Lambda. While that certainly did happen, I was pleasantly surprised to find that established companies—up to and including enterprises—were just as quick to jump in. After a bit of experimentation, they quickly found ways to build event-driven applications that supported critical internal use cases. I took this as an early indicator that Lambda would be a success. It was easy to see how quickly our customers felt a new sense of empowerment: they could move from idea to implementation, and from there to realizing business value, more quickly than ever, while still building their systems in a scalable and composable way.

Today, over 1.5 million Lambda users collectively make tens of trillion function invocations per month. These customers use Lambda for file processing, stream processing (in conjunction with Amazon Kinesis and/or Amazon MSK), web applications, IoT backends, mobile backends (often using Amazon API Gateway and AWS Amplify as well) and to support and power many other use cases.

The First Decade of Serverless Innovation
Let’s roll back the calendar and take a look at a few of the more significant Lambda launches of the past decade:

2014 – The preview launch of AWS Lambda ahead of AWS re:Invent 2014 with support for Node.js and the ability to respond to event triggers from S3 buckets, DynamoDB tables, and Kinesis streams.

2015 – General availability, use of Amazon Simple Notification Service (Amazon SNS) notifications as triggers, and support for Lambda functions written in Java.

2016 – Support for DynamoDB Streams, Support for Python, and an increase in the function duration to 5 minutes (this was later raised to 15 minutes). Access to resources in a VPC, the power to call Lambda functions from Amazon Aurora stored procedures, environment variables, and the Serverless Application Model. This year also saw the introduction of Step Functions, which gave you the power to compose multiple Lambda functions to build more complex applications.

2017 – Support for AWS X-Ray, launches of AWS SAM Local and the Serverless Application Repository.

2018 – Support for Amazon SQS as an event trigger, the power to Extend AWS CloudFormation with Lambda-powered macros, and the ability to write your Lambda functions in any programming language.

2019 – Support for provisioned concurrency to give you additional control over performance.

2020 – Access to Savings Plans to save up to 17%, the ability for Lambda functions to access a shared file system, support for AWS PrivateLink to access your functions over a private network, code signing, billing at 1 ms granularity, functions that can use up to 10 MB of memory and 6 vCPUs, and support for container images.

2021 – Amazon S3 Object Lambda to let you process data as it is being retrieved from S3, AWS Lambda Extensions, support for running Lambda functions on Graviton processors.

2022 – Support for up to 10 GB of ephemeral storage per function invocation, HTTPS endpoints for Lambda functions, and Lambda SnapStart to make function invocation faster and more predictable.

2023 – Amazon S3 Object Lambda support for CloudFront, response streaming, and 12x faster function scaling when handling an unpredictable volume of requests.

2024 -New controls to make it easier to capture and search your Lambda function logs, SnapStart support for Java functions that use the ARM64 architecture, recursive loop detection, a new console editor based on VS Code, and an enhanced local IDE experience. The last two launches were designed to improve the developer experience by making it easier to build, test, debug, and deploy Lambda functions.

Again, this is just a subset of what we have launched. If you want to find even more launches, check out the Lambda category tag and search the What’s New for Lambda.

The Next Decade of Serverless
From the start, the vision for severless has been about helping developers to move from idea to business value more quickly. With that in mind, here are a couple of trends that seem clear to me as I look at Lambda’s direction over the first decade:

Default Choice – The serverless model is definitely here to stay, and will likely become the default operating model over time.

Continued Shift Toward Composability – Over time I can see that serverless applications will continue to make increasing use of reusable, prebuilt components. Aided by AI-powered development tools, a lot of new code will focus on connecting exiting components together in new and powerful ways. This will also boost consistency and reliability across applications.

Automated, AI-Optimized Infrastructure Management– We have already seen that Lambda reduces the amount of time and effort needed for managing infrastructure. Going forward, I can see that Machine Learning and other forms of AI will help to optimize costs and performance by allocating resources optimally with minimal human intervention. Applications will run on infrastructure that is automated, self-healing, and fault tolerant.

Extensibility and Integration – As a consequence of the two previous items, applications should be able to grow and adapt to changing conditions more easily than ever.

Security – Automated infrastructure management, real-time monitoring and other forms of threat detection, and AI-assisted remediation will work together to make serverless applications even more secure.

Some Lambda Resources
If you are already using Lambda to build serverless apps, great! If you are ready to get started, here are some resources to help out:

Serverless Training – Enroll in the free Serverless Learning Plan to learn about serverless concepts, common patterns, and best practices. Read the Serverless Ramp-Up Guide, and look at our extensive (in both topic and language) selection of digital training courses and in-person classroom training:

Case Studies – Review the AWS Serverless Customer Success stories to learn how AWS customers are building and innovating with Lambda and other serverless technologies.

re:Invent 2024 Sessions -Browse the re:Invent 2024 Session Catalog to find nearly 200 sessions focused on Serverless Compute & Containers:

Podcast – Listen to Episode 137 (AWS Lambda: A Decade of Transformation) of the AWS Developers Podcast to hear Marc Brooker and Julian Wood discuss the origins, evolution, and impact of Lambda.

New Books – Take a peek at some of the newest books on serverless development and architecture:

I hope that you have enjoyed this not-so-brief look at the past, present, and future of AWS Lambda. Leave me a comment and let me know what you think!

— Jeff;

El Capitan Towers Above the Top500 in a Big HPE and AMD Win

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/el-capitan-towers-above-the-top500-in-a-big-hpe-and-amd-win/

The newest Top500 list is out, and we have the former #1 supercomputer Frontier was dethroned. In this list, the Intel-powered Aurora supercomputer passed 1EF, but then El Capitan rose to take the #1 spot. This is a big win for HPE and AMD delivering a system at over 2 exaflops of FP64 performance. El […]

The post El Capitan Towers Above the Top500 in a Big HPE and AMD Win appeared first on ServeTheHome.

AWS Weekly Roundup: AWS BuilderCards at re:Invent 2024, AWS Community Day, Amazon Bedrock, vector databases, and more (Nov 18, 2024)

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-buildercards-at-reinvent-2024-aws-community-day-amazon-bedrock-vector-databases-and-more-nov-18-2024/

This week, we wrapped up the final 2024 Latin America Amazon Web Services (AWS) Community Days of the year in Brazil, with multiple parallel events taking place. In Goiânia, we had Marcelo Palladino, senior developer advocate, and Marcelo Paiva, AWS Community Builder, as keynote speakers. Florianópolis feature Ana Cunha, senior developer advocate, and in Santiago de Chile, I had the honor to share the stage with Rossana Suarez, AWS Container Hero, as keynote speakers. These events, organized by communities for communities, provide opportunities to network, learn something new, and immerse yourself in the community. In a community, everyone grows together, and no one is left behind.

AWS Lambda celebrates its 10th anniversary, the service that introduced me to AWS and remains my favorite. Born from customer needs, it revolutionized cloud computing by allowing code execution without server management. Since its inception, documented in this LinkedIn post by Dr. Werner Vogels, Chief Technology Officer at Amazon.com, through the original PR/FAQ document, the service has grown significantly, introducing features such as 1ms billing precision and support for 10GB memory. Thank you AWS Lambda, here’s to many more anniversaries.

Amazon invests $110 million to support AI research at universities using Trainium chips. The initiative provides computing resources using AWS Trainium chips, enabling researchers to develop new AI architectures and machine learning innovations that will be open-sourced for broader advancement. Check out the Linkedin post by Matt Garman, CEO at AWS.

Last week’s launches
AWS BuilderCards second edition at re:Invent 2024 – Jeff Barr announced the launch of the second edition of AWS BuilderCards at re:Invent 2024. It includes improvements to the design and game mechanics, plus a new add-on pack on generative AI. Over 15,000 sets have been distributed at previous events, with excellent user feedback. They’ll be available for online purchase after re:Invent.

Amazon EventBridge announces up to 94% improvement in end-to-end latency for Event Buses – Amazon EventBridge has improved end-to-end latency for Event Buses by up to 94%, reducing average latency from 2235.23ms (measured in January 2023) to 129.33ms (measured in August 2024 at P99). This enhancement enables faster processing for time-sensitive applications such as fraud detection, industrial automation, and gaming across all AWS Regions where Amazon EventBridge is available, including the AWS GovCloud (US) Regions, at no additional cost to you.

Introducing resource control policies (RCPs), a new type of authorization policy in AWS Organizations – Resource control policies (RCPs), a new authorization policy in AWS Organizations. RCPs allow centralized control over maximum permissions granted to resources, complementing service control policies (SCPs) that control permissions for principals. RCPs can restrict external access to resources like Amazon Simple Storage Service (Amazon S3) buckets, enforcing a data perimeter across the organization.

Replicate changes from databases to Apache Iceberg tables using Amazon Data Firehose (in preview) – A new preview capability in Amazon Data Firehose that captures and replicates database changes to Apache Iceberg tables on Amazon S3. This feature supports PostgreSQL and MySQL databases, providing a simple solution to stream database updates without impacting performance. It automatically handles data partitioning and schema evolution, eliminating the need for complex ETL processes.

Amazon S3 now supports up to 1 million buckets per AWS account– Amazon S3 has increased its default bucket quota from 100 to 10,000 per AWS account. Customers can now request increases up to 1 million buckets. The first 2,000 buckets are free, with a small monthly fee applying thereafter for additional buckets.

Amazon Keyspaces (for Apache Cassandra) reduces prices by up to 75% – Amazon Keyspaces (for Apache Cassandra) announces significant price reductions of up to 75%. The service reduces on-demand mode pricing by up to 56% for single-region and 65% for multi-region usage. Time-to-live (TTL) delete prices are also reduced by 75%.

Centrally managing root access for customers using AWS Organizations – AWS Identity and Access Management (IAM) launches a new capability for centrally managing root access in AWS Organizations. This feature allows security teams to remove long-term root credentials from member accounts and use temporary, task-scoped root sessions for specific actions. The solution enhances security by eliminating permanent root credentials while maintaining the ability to perform necessary privileged operations.

Amazon DynamoDB reduces prices for on-demand throughput and global tables – Amazon DynamoDB announces significant price reductions, cutting on-demand mode throughput costs by 50% and global tables by up to 67%. Multi-region replicated writes now match single-region pricing. These changes make on-demand mode the recommended choice for most DynamoDB workloads.

Amazon Q Developer plugins for Datadog and Wiz now generally available – Amazon Q Developer now offers plugins for Datadog and Wiz services, allowing users to access these partners features directly through the AWS Console. Users can query information using natural language commands like @datadog or @wiz to get real-time updates and security insights.

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

Introducing Stable Diffusion 3.5 Large in Amazon SageMaker JumpStart – This powerful 8.1 billion parameter model enables high-quality, photorealistic image generation from text prompts. Customers can seamlessly deploy and use the model in Amazon SageMaker JumpStart, benefiting from Amazon SageMaker security and machine learning operations (MLOps) capabilities.

Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services – This blog post explains how we developed a Chrome extension that uses AI services to enhance live streaming experiences. The extension use Amazon Transcribe, Amazon Translate, and Amazon Bedrock to provide real-time transcription, translation, and summarization of live streams directly in the browser. It supports over 50 languages for transcription and 75 for translation, making content globally accessible.

Simplify automotive damage processing with Amazon Bedrock and vector databases –This blog post presents a solution combining Amazon Bedrock and vector databases to streamline automotive damage assessment. The system uses AI to analyze vehicle damage images, provide cost estimates, and match with similar cases from existing datasets. It use Anthropic’s Claude 3 and Amazon Titan Multimodal Embeddings, for efficient, accurate processing.

Revolutionize trip planning with Amazon Bedrock and Amazon Location Service – Amazon Bedrock and Amazon OpenSearch Service vector databases combine to automate automotive damage assessment, using AI to analyze images and match them with historical data for accurate repair estimates.

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

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 November 23 in Indonesia, and on December 14 in Kochi, India.

AWS re:Invent 2024 – Join us in Las Vegas to learn all things AWS. Our annual conference is the best—and fastest—way to grow your skills. If you can’t join us in person, you can attend virtually by registering at
Watch re:Invent online.

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

Create your AWS Builder ID and reserve your alias. Builder ID is a universal login credential that gives users access to AWS tools and resources, including over 600 free training courses, community features, and developer tools such as Amazon Q Developer beyond the AWS Management Console.

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

Thanks to Odina Jacobs for the AWS Community Chile photo.

— 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!

Replicate changes from databases to Apache Iceberg tables using Amazon Data Firehose (in preview)

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/replicate-changes-from-databases-to-apache-iceberg-tables-using-amazon-data-firehose/

Today, we’re announcing the availability, in preview, of a new capability in Amazon Data Firehose that captures changes made in databases such as PostgreSQL and MySQL and replicates the updates to Apache Iceberg tables on Amazon Simple Storage Service (Amazon S3).

Apache Iceberg is a high-performance open-source table format for performing big data analytics. Apache Iceberg brings the reliability and simplicity of SQL tables to S3 data lakes and makes it possible for open source analytics engines such as Apache Spark, Apache Flink, Trino, Apache Hive, and Apache Impala to concurrently work with the same data.

This new capability provides a simple, end-to-end solution to stream database updates without impacting transaction performance of database applications. You can set up a Data Firehose stream in minutes to deliver change data capture (CDC) updates from your database. Now, you can easily replicate data from different databases into Iceberg tables on Amazon S3 and use up-to-date data for large-scale analytics and machine learning (ML) applications.

Typical Amazon Web Services (AWS) enterprise customers use hundreds of databases for transactional applications. To perform large scale analytics and ML on the latest data, they want to capture changes made in databases, such as when records in a table are inserted, modified, or deleted, and deliver the updates to their data warehouse or Amazon S3 data lake in open source table formats such as Apache Iceberg.

To do so, many customers develop extract, transform, and load (ETL) jobs to periodically read from databases. However, ETL readers impact database transaction performance, and batch jobs can add several hours of delay before data is available for analytics. To mitigate impact on database transaction performance, customers want the ability to stream changes made in the database. This stream is referred to as a change data capture (CDC) stream.

I met multiple customers that use open source distributed systems, such as Debezium, with connectors to popular databases, an Apache Kafka Connect cluster, and Kafka Connect Sink to read the events and deliver them to the destination. The initial configuration and test of such systems involves installing and configuring multiple open source components. It might take days or weeks. After setup, engineers have to monitor and manage clusters, and validate and apply open source updates, which adds to the operational overhead.

With this new data streaming capability, Amazon Data Firehose adds the ability to acquire and continually replicate CDC streams from databases to Apache Iceberg tables on Amazon S3. You set up a Data Firehose stream by specifying the source and destination. Data Firehose captures and continually replicates an initial data snapshot and then all subsequent changes made to the selected database tables as a data stream. To acquire CDC streams, Data Firehose uses the database replication log, which reduces impact on database transaction performance. When the volume of database updates increases or decreases, Data Firehose automatically partitions the data, and persists records until they’re delivered to the destination. You don’t have to provision capacity or manage and fine-tune clusters. In addition to the data itself, Data Firehose can automatically create Apache Iceberg tables using the same schema as the database tables as part of the initial Data Firehose stream creation and automatically evolve the target schema, such as new column addition, based on source schema changes.

Since Data Firehose is a fully managed service, you don’t have to rely on open source components, apply software updates, or incur operational overhead.

The continual replication of database changes to Apache Iceberg tables in Amazon S3 using Amazon Data Firehose provides you with a simple, scalable, end-to-end managed solution to deliver CDC streams into your data lake or data warehouse, where you can run large-scale analysis and ML applications.

Let’ see how to configure a new pipeline
To show you how to create a new CDC pipeline, I setup a Data Firehose stream using the AWS Management Console. As usual, I also have the choice to use the AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or Terraform.

For this demo, I choose a MySQL database on Amazon Relational Database Service (Amazon RDS) as source. Data Firehose also works with self-managed databases on Amazon Elastic Compute Cloud (Amazon EC2). To establish connectivity between my virtual private cloud (VPC)—where the database is deployed—and the RDS API without exposing the traffic to the internet, I create an AWS PrivateLink VPC service endpoint. You can learn how to create a VPC service endpoint for RDS API by following instructions in the Amazon RDS documentation.

I also have an S3 bucket to host the Iceberg table, and I have an AWS Identity and Access Management (IAM) role setup with correct permissions. You can refer to the list of prerequisites in the Data Firehose documentation.

To get started, I open the console and navigate to the Amazon Data Firehose section. I can see the stream already created. To create a new one, I select Create Firehose stream.

Create Firehose Stream

I select a Source and Destination. In this example: a MySQL database and Apache Iceberg Tables. I also enter a Firehose stream name for my stream.

Create Firehose Stream - screen 1

I enter the fully qualified DNS name of my Database endpoint and the Database VPC endpoint service name. I verify that Enable SSL is checked and, under Secret name, I select the name of the secret in AWS Secrets Manager where the database username and password are securely stored.

Create Firehose Stream - screen 2

Next, I configure Data Firehose to capture specific data by specifying databases, tables, and columns using explicit names or regular expressions.

I must create a watermark table. A watermark, in this context, is a marker used by Data Firehose to track the progress of incremental snapshots of database tables. It helps Data Firehose identify which parts of the table have already been captured and which parts still need to be processed. I can create the watermark table manually or let Data Firehose automatically create it for me. In that case, the database credentials passed to Data Firehose must have permissions to create a table in the source database.

Create Firehose Stream - screen 3

Next, I configure the S3 bucket Region and name to use. Data Firehose can automatically create the Iceberg tables when they don’t exist yet. Similarly, it can update the Iceberg table schema when detecting a change in your database schema.

Create Firehose Stream - screen 4

As a final step, it’s important to enable Amazon CloudWatch error logging to get feedback about the stream progress and the eventual errors. You can configure a short retention period on the CloudWatch log group to reduce the cost of log storage.

After having reviewed my configuration, I select Create Firehose stream.

Create Firehose Stream - screen 5

Once the stream is created, it will start to replicate the data. I can monitor the stream’s status and check for eventual errors.

Create Firehose Stream - screen 6

Now, it’s time to test the stream.

I open a connection to the database and insert a new line in a table.

Firehose - MySQL

Then, I navigate to the S3 bucket configured as the destination and I observe that a file has been created to store the data from the table.

View parquet files on S3 bucket

I download the file and inspect its content with the parq command (you can install that command with pip install parquet-cli)

Parquet file content

Of course, downloading and inspecting Parquet files is something I do only for demos. In real life, you’re going to use AWS Glue and Amazon Athena to manage your data catalog and to run SQL queries on your data.

Things to know
Here are a few additional things to know.

This new capability supports self-managed PostgreSQL and MySQL databases on Amazon EC2 and the following databases on Amazon RDS:

The team will continue to add support for additional databases during the preview period and after general availability. They told me they are already working on supporting SQL Server, Oracle, and MongoDB databases.

Data Firehose uses AWS PrivateLink to connect to databases in your Amazon Virtual Private Cloud (Amazon VPC).

When setting up an Amazon Data Firehose delivery stream, you can either specify specific tables and columns or use wildcards to specify a class of tables and columns. When you use wildcards, if new tables and columns are added to the database after the Data Firehose stream is created and if they match the wildcard, Data Firehose will automatically create those tables and columns in the destination.

Pricing and availability
The new data streaming capability is available today in all AWS Regions except China Regions, AWS GovCloud (US) Regions, and Asia Pacific (Malaysia) Regions. We want you to evaluate this new capability and provide us with feedback. There are no charges for your usage at the beginning of the preview. At some point in the future, it will be priced based on your actual usage, for example, based on the quantity of bytes read and delivered. There are no commitments or upfront investments. Make sure to read the pricing page to get the details.

Now, go configure your first continual database replication to Apache Iceberg tables on Amazon S3 and visit http://aws.amazon.com/firehose.

— seb

Centrally managing root access for customers using AWS Organizations

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/centrally-managing-root-access-for-customers-using-aws-organizations/

AWS Identity and Access Management (IAM) is launching a new capability allowing security teams to centrally manage root access for member accounts in AWS Organizations. You can now easily manage root credentials and perform highly privileged actions.

Managing root user credentials at scale
For a long time, Amazon Web Services (AWS) accounts were provisioned with highly privileged root user credentials, which had unrestricted access to the account. This root access, while powerful, also posed significant security risks. Each AWS account’s root user had to be secured by adding layers of protection like multi-factor authentication (MFA). Security teams were required to manage and secure these root credentials manually. The process involved rotating credentials periodically, storing them securely, and making sure that the credentials complied with security policies.

As our customers expanded their AWS environments, this manual approach became cumbersome and prone to error. For example, large enterprises operating hundreds or thousands of member accounts struggled to secure root access consistently across all accounts. The manual intervention not only added operational overhead but also created a lag in account provisioning, preventing full automation and increasing security risks. Root access, if not properly secured, could lead to account takeovers and unauthorized access to sensitive resources.

Furthermore, whenever specific root actions such as unlocking an Amazon Simple Storage Service (Amazon S3) bucket policy or an Amazon Simple Queue Service (Amazon SQS) resource policy were required, security teams had to retrieve and use root credentials, which only increased the attack surface. Even with rigorous monitoring and strong security policies, maintaining long-term root credentials opened doors to potential mismanagement, compliance risks, and manual errors.

Security teams began seeking a more automated, scalable solution. They needed a way to not only centralize the management of root credentials but also programmatically manage root access without needing long-term credentials in the first place.

Centrally manage root access
With the new ability to centrally manage root access, we address the longstanding challenge of managing root credentials across multiple accounts. This new capability introduces two essential capabilities: the central management of root credentials and root sessions. Together, they offer security teams a secure, scalable, and compliant way to manage root access across AWS Organizations member accounts.

Let’s first discuss the central management of root credentials. With this capability, you can now centrally manage and secure privileged root credentials across all accounts in AWS Organizations. Root credentials management allows you to:

  • Remove long-term root credentials – Security teams can now programmatically remove root user credentials from member accounts, confirming that no long-term privileged credentials are left vulnerable to misuse.
  • Prevent credential recovery – It not only removes the credentials but also prevents their recovery, safeguarding against any unintended or unauthorized root access in the future.
  • Provision secure-by-default accounts – Because you can now create member accounts without root credentials from the start, you no longer need to apply additional security measures like MFA after account provisioning. Accounts are secure by default, which drastically reduces security risks associated with long-term root access and helps simplify the entire provisioning process.
  • Help to stay compliant – Root credentials management allows security teams to demonstrate compliance by centrally discovering and monitoring the status of root credentials across all member accounts. This automated visibility confirms that no long-term root credentials exist, making it easier to meet security policies and regulatory requirements.

But how can we make sure it remains possible to perform selected root actions on the accounts? This is the second capability we launch today: root sessions. It offers a secure alternative to maintaining long-term root access. Instead of manually accessing root credentials whenever privileged actions are required, security teams can now gain short-term, task-scoped root access to member accounts. This capability makes sure that actions such as unlocking S3 bucket policies or SQS queue policies can be performed securely without the need for long-term root credentials.

Root sessions key benefits include:

  • Task-scoped root access – AWS enables short-term root access for specific actions, adhering to the best practices of least privilege. This limits the scope of what can be done and minimizes the duration of access, reducing potential risks.
  • Centralized management – You can now perform privileged root actions from a central account without needing to log in to each member account individually. This streamlines the process and reduces the operational burden on security teams, allowing them to focus on higher-level tasks.
  • Alignment with AWS best practices – By using short-term credentials, organizations align themselves with AWS security best practices, which emphasize the principle of least privilege and the use of short-term, temporary access where possible.

This new capability does not grant full root access. It provides temporary credentials for performing one of these five specific actions. The first three actions are possible with central management of root accounts. The last two come when enabling root sessions.

  • Auditing root user credentials – Read-only access to review root user information
  • Re-enabling account recovery – Reactivating account recovery without root credentials
  • Deleting root user credentials – Removing console passwords, access keys, signing certificates, and MFA devices
  • Unlocking an S3 bucket policy – Editing or deleting an S3 bucket policy that denies all principals
  • Unlocking an SQS queue policy – Editing or deleting an Amazon SQS resource policy that denies all principals

How to obtain root credentials on a member account
In this demo, I show you how to prepare your management account, create a member account without root credentials, and obtain temporary root credentials to make one of the five authorized API call on the member account. I assume you have an organization already created.

First, I create a member account.

aws organizations create-account    \
     --email [email protected] \
     --account-name 'Root Accounts Demo account'
{
    "CreateAccountStatus": {
        "Id": "car-695abd4ee1ca4b85a34e5dcdcd1b944f",
        "AccountName": "Root Accounts Demo account",
        "State": "IN_PROGRESS",
        "RequestedTimestamp": "2024-09-04T20:04:09.960000+00:00"
    }
}

Then, I enable the two new capabilities on my management account. Don’t worry, these commands don’t alter the behavior of the accounts in any way other than enabling use of the new capability.

➜  aws organizations enable-aws-service-access \
        --service-principal iam.amazonaws.com

➜  aws iam enable-organizations-root-credentials-management
{
    "OrganizationId": "o-rlrup7z3ao",
    "EnabledFeatures": [
        "RootCredentialsManagement"
    ]
}

➜  aws iam enable-organizations-root-sessions
{
    "OrganizationId": "o-rlrup7z3ao",
    "EnabledFeatures": [
        "RootSessions",
        "RootCredentialsManagement"
    ]
}

Alternatively, I can also use the console on the management account. Under Access management, I select Account settings.

Root Access Management

Now, I’m ready to make requests to obtain temporary root credentials. I have to pass one of the five managed IAM policies to scope down the credentials to a specific action.

➜  aws sts assume-root \
       --target-principal <my member account id> \
       --task-policy-arn arn=arn:aws:iam::aws:policy/root-task/S3UnlockBucketPolicy 

{
    "Credentials": {
        "AccessKeyId": "AS....XIG",
        "SecretAccessKey": "ao...QxG",
        "SessionToken": "IQ...SS",
        "Expiration": "2024-09-23T17:44:50+00:00"
    }
}

Once I obtain the access key ID, the secret access key, and the session token, I use them as usual with the AWS Command Line Interface (AWS CLI) or an AWS SDKs.

For example, I can pass these three values as environment variables.

$ export AWS_ACCESS_KEY_ID=ASIA356SJWJITG32xxx
$ export AWS_SECRET_ACCESS_KEY=JFZzOAWWLocoq2of5Exxx
$ export AWS_SESSION_TOKEN=IQoJb3JpZ2luX2VjEMb//////////wEaCXVxxxx

Now that I received the temporary credentials, I can make a restricted API call as root on the member account. First, I verify I now have root credentials. The Arn field confirms I’m working with the root account.


# Call get Caller Identity and observe I'm root in the member account
$ aws sts get-caller-identity
{
   "UserId": "012345678901",
   "Account": "012345678901",
   "Arn": "arn:aws:iam::012345678901:root"
}

Then, I use the delete-bucket-policy from S3 to remove an incorrect policy that has been applied to a bucket. The invalid policy removed all bucket access for everybody. Removing such policy requires root credentials.

aws s3api delete-bucket-policy --bucket my_bucket_with_incorrect_policy

When there is no output, it means the operation is successful. I can now apply a correct access policy to this bucket.

Credentials are valid only for 15 minutes. I wrote a short shell script to automate the process of getting the credentials as JSON, exporting the correct environment variables, and issuing the command I want to run as root.

Availability
Central management of root access is available at no additional cost in all AWS Regions except AWS GovCloud (US) and AWS China Regions, where there is no root account. Root sessions are available everywhere.

You can start using it through the IAM console, AWS CLI or AWS SDK. For more information, visit AWS account root user in our documentation and follow best practices for securing your AWS accounts.

— seb

Introducing resource control policies (RCPs), a new type of authorization policy in AWS Organizations

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/introducing-resource-control-policies-rcps-a-new-authorization-policy/

Today, I am happy to introduce resource control policies (RCPs) – a new authorization policy managed in AWS Organizations that can be used to set the maximum available permissions on resources within your entire organization. They are a type of preventative control that help you establish a data perimeter in your AWS environment and restrict external access to resources at scale. Enforced centrally within Organizations, RCPs provide confidence to the central governance and security teams that access to resources within their AWS accounts conforms to their organization’s access control guidelines.

RCPs are available in all commercial AWS Regions and, at launch, the following services are supported: Amazon Simple Storage Service (Amazon S3), AWS Security Token Service (AWS STS), AWS Key Management Service (AWS KMS), Amazon Simple Queue Service (Amazon SQS), and AWS Secrets Manager.

There are no additional charges for enabling and using RCPs.

How are they different from service control policies (SCPs)?
While similar in nature, RCPs complement service control policies (SCPs), but they work independently.

Service control policies (SCPs) allow you to limit the permissions granted to principals within your organization such as AWS Identity and Access Management (IAM) roles. By constraining these permissions centrally within Organizations you can restrict access to AWS services, specific resources and even under what conditions principals can make requests across multiple AWS accounts.

RCPs, on the other hand, allow you to limit the permissions granted to resources in your organization. Because you implement RCPs centrally within Organizations, you can enforce consistent access controls on resources across multiple AWS accounts. For instance, you can restrict access to S3 buckets in your accounts so that they can only be accessed by principals that belong to your organization. RCPs are evaluated when your resources are being accessed irrespective of who is making the API request.

Keep in mind that neither SCPs nor RCPs grant any permissions. They only set the maximum permissions available to principals and resources in your organization. You still need to grant permissions with appropriate IAM policies, such as identity-based or resource-based policies.

Quotas for SCPs and RCPs are completely independent from each other. Each RCP can contain up to 5,120 characters. You can have up to five RCPs attached to the organization root, each OU, and account, and you can create and store up to 1000 RCPs in an organization.

How to get started
To start using RCPs you must first enable them. You can do this using the Organizations console, an AWS SDK, or by using the AWS Command Line Interface (AWS CLI). Make sure you are using the Organizations management account or a delegated administrator because those are the only accounts that can enable or disable policy types.

Make sure that you are using Organizations with the “all features” option. If you are using the “Consolidated billing features” mode, then you need to migrate to using all features before you can enable RCPs.

For console users, first head to the Organizations console. Choose Policies and you should see the option to enable Resource control policies.

enabling RCPs in the AWS Organizations console

After RCPs are enabled, you will notice in the Resource control policies page that a new policy called RCPFullAWSAccess is now available. This is an AWS managed policy that is automatically created and attached to every entity in your organization including the root, each OU, and AWS account.

the RCPFullAWSAccessPolicy can be seen on the console once RCPs are enabled

This policy allows all principals to perform any action against the organization’s resources, which means that until you start creating and attaching your own RCPs, all of your existing IAM permissions continue to operate as they did.

This is how it looks:

{
  "Version": "2012-10-17",
  "Statement": [
    { 
        "Effect": "Allow", 
        "Principal": "*", 
        "Action": "*", 
        "Resource": "*" 
    }
  ]
}

Creating an RCP

Now, we are ready to create our first RCP! Let’s look at an example.

By default, AWS resources do not permit access to external principals; resource owners must explicitly grant such access by configuring their policies. While developers have the flexibility to set resource-based policies according to their application needs, RCPs enable central IT teams to maintain control over the effective permissions across resources in their organization. This ensures that even if developers grant broad access, external identities are still restricted access in accordance with the organization’s security requirements.

Let’s create an RCP to restrict access to our S3 buckets so that only principals within our organization can access them.

On the Resources control policies page, choose Create policy which will take you to the page where you can author a new policy.

create a new resource control policy pageI am going to call this policy EnforceOrgIdentities. I recommend you enter a clear description so it is easy to understand at first glance what this policy does and to tag it appropriately.

The next section is where you can edit your policy statement. I replace the pre-populated policy template with my own:

create policy - policy syntaxHere is the full JSON policy document:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "EnforceOrgIdentities",
            "Effect": "Deny",
            "Principal": "*",
            "Action": "s3:*",
            "Resource": "*",
            "Condition": {
                "StringNotEqualsIfExists": {
                    "aws:PrincipalOrgID": "[MY ORG ID]"
                },
                "BoolIfExists": {
                    "aws:PrincipalIsAWSService": "false"
                }
            }
        }
    ]
}

Let’s break this down:

Version – This is a standard and required element of IAM policies. AWS maintains backwards compatibility, so using the latest version, currently 2012-10-17, doesn’t break existing policies but allows you to use newer features.

Statement – An array that can contain one or more statement objects. Each of these statement objects defines a single permission or set of permissions.

Sid – This is an optional field that can be helpful for policy management and troubleshooting. It needs to be unique within the scope of this JSON policy document.

Effect – As you might remember from earlier, by default we have an RCP that allows access to every AWS principal, action, and resource attached to every entity in our organization. Therefore, you should use Deny to apply restrictions.

Principal – For an RCP, this field must always be set to "*". Use the Condition field if you want this policy to apply only to specific principals.

Action – Specifies the AWS service and the actions that this policy applies to. In this case, we want to deny all interactions with Amazon S3 if they don’t meet our access control guidelines.

Resource – Specifies the resources that the RCP applies to.

Condition – A collection of conditions that will be evaluated to determine whether the policy should be applied or not for each request.

It’s important to remember that all conditions must evaluate to true for the RCP to be applied. In this case, we are applying two conditions:

1. Was the request made by an external principal?

"StringNotEqualsIfExists": 
 { 
   "aws:PrincipalOrgID": "[MY ORG ID]" 
 }

This condition first checks if the key aws:PrincipalOrgID is present in the request. If it’s not, then this condition evaluates to true without further evaluation.

If it does exist, then it compares the value to our organization ID. If the value is the same then it evaluates to false which means that the RCP will not be applied because all conditions must evaluate to true. This is the intended behaviour because we don’t want to deny access to principals within our organization.

However, if the value doesn’t match our organization ID, that means the request was made by a principal who is external to our organization. The condition evaluates to true which means that the RCP can still potentially be applied as long as the second condition also evaluates to true.

2. Is the request coming from an AWS service?

"BoolIfExists": 
   { 
     "aws:PrincipalIsAWSService": "false"
   }

This condition tests if the request contains a special key called aws:PrincipalIsAWSService which is automatically injected into the request context for all signed API requests and is set to true when it originates from an AWS service such as AWS CloudTrail writing events to your S3 bucket. If the key is not present, then this condition evaluates to true.

If it does exist, then it will compare the value to what we declare in our statement. In this case, we are testing if the value is equal to false. If it is, then we return true since that would mean that the request was not made by an AWS service and could potentially still have been made by someone outside of our organization. Otherwise, we return false.

In other words, if the request did not originate from a principal within our organization and it did not originate from an AWS service, then access to the S3 bucket is denied.

This policy is just a sample and you should tailor it to meet your unique business and security objectives. For instance, you might want to customize this policy to allow access by your business partners or to restrict access to AWS services so that they can access your resources only on your behalf. See Establishing a data perimeter on AWS: Allow only trusted identities to access company data for more details.

Attaching an RCP
The process of attaching an RCP is similar to an SCP. As previously mentioned, you can attach it to the root of your organization, to an OU, or to specific AWS accounts within your organization.

attaching a policy

After the RCP is attached, access requests to affected AWS resources must comply with the RCP restrictions. We recommend that you thoroughly test the impact that the RCP has on resources in your accounts before enforcing it at scale. You can begin by attaching RCPs to individual test accounts or test OUs.

Seeing it in action
I have now created and attached my RCP, so I’m ready to see it in practice! Let’s assume that a developer attached a resource-based policy to an S3 bucket in our organization and they explicitly gave access to identities in an external account:

bucket policy with external account id

RCPs do not prevent users from saving resource-based policies that are more permissive than the RCP allows. However, the RCP will be evaluated as part of the authorization process, as we’ve seen previously, so the request by external identities will be denied regardless.

We can prove this by trying to access the bucket with this external account, this time from the AWS CLI:


$ aws s3api get-object —bucket 123124ffeiufskdjfgbwer \
  --key sensitivefile.txt \
  --region us-east-1 local_file

An error occurred (AccessDenied) when calling the GetObject operation: Access Denied

Scaling the deployment of RCPs in your environment
So far, we have seen how we can manage RCPs using the console. However, for large-scale control management you should look into configuring them as infrastructure as code and make sure they are integrated into your existing continuous integration and continuous delivery (CI/CD) pipelines and processes.

If you use AWS Control Tower, you can deploy RCP-based controls in addition to SCP-based controls. For instance, you can use AWS Control Tower to deploy an RCP similar to that we created in the preceding example which prevents external principals from accessing S3 buckets in our organization. This ensures that RCPs are consistently applied to resources in managed accounts, streamlining and centralizing access control governance at scale.

Additionally, similar to SCPs, AWS Control Tower also supports drift detection for RCPs. If an RCP is modified or removed outside of AWS Control Tower, you will be notified of the drift and provided with steps for remediation.

Conclusion
Resource control policies (RCPs) offer centralized management over the maximum permissions available to AWS resources in your organization. Along with SCPs, RCPs help you to centrally establish a data perimeter across your AWS environment and prevent unintended access at scale. SCPs and RCPs are independent controls that allow you to achieve a distinct set of security objectives. You can choose to enable only SCPs or RCPs, or use both policy types together to establish a comprehensive security baseline as part of the defense-in-depth security model.

To learn more, see Resource control policies (RCPs) in the AWS Organizations User Guide.

Matheus Guimaraes | @codingmatheus

AWS BuilderCards second edition at re:Invent 2024

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-buildercards-second-edition-available-at-reinvent-2024-and-online/

I have been following the progress of AWS BuilderCards for several years. Players of all skill levels use the cards to learn about AWS in a fun and engaging way, competing (in a friendly fashion) to combine their cards to form architectures, gaining knowledge and scoring points as they play:

To date, more than 15,000 sets of BuilderCards have been printed and put to use over the course of three re:Invents, fifteen AWS Summits, and multiple community events. For example, here is a group of AWS enthusiasts having a good time in Tokyo during JAWS Days 2024:

Feedback from players has been incredibly positive, with a 4.8 star customer satisfaction score (CSAT) across more than 1500 replies.

Second Edition Now Available
I am happy to announce that the second edition of AWS BuilderCards will be distributed at re:Invent 2024 and will soon be available for purchase online. In response to feedback on the first edition, we have made many changes to the second edition. Here’s a summary:

Design — The revised design focuses on the contents of the card, with additional gradients and patterns to make the cards more attractive and playful. The font size was increased, game effects are now represented as icons, and the QR codes now point to the BuilderCards site:

Game Mechanics — The mechanics have been revised to improve balance, simplify play, and remove some bias. There are also some starter cards that represent a common on-premises data center environment.

Generative AI Add-On Pack — This new deck includes 50 additional BuilderCards designed to show how Generative AI and AWS architecture align. This add-on pack also includes a set of Mission cards. These cards add context, with QR codes that link to published best practices or solutions. The cards are larger, with additional text and an architecture diagram. Use is optional, with players earning extra points for completing a mission. Each deck also includes five blank BuilderCards and two blank Mission cards to support customization:

Get Your BuilderCards
If you will be at re:Invent 2024, make plans to visit the BuilderCards play area on Level 1 of Caesar’s Forum next to the PeerTalk area:

Sunday – 10 AM to 6 PM
Monday – 9 AM to 5 PM
Tuesday – 9 AM to 5 PM
Wednesday – 9 AM to 5 PM
Thursday – 9 AM to 4 PM

You can play against other re:Invent attendees and also get your own game and add-on packs to take home (we’ll give away 1,000 per day). Thanks to hard work by Masanori Yamaguchi (AWS Hero) and Kosuke Enomoto (AWS Community Builder) we will also have BuilderCards in Japanese for on-site play. You can read Kosuke’s blog post (AWS BuilderCards Japanese Edition for JAWS DAYS) to learn more about the translation process.

If you won’t be able to attend re:Invent, you will soon be able to purchase a deck of your own. Check back here for more info!

Stay Tuned
The BuilderCards team is working on additional goodies including expansion packs and additional language packs.

— Jeff;

AWS Weekly Roundup: 20 years of AWS News Blog, Express brokers for Amazon MSK, Windows Server 2025 images on EC2, and more (Nov 11, 2024)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-20-years-of-aws-news-blog-express-brokers-for-amazon-msk-windows-server-2025-images-on-ec2-and-more-nov-11-2024/

Happy 20th Anniversary of the AWS News Blog! 🎉🥳🎊 On November 9, 2004, Jeff Barr published his first blog post. At the time, he started a personal blog site using TypePad. He wanted to speak to his readers with his personal voice, not the company or team.

On April 29, 2014, we created a new AWS blog site and migrated all posts to that page. There are currently over 4,300 posts on the AWS News Blog, with Jeff contributing over 3,200 of them.

Since December 2016, the AWS News Blog has added new writers, but we are still following Jeff’s leadership principals for AWS News Bloggers in accordance with Day One. What’s unique about the AWS News Blog is that the blog writers get to use the features of the product team in advance, following the Customer Obsession leadership principle, and focus on walk-throughs of how customers can quickly use them to save time, with the Frugality principle.

I am very grateful for Jeff’s fundamental and pivotal role over the past 20 years, and I look forward to the next 20 years!

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

New Express brokers for Amazon MSK – Express brokers are a new broker type for Amazon MSK Provisioned designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent as compared to standard Apache Kafka brokers. Express brokers come preconfigured with Kafka best practices by default, support all Kafka APIs, and provide the same low-latency performance, so you can continue using existing client applications without any changes.

New Amazon Kinesis Client Library 3.0 – You can now reduce compute costs to process streaming data by up to 33 percent with Kinesis Client Library (KCL) 3.0, compared to previous KCL versions. KCL 3.0 introduces an enhanced load balancing algorithm that continuously monitors resource utilization of the stream processing workers and automatically redistributes the load from overutilized workers to other underutilized workers. To learn more, read the AWS Big Data Blog post.

Microsoft Windows Server 2025 images on Amazon EC2 – We now support Microsoft Windows Server 2025 with License Included (LI) Amazon Machine Images (AMIs), providing customers with an easy and flexible way to launch the latest version of Windows Server. By running Windows Server 2025 on Amazon EC2, customers can take advantage of the security, performance, and reliability of AWS with the latest Windows Server features. To learn more about running Windows Server 2025 on Amazon EC2, visit Windows Workloads on AWS.

Anthropic’s Claude 3.5 Haiku model in Amazon Bedrock – Claude 3.5 Haiku is the next generation of Anthropic’s fastest model, combining rapid response times with improved reasoning capabilities, making it ideal for tasks that require both speed and intelligence. Claude 3.5 Haiku improves across every skill set and surpasses even Claude 3 Opus, the largest model in Anthropic’s previous generation, on many intelligence benchmarks—including coding. To learn more, read the AWS News Blog post.

Amazon Bedrock Prompt Management GA – You can simplify the creation, testing, versioning, and sharing of prompts in Amazon Bedrock Prompt Management. At general availability, we added new features that provide enhanced options for configuring your prompts and enabling seamless integration for invoking them in your generative AI applications, such as structured prompts and Converse and InvokeModel API integration. To learn more, read the AWS Machine Learning blog post.

Six new synthetic generative voices for Amazon Polly – The generative engine is Amazon Polly’s most advanced text-to-speech (TTS) model leveraging the generative AI technology. We added six new synthetic female-sounding generative voices: Ayanda (South African English), Léa (French), Lucia (European Spanish), Lupe (American Spanish), Mía (Mexican Spanish), and Vicki (German). This extends thirteen voices and nine locales to provide you with more options of highly expressive and engaging voices.

Amazon OpenSearch Service Extended Support – We announce the end of Standard Support and Extended Support timelines for legacy Elasticsearch versions and OpenSearch Versions. Standard Support ends on Nov 7, 2025, for legacy Elasticsearch versions up to 6.7, Elasticsearch versions 7.1 through 7.8, OpenSearch versions from 1.0 through 1.2, and OpenSearch versions 2.3 through 2.9. With Extended Support, for an incremental flat fee over regular instance pricing, you continue to get critical security updates beyond the end of Standard Support. To learn more, read the AWS Big Data Blog post.

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

Other AWS news
Here are some additional news items that you might find interesting:

CEO’s visiting at AWS data center – Matt Garman, CEO of AWS, had a great time visiting one of our AWS data centers recently, and was able to get a look at the continuous innovation delivered by the team. Of course, it’s no surprise that Amazon’s senior executives visit fulfillment centers, contact centers, and data centers, to do real work for customers. AWS data centers are designed for customers in every aspect, for maximum resilience, performance, and energy efficiency.

AWS supports small businesses, creates jobs, sets up sustainability initiatives, and develops educational programs near AWS data centers. Get the latest updates – AWS in your community: Here’s what’s happening near data centers across the US on About Amazon News.

Amazon Q Business at Amazon – I introduced an Amazon story to use Code transformation in Amazon Q Developer to migrate more than old 30,000 Java applications to Java 17 version. It saved over 4,500 developer years of effort compared to previous manual jobs and saved the company $260 million in annual by moving to the latest Java version.

Here is another dogfooding story of Amazon Q Business at Amazon. Amazon built an internal chatbot with Amazon Q Business and it has resolved over 1 million internal Amazon developer questions, reducing time spent churning on manual technical investigations by more than 450,000 hours.

Our team onboarded Amazon Q Business with millions of internal documents and integrated Q Business into the tools our team use every day. Now, instead of waiting hours for responses to complex technical questions on Q&A boards or Slack channels, developers can get answers in seconds.

TOURCast at PGA TOUR – If you enjoy golf, this news will be of interest to you. The PGA TOUR debuted TOURCast in Japan at the 2024 ZOZO Championship to capture and disseminate better statistical data and bring fans closer to the game based on new scoring system called ShotLink, powered by CDW. This marks the first time the PGA TOUR has been able to bring this technology to Asia, leveraging the flexibility and scalability of AWS to overcome unique challenges.


PGA TOUR volunteer setting up GPS equipment on the fairway at ZOZO championship that will input specific shot data and feed back to Shotlink Select Plus. [IMAGE: PGA TOUR]

They’ve completely rebuilt their scoring system over the past two years on a new cloud stack. With AWS cloud, whether data comes from high-tech radar systems, cameras, or manual input, the system processes it all seamlessly.

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

AWS GenAI Lofts – AWS GenAI Lofts are about more than just the tech, they bring together startups, developers, investors, and industry experts. Whether you’re looking to gain deep insights, or get your questions answered by generative AI pros, our GenAI Lofts have you covered, and provide everything you need to start building your next innovation. Join events in São Paulo (through November 20), and Paris (through November 25).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Jakarta, Indonesia (November 23), Kochi, India (December 14).

AWS re:Invent – You can still register for the annual learning event, taking place December 2–6 in Las Vegas. Surprisingly Andy Jassy, CEO of Amazon said he will come back and participate in AWS re:Invent this year. He said “As always, the priority is to make this a learning event so customers can take nuggets back and change their own customer experiences and businesses. We’ll also have a bunch of goodies for you that we’ll announce and that we think folks will like.” Let’s meet there!

You can browse all upcoming in-person and virtual events.

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

— Channy

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

Announcing new APIs for Amazon Location Service Routes, Places, and Maps

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/announcing-new-apis-for-amazon-location-service-routes-places-and-maps/

Today, Amazon Location Service released 17 new and enhanced APIs that expand and improve capabilities for the Routes, Places, and Maps functionalities, creating a more cohesive and streamlined experience for developers. By introducing enhanced features and offering simplified migration, these updates make Amazon Location Service more accessible and useful for a wide range of applications.

You can now access advanced route optimization, toll cost calculations, GPS traces snapping, and a variety of map styles with static and dynamic rendering options, and perform proximity-based search and predictive suggestions, with rich, detailed information on points of interest.

At Amazon, the vast majority of our roadmaps are driven by customer feedback. Many customers building applications with Amazon Location Service have shared that they need purpose-built APIs and more granular details, such as contact information and business hours, when working with location-based data. Although the current API set has provided valuable tools for many customers, developers have expressed a desire for additional capabilities, such as detailed route planning, proximity-based searches, additional places details, and static map images. These new APIs address these requests and provide a more comprehensive, out-of-the-box location solution.

New and enhanced capabilities
Today’s launch introduces 10 updated APIs and seven entirely new APIs, responding directly to your feedback. Each service—Routes, Places, and Maps—receives specific enhancements designed to support a broader range of use cases.

Routes
The Amazon Location Routes API now supports advanced route planning and customization options, allowing users to:

  • CalculateIsolines to identify service areas within specific travel time, or distance
  • OptimizeWaypoints to determine the most efficient sequence of waypoints, helping to minimize either travel time or distance
  • Calculate Toll Costs to provide precise cost estimates for routes involving toll roads
  • SnapToRoads, to enable precise matching of GPS traces by snapping points to the road network

With these capabilities, you can design more accurate and dynamic route experiences for your users. For example, a logistics company could optimize driver routes in real-time, factoring in live traffic and minimizing travel costs for deliveries.

Maps
The updated Amazon Location Maps API includes more purpose-built map styles crafted by skilled cartographers. These map styles offer professional designs that accelerate time to market and eliminate the need for custom map creation. Additionally, the Static Map Image feature allows developers to integrate static maps within applications, reducing the need for continuous data streaming and improving performance in use cases where interactivity isn’t necessary.

Key features of the Maps API include:

  • GetTile, to download a tile from a tileset, with a specified X, Y, and Z axis values
  • GetStyleDescriptor, to return information about the style
  • GetStaticMap, which enables the rendering of non-interactive maps for reporting or visualization purposes

Places
The Amazon Location Places API enhancements allow more detailed search capabilities, addressing requests for increased granularity in location data. The new capabilities include:

  • SearchNearby and Autocomplete, which support proximity-based queries and enable predictive text features for better user experiences
  • Enhanced business details with categories such as Business Hours, Contact Information, and additional attributes for points of interest

These features are especially useful for applications where users need detailed information about nearby locations, such as food delivery services or retail applications. Imagine that a customer opens a food delivery application, searches for nearby restaurants using SearchNearby, and retrieves restaurant details such as business hours and contact information to confirm availability. Once multiple delivery orders are assigned to a driver, the application uses OptimizeWaypoints to suggest the most efficient route for pickups and deliveries. As the driver follows the route, SnaptoRoads provides precise visualization of their location, enhancing the customer’s real-time tracking experience.

Enhanced Location Service in action
Calling the API is straightforward. You can use the AWS Command Line Interface (AWS CLI), one of our AWS SDKs, or the plain REST API. However, displaying the information on a map in a web or mobile app requires some additional setup. Although the process is well documented, it’s too detailed to cover fully here. In this demo, I’ll focus on using the API.

Amazon Location Service allows API calls to be authenticated in two ways: through AWS API authentication (AWS Sigv4 authentication) or through API keys. API keys can be more convenient for developers of mobile applications where the end user is not authenticated or when integrating with Amazon Cognito is not feasible. This is the recommended authentication method for front-end applications.

To demonstrate the versatility of the APIs and how easily you can integrate inside your applications, I use a combination of the AWS CLI, cURL, and a graphical REST API client for each step of the demo.

Step 1: Create an API key

First, I create an API key for my application using the AWS CLI. You can also manage API keys in the AWS Management Console.

REGION=eu-central-1
KEYNAME=geo-key-seb

aws location create-key --region ${REGION} --key-name ${KEYNAME} --restrictions \
AllowActions="geo-routes:*","geo-places:*","geo-maps:*",\
AllowResources="arn:aws:geo-routes:${REGION}::provider/default",\
"arn:aws:geo-places:${REGION}::provider/default",\
"arn:aws:geo-maps:${REGION}::provider/default" \
--no-expiry 

{
    "Key": "v1.public.ey...cy",
    "KeyArn": "arn:aws:geo:eu-central-1:02345678901:api-key/geo-key-seb",
    "KeyName": "geo-key-seb",
    "CreateTime": "2024-09-29T09:35:53.115000+00:00"
}

This command generates the API key, which I can now use to call Amazon Location APIs.

Step 2: Get geographic coordinates

Next, I use cURL to retrieve the geographic coordinates (a longitude and latitude) for the city center of Lille, France, by calling GeoCode and passing an address in the QueryText parameter.

$ curl --silent -X "POST" "https://places.geo.eu-central-1.amazonaws.com/v2/geocode?key=v1.public.ey...cy" \
       -d $'{ "QueryText": "Grand Place, Lille, France" }' 
 
{"ResultItems":[{"PlaceId":"AQ...5U","PlaceType":"Street","Title":"Grand'Place, 59800 Lille, France",
                 "Address":{"Label":"Grand'Place, 59800 Lille, France",
                 "Country":{"Code2":"FR","Code3":"FRA","Name":"France"},
                 "Region":{"Code":"HDF","Name":"Hauts-de-France"},"SubRegion":{"Name":"Nord"},
                 "Locality":"Lille","District":"Centre","PostalCode":"59800",
                 "Street":"Grand'Place","StreetComponents":[{"BaseName":"Grand'Place","Language":"fr"}]},
                 "Position":[3.06361,50.63706],
                 "MapView":[3.0628,50.6367,3.06413,50.63729],
                 "MatchScores":{"Overall":1,"Components":{"Address":{"Country":1,"Locality":1,"Intersection":[1]}}}}]}

This returns several data points, including the GPS coordinates for the city center: [3.06361, 50.63706].

Step 3: Search for nearby places

Using the coordinates retrieved, I use a REST API client tool to call the SearchNearby API to find places of interest around Lille’s city center.

Amazon Location Service - API call for places of interest

On the right side of the screen, I can read the API response: a list of nearby places, such as restaurants, banks, and parking areas. I can further refine the search by specifying categories or restricting the search area.

The SearchNearby API accepts an optional Filter parameter that helps you restrict the search within a bounding box or to include or exclude business chains, categories, countries, or food types.

"Filter": {
   "BoundingBox": [ number ],
   "ExcludeBusinessChains": [ "string" ],
   "ExcludeCategories": [ "string" ],
   "ExcludeFoodTypes": [ "string" ],
   "IncludeBusinessChains": [ "string" ],
   "IncludeCategories": [ "string" ],
   "IncludeCountries": [ "string" ],
   "IncludeFoodTypes": [ "string" ]
},

In my search for nearby points of interest, one of the results returned was a McDonald’s, a well-known international reference 🍔.

Amazon Location Service - SearchNearby result

Step 4: Get driving directions

Finally, I use the AWS CLI to calculate driving directions between two city centers: Brussels, Belgium, and Lille, France.

aws location calculate-routes    \
    --origin 4.35278 50.84687      \
    --destination 3.06361 50.63706 \
    --key "v1.public.ey...cy"

The response includes a polyline for rendering the path on a map and a step-by-step list of driving directions.

...
          "TravelMode": "Car",
          "Type": "Vehicle",
          "VehicleLegDetails": {
            "TravelSteps": [
              {
                "Duration": 15,
                "Distance": 75,
                "ExitNumber": [],
                "GeometryOffset": 0,
                "Type": "Depart"
              },
              {
                "Duration": 10,
                "Distance": 8,
                "ExitNumber": [],
                "GeometryOffset": 2,
                "Type": "Turn",
                "TurnStepDetails": {
                  "Intersection": [],
                  "SteeringDirection": "Right",
                  "TurnIntensity": "Typical"
                }
              },
...

Step 5: Displaying the driving directions on a map

To visualize the route on a map, I use the MapLibre library, which is a rendering engine for displaying maps in web and mobile applications. Following the Amazon Location Service Developer Guide, I built a basic app to display the route.

Amazon Location Service - Map with route

In addition to MapLibre, you can use AWS Amplify to integrate and display Amazon Location data in your applications.

Getting started
With these new and updated APIs, Amazon Location Service offers a more comprehensive suite of mapping and location data for your business needs. These will help to accelerate your development lifecyle by increasing developers’ agility and scalability.

To get started, explore the updated Amazon Location Service Developer Guide and begin integrating these features today. You can also visit the Amazon Location Service page to learn more or try out the APIs with your favorite AWS SDKs to see how they can enhance your applications.

— seb

Introducing Express brokers for Amazon MSK to deliver high throughput and faster scaling for your Kafka clusters

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-express-brokers-for-amazon-msk-to-deliver-high-throughput-and-faster-scaling-for-your-kafka-clusters/

Today, we’re announcing the general availability of Express brokers, a new broker type for Amazon Managed Streaming for Apache Kafka (Amazon MSK). It’s designed to deliver up to three times more throughput per-broker, scale up to 20 times faster, and reduce recovery time by 90 percent as compared to Standard brokers running Apache Kafka. Express brokers come preconfigured with Kafka best practices by default, support Kafka APIs, and provide the same low latency performance that Amazon MSK customers expect, so they can continue using existing client applications without any changes.

Express brokers provide improved compute and storage elasticity for Kafka applications when using Amazon MSK provisioned clusters. Amazon MSK is a fully-managed AWS service that makes it easier for you to build and run highly available and scalable applications based on Apache Kafka.

Let’s dive deeper into some of the key features that Express brokers have and the benefits they provide:

  • Easier operations with hands-free storage management – Express brokers offer unlimited storage without preprovisioning, eliminating disk-related bottlenecks. Cluster sizing is simpler, requiring only ingress and egress throughput divided by recommended per-broker throughput. This removes the need for proactive disk capacity monitoring and scaling, simplifying cluster management and improving resilience by eliminating a potential failure source.
  • Fewer brokers with up to three times throughput per broker – Higher throughput per broker allows for smaller clusters for the same workload. Standard brokers’ throughput must account for client traffic and background operations, with m7g.16xl Standard brokers safely handling 154 MBps ingress. Express brokers use opinionated settings and resource isolation, enabling m7g.16xl size instances to safely manage up to 500 MBps ingress without compromising performance or availability during cluster events.
  • Higher utilization with 20 times faster scaling – Express brokers reduce data movement during scaling, making them up to 20 times faster than Standard brokers. This allows for more quicker and reliable cluster resizing. You can monitor each broker’s ingress throughput capacity and add brokers within minutes, eliminating the need for over-provisioning in anticipation of traffic spikes.
  • Higher resilience with 90 percent faster recovery – Express brokers are designed for mission-critical applications requiring high resilience. They come preconfigured with best-practice defaults, including 3-way replication (RF=3), which reduce failures due to misconfiguration. Express brokers also recover 90 percent faster from transient failures compared to standard Apache Kafka brokers. Express brokers’ rebalancing and recovery use minimal cluster resources, simplifying capacity planning. This eliminates the risk of increased resource utilization and the need for continuous monitoring when right-sizing clusters.

You have choice options in Amazon MSK depending on your workload and preference:

MSK provisioned MSK Serverless
Standard brokers Express brokers
Configuration range Most flexible Flexible Least flexible
Cluster rebalancing Customer managed Customer managed
but up to 20x faster
MSK managed
Capacity management Yes Yes (compute only) No
Storage management Yes No No

Express brokers lower costs, provide higher resiliency, and lower operational overhead, making them the best choice for all Kafka workloads. If you prefer to use Kafka without managing any aspect of its capacity, its configuration, or how it scales, then you can choose Amazon MSK Serverless. This provides a fully abstracted Apache Kafka experience that eliminates the need for any infrastructure management, scales automatically, and charges you on a pay-per-use consumption model that doesn’t require you to optimize resource utilization.

Getting started with Express brokers in Amazon MSK
To get started with Express brokers, you can use the Sizing and Pricing worksheet that Amazon MSK provides. This worksheet helps you estimate the cluster size you’ll need to accommodate your workload and also gives you a rough estimate of the total monthly cost you’ll incur.

The throughput requirements of your workload are the primary factor in the size of your cluster. You should also consider other factors, such as partition and connection count to arrive at the size and number of brokers you’ll need for your cluster. For example, if your streaming application needs 30 MBps of data ingress (write) and 80 MBps data egress (read) capacity, you can use three express.m7g.large brokers to meet your throughput needs (assuming the partition count for your workload is within the maximum number of partitions that Amazon MSK recommends for an m7g.large instance).

The following table shows the recommended maximum ingress, egress, and partition counts per instance size for sustainable and safe operations. You can learn more about these recommendations in the Best practices section of Amazon MSK Developer Guide.

Instance size Ingress (MBps) Egress (MBps)
express.m7g.large 15.6 31.2
express.m7g.4xlarge 124.9 249.8
express.m7g.16xlarge 500.0 1000.0

Once you have decided the number and size of Express brokers you’ll need for your workload, go to the AWS Management Console or use the CreateCluster API to create an Amazon MSK provisioned cluster.

When you create a new cluster on the Amazon MSK console, in the Broker type option, choose Express brokers and then select the mount of compute capacity that you want to provision for the broker. As you can see in the screen shot, you can use Apache Kafka 3.6.0 version and Graviton-based instances for Express brokers. You don’t need to preprovision storage for Express brokers.

You can also customize some of these configurations to further fine-tune the performance of your clusters according to your own preferences. To learn more, visit Express broker configurations in the Amazon MSK developer guide.

To create an MSK cluster in the AWS Command Line Interface (AWS CLI), use the create-cluster command.

aws kafka create-cluster \
    --cluster-name "channy-express-cluster" \
    --kafka-version "3.6.0" \
    --number-of-broker-nodes 3 \
    --broker-node-group-info file://brokernodegroupinfo.json

A JSON file named brokernodegroupinfo.json specifies the three subnets over which you want Amazon MSK to distribute the broker nodes.

{
    "InstanceType": "express.m7g.large",
    "BrokerAZDistribution": "DEFAULT",
    "ClientSubnets": [
        "subnet-0123456789111abcd",
        "subnet-0123456789222abcd",
        "subnet-0123456789333abcd"
    ]
}

Once the cluster is created, you can use the bootstrap connection string to connect your clients to the cluster endpoints.

With Express brokers, you can scale vertically (changing instance size) or horizontally (adding brokers). Vertical scaling doubles throughput without requiring partition reassignment. Horizontal scaling adds brokers in sets of three and and allows you to create more partitions, but it requires partition reassignment for new brokers to serve traffic.

A major benefit of Express brokers is that you can add brokers and rebalance partitions within minutes. On the other hand, rebalancing partitions after adding Standard brokers can take several hours. The graph below shows the time it took to rebalance partitions after adding 3 Express brokers to a cluster and reassigning 2000 partitions to each of the new brokers.

As you can see, it took approximately 10 minutes to reassign these partitions and utilize the additional capacity of the new brokers. When we ran the same experiment on an equivalent cluster comprising of Standard brokers, partition reassignment took over 24hours.

To learn more about the partition reassignment, visit Expanding your cluster in the Apache Kafka documentation.

Things to know
Here are some things you should know about Express brokers:

  • Data migration – You can migrate the data in your existing Kafka or MSK cluster to a cluster composed of Express brokers using Amazon MSK Replicator, which copies both the data and the metadata of your cluster to a new cluster.
  • Monitoring – You can monitor your cluster composed of Express brokers in the cluster and at the broker level with Amazon CloudWatch metrics and enable open monitoring with Prometheus to expose metrics using the JMX Exporter and the Node Exporter.
  • Security – Just like with other broker types, Amazon MSK integrates with AWS Key Management Service (AWS KMS) to offer transparent server-side encryption for the storage in Express brokers. When you create an MSK cluster with Express brokers, you can specify the AWS KMS key that you want Amazon MSK to use to encrypt your data at rest. If you don’t specify a KMS key, Amazon MSK creates an AWS managed key for you and uses it on your behalf.

Now available
The Express broker type is available today in the US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland), and Europe (Stockholm) Regions.

You pay an hourly rate for Apache Kafka broker instance usage (billed at one-second resolution) for Express brokers, with varying fees depending on the size of the broker instance and active brokers in your MSK clusters. You also pay a per-GB rate for data written to an Express broker (billed at per-byte resolution). To learn more, visit the Amazon MSK pricing page.

Give Express brokers for Amazon MSK a try in the Amazon MSK console. For more information, visit the Amazon MSK Developer Guide and send feedback to AWS re:Post for Amazon MSK or through your usual AWS support contacts.

— Channy

Introducing the last cohort of AWS Heroes this year – November 2024

Post Syndicated from Taylor Jacobsen original https://aws.amazon.com/blogs/aws/introducing-the-last-cohort-of-aws-heroes-this-year-november-2024/

As we gear up ahead of AWS re:Invent 2024, we’re thrilled to announce our final cohort of new AWS Heroes! These individuals exemplify expertise and dedication to leveraging AWS technologies and sharing knowledge. Their contributions to the AWS community are greatly appreciated, and today we’re excited to celebrate them.

Ayyanar Jeyakrishnan – Bengaluru, India

Machine Learning Hero Ayyanar Jeyakrishnan is a Principal Engineer/Executive Director at Wells Fargo. He is a seasoned machine learning and cloud enthusiast with a strong focus on AWS technologies. Ayyanar’s expertise includes creating data platforms and architecting DevOps and MLOps solutions to streamline the deployment and management of machine learning models on AWS. He is passionate about sharing his knowledge and frequently speaks at industry events and community meetups on MLOps, generative AI, and machine learning applications.

Dzenana Dzevlan – Sarajevo, Bosnia and Herzegovina

Community Hero Dzenana Dzevlan is a co-founder and Technical Manager at allOps Solutions, an APN Partner company. She is an expert practice lecturer at International Burch University, and actively shares her knowledge in DevOps, generative AI, and other emerging technologies. Dzenana’s passion for technology extends beyond the classroom, as she actively mentors students in cloud and AI solutions. A dedicated advocate for diversity in tech, she champions women’s inclusion and empowerment in the industry. Through speaking engagements and mentorship programs, Dzenana inspires and equips the next generation of IT and cloud professionals.

Kenneth Attard – Valletta, Malta

Community Hero Kenneth Attard is an Enterprise Architect at Betsson Group in Malta with over 20 years of technical experience, including the past eight years specializing in AWS Cloud networking, security, and governance. As the leader of the AWS Malta User Group and organizer of the Malta AWS Community Day, Kenneth is passionate about fostering knowledge and learning among cloud enthusiasts and professionals. He is also a frequent speaker at both local and international events, including AWS Cloud Days, AWS Summits, and AWS Community Days across multiple countries.

Marcin Sodkiewicz – Wrocław, Poland

Serverless Hero Marcin Sodkiewicz is a Principal Software Engineer at Ryanair. He has been there since 2016 and was part of a giant technological leap, from the data center into the cloud and teams building with a serverless-first mindset. Over those years, he has seen and learned a lot—especially the difference the cloud makes in quickly delivering high-quality, scalable, reliable, and profitable software. The profitability aspect is super important to him, as he works at a low-cost airline with a mission of “making travel affordable for everyone,” which matches his interest in building cost-optimized solutions that bring competitive advantages. Marcin blogs and talks about AWS through the lens of his favorite topics: event-driven and serverless architectures, resiliency, cost optimization, and observability. Additionally, he is one of the organizers of the AWS User Group in his city – Wrocław.

Stephen Sennett – Melbourne, Australia

Community Hero Stephen Sennett is a Senior Consultant at Kinetic IT in Australia. As an experienced cloud technologist, he has worked with AWS for over a decade as an architect, consultant, engineer, and educator. Stephen was a member of the AWS Community Builders program between 2021 and 2024, acting as a mentor to others in the AWS community, and serving as a public speaking coach for upcoming thought leaders through the AWS New Voices program. He is an experienced keynote speaker, delivering sessions at AWS Community Days, AWS Summits, and other tech conferences around the world. Outside of his professional role, Stephen is an active volunteer emergency management officer, and non-profit board director.

Vadym Kazulkin – Bonn, Germany

Serverless Hero Vadym Kazulkin is Head of Development at ip.labs GmbH (a Fujifilm subsidiary), and brings over two decades of Java ecosystem expertise. His current focus is designing and implementing highly scalable AWS Cloud applications, with a passion for serverless architecture. As co-organizer of the Java User Group Bonn meetup, Vadym actively shares his knowledge at local and international events, including AWS and Java meetups, conferences, AWS Community Days, and ServerlessDays. He values community engagement, both for sharing insights and continuous learning in cloud and serverless technologies.

Learn More

Visit the AWS Heroes website if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you.

— Taylor

AWS Weekly Roundup: AWS Lambda, Amazon Bedrock, Amazon Redshift, Amazon CloudWatch, and more (Nov 4, 2024)

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-lambda-amazon-bedrock-amazon-redshift-amazon-cloudwatch-and-more-nov-4-2024/

The spooky season has come and gone now. While there aren’t any Halloween-themed releases, AWS has celebrated it in big style by having a plethora of exciting releases last week! I think it’s safe to say that we have truly entered the ‘pre’ re:Invent stage as more and more interesting things are being released every week on the countdown to AWS re:Invent 2024.

There is a lot to cover, so let me put my wizard hat on, open the big bag of treats, and dive into last week’s goodies!

Something for developers
There was no shortage of treats from AWS for developers this Halloween!

AWS enhances the Lambda application building experience with VS Code IDE and AWS Toolkit — AWS has enhanced AWS Lambda development with the AWS Toolkit for Visual Studio Code, providing a guided setup for coding, testing, and deploying Lambda applications directly within the IDE. It includes sample walkthroughs and one-click deployment, simplifying the development process. Now, building apps with Lambda is as intuitive as crafting a spell in a wizard’s workshop!

AWS Amplify integration with Amazon S3 for static website hosting — AWS Amplify Hosting now integrates with Amazon S3 for seamless static website hosting, with global CDN support via Amazon CloudFront. This simplifies set up, offering secure, high-performance delivery with custom domains and SSL certificates. Hosting your site is now easier than spotting a jack-o’-lantern on Halloween night!

AWS Lambda now supports AWS Fault Injection Service (FIS) actions — AWS Lambda now supports AWS Fault Injection Simulator (FIS) actions, enabling developers to test resilience by injecting controlled faults like latency and execution errors. This helps simulate real-world failures without code changes, improving monitoring and operational readiness. Great for testing that old candy dispenser!

AWS CodeBuild now supports retrying builds automatically — AWS CodeBuild now offers automatic build retries, allowing developers to set a retry limit for failed builds. This reduces manual intervention by automatically retrying builds up to the specified limit, tackling those pesky, intermittent failures like a ghostbuster clearing a haunted pipeline!

Amazon Virtual Private Cloud launches new security group sharing features — Amazon VPC now supports sharing security groups across multiple VPCs within the same account and with participant accounts in shared VPCs. This streamlines security management and ensures consistent traffic filtering across your organization. Now, keeping your network secure is as seamless as warding off digital goblins!

Amazon DataZone expands data access with tools like Tableau, Power BI, and more — Amazon DataZone now supports the Amazon Athena JDBC Driver, allowing seamless access to data lake assets from BI tools, like Tableau and Power BI. This lets analysts connect and analyze data with ease. Now, accessing data is as effortless as a witch flying on her broomstick!

Generative AI
Amazon Q and Amazon Bedrock continue to make generative AI seem like magic. Here are some releases from last week.

Amazon Q Developer inline chat — Amazon Q Developer has introduced inline chat support, allowing developers to engage directly within their code editor for actions like optimization, commenting, and test generation. Real-time inline diffs make it simple to review changes, available in Visual Studio Code and JetBrains IDEs. It’s practically code magic – no witch’s cauldron needed!

Meta’s Llama 3.1 8B and 70B models are now available for fine-tuning in Amazon Bedrock — Amazon Bedrock now supports fine-tuning for Meta’s Llama 3.1 8B and 70B models, allowing developers to customize these AI models with their own data. With a 128K context length, Llama 3.1 processes large text volumes efficiently, making it perfect for domain-specific applications. Now, your AI won’t be scared of handling monstrous amounts of data—even on a dark, stormy night!

Fine-tuning for Anthropic’s Claude 3 Haiku in Amazon Bedrock is now generally available — Fine-tuning for the Claude 3 Haiku model in Amazon Bedrock is now generally available, enabling customization with your data for better accuracy. Make your AI as unique as your Halloween costume!

Cost Planning, Saving, and Tracking
Here are some new releases that can help you stay on top of your budget and keep an eye on the amount of candy that you buy.

AWS now accepts partial card payments — AWS now supports partial payments with credit or debit cards, letting users split monthly bills across multiple cards. This flexibility makes managing your budget as smooth as a ghost gliding through a haunted house!

Amazon Bedrock now supports cost allocation tags on inference profiles — Amazon Bedrock now supports cost allocation tags for inference profiles, allowing customers to track and manage generative AI costs by department or application. This makes financial management a treat, not a trick!

AWS Deadline Cloud now adds budget-related events — AWS Deadline Cloud, a service used for rendering and managing visual effects and animation workloads, now sends budget-related events via Amazon EventBridge, enabling real-time spending updates and automated notifications. This helps keep project costs under control without any unexpected scares!

And the busiest team of the week award goes to…Amazon Redshift!
Clearly, the Amazon Redshift team loves Halloween and decided to celebrate in big style with many releases! Here are the highlights:

Amazon Redshift integration with Amazon Bedrock for generative AI — Amazon Redshift now integrates with Amazon Bedrock for generative AI tasks using SQL, adding AI capabilities like text generation directly in your data warehouse. Now, you can conjure up rich insights without the need for complicated spells!

Announcing general availability of auto-copy for Amazon Redshift — Auto-copy for continuous data ingestion from Amazon S3 into Amazon Redshift is now generally available. This streamlines workflows, making data integration as smooth as carving a soft pumpkin!

Amazon Redshift now supports incremental refresh on Materialized Views (MVs) for data lake tables — Amazon Redshift now supports incremental refresh for materialized views on data lake tables, updating only changed data to boost efficiency. This keeps your data fresh without any haunting overhead!

Announcing Amazon Redshift Serverless with AI-driven scaling and optimization — Amazon Redshift Serverless now offers AI-driven scaling, adjusting resources automatically based on workload. This ensures smooth performance without any chilling surprises!

CSV result format support for Amazon Redshift Data API — Amazon Redshift Data API now supports CSV output for SQL query results, enhancing data processing flexibility. This makes handling data as smooth as a ghost’s whisper!

Halloween week contest runner-up…Amazon CloudWatch!
The Amazon CloudWatch team has also been busy giving out candy this Halloween! Let’s check it out.

Amazon CloudWatch now monitors EBS volumes exceeding provisioned performance — Amazon CloudWatch now provides metrics to check if Amazon EBS volumes exceed their IOPS or throughput limits. This helps quickly spot and resolve performance issues before they turn into a haunting problem!

New Amazon CloudWatch metrics for monitoring I/O latency of Amazon EBS volumes — Amazon CloudWatch now offers metrics for average read and write I/O latency of Amazon EBS volumes, aiding in identifying performance issues. These insights are available per minute at no extra cost. This should help you prevent latency from sneaking up on you like a Halloween ghost!

Amazon ElastiCache for Valkey adds new CloudWatch metrics to monitor server-side response time — Amazon ElastiCache now includes metrics for read and write request latency, helping monitor server response times. This aids in quickly spotting and resolving performance issues before they become a frightful surprise!

Conclusion
And that’s a wrap on Halloween 2024. I don’t know about you, but this is my favorite time of the year, followed by News Year’s. Both carry an element of unpredictability that I very much enjoy. With Halloween, you can get excited about what costume you’re going to wear, whereas New Year’s is all about new possibilities and conquering new horizons.

Luckily, you don’t have to wait for the new year to unlock new frontiers with AWS as we bring excitement and innovation throughout the year. And what better way to see this in action than at AWS re:Invent 2024!

I wonder what kinds of spells and surprises we’ll be conjuring up come Halloween 2025. Until next time, keep your eyes on the horizon—and your broomsticks at the ready!

Fine-tuning for Anthropic’s Claude 3 Haiku model in Amazon Bedrock is now generally available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/fine-tuning-for-anthropics-claude-3-haiku-model-in-amazon-bedrock-is-now-generally-available/

Today, we are announcing the general availability of fine-tuning for Anthropic’s Claude 3 Haiku model in Amazon Bedrock in the US West (Oregon) AWS Region. Amazon Bedrock is the only fully managed service that provides you with the ability to fine-tune Claude models. You can now fine-tune and customize the Claude 3 Haiku model with your own task-specific training dataset to boost model accuracy, quality, and consistency to further tailor generative AI for your business.

Fine-tuning is a technique where a pre-trained large language model (LLM) is customized for a specific task by updating the weights and tuning hyperparameters like learning rate and batch size for optimal results.

Anthropic’s Claude 3 Haiku model is the fastest and most compact model in the Claude 3 model family. Fine-tuning Claude 3 Haiku offers significant advantages for businesses:

  • Customization – You can customize models that excel in areas crucial to your business compared to more general models by encoding company and domain knowledge.
  • Specialized performance – You can generate higher quality results and create unique user experiences that reflect your company’s proprietary information, brand, products, and more.
  • Task-specific optimization – You can enhance performance for domain-specific actions such as classification, interactions with custom APIs, or industry-specific data interpretation.
  • Data security – You can fine-tune with peace of mind in your secure AWS environment. Amazon Bedrock makes a separate copy of the base foundation model that is accessible only by you and trains this private copy of the model.

You can now optimize performance for specific business use cases by providing domain-specific labeled data to fine-tune the Claude 3 Haiku model in Amazon Bedrock.

In early 2024, we started to engage customers with a team of experts from the AWS Generative AI Innovation Center to help fine-tune Anthropic’s Claude models with their proprietary data sources. I’m happy to share that you can now fine-tune Anthropic’s Claude 3 Haiku model in Amazon Bedrock directly in the Amazon Bedrock console.

Get started with fine-tuning for Anthropic’s Claude 3 Haiku model in Amazon Bedrock
I will demonstrate how to easily fine-tune the Claude 3 Haiku model in Amazon Bedrock. To learn more about the fine-tuning workflow in detail, visit the AWS Machine Learning Blog post, Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality.

To create a simple fine-tuning job in the Amazon Bedrock console, go to the Foundation models section in the navigation pane and select Custom models. In the Models section, select the Customize model button and then select Create Fine-tuning job.

Next, choose the model that you want to customize with your own data, give your resulting model a name, and optionally add encryption keys and any tags to associate with the model in the Model details section. Enter a name for the job and optionally add any tags for the job in the Job configuration section.

You can select the Amazon S3 location of the training dataset file and, if applicable, the validation dataset file in the Input data section.

Each dataset can be created using a JSON Lines (JSONL) file with multiple JSON lines using the single-turn or multi-turn messaging format. Each JSON line is a sample containing a system and message, an array of message objects.

I prepared some of the latest information about Anthropic’s Claude 3.5 Sonnet model in Amazon Bedrock. To learn more, visit Prepare the datasets in the Amazon Bedrock documentation.

{"system": "You are an helpful assistant.","messages":[{"role": "user", "content": "What’s the latest models to support Amazon Bedrock"},{"role": "assistant", "content": "Amazon Bedrock is a fully managed service that support Anthropic’s Claude 3 Sonnet, Haiku, and Opus models and the latest Claud 3.5 Sonnet model."}]}
{"system": "You are an helpful assistant.","messages":[{"role": "user", "content": "How long context window of Claude 3.5?"},{"role": "assistant", "content": "Anthropic's Claude 3 models have a 200,000 token context window enabling you to relay a large volume of information to Claude. This translates to roughly 150,000 words, or over 500 pages of material."}]}
{"system": "You are an helpful assistant.","messages":[{"role": "user", "content": "Is Claude 3.5 available in Bedrock?"},{"role": "assistant", "content": "Anthropic’s most intelligent and advanced model, Claude 3.5 Sonnet, demonstrates exceptional capabilities across a diverse range of tasks and evaluations while also outperforming Claude 3 Opus."}]}

In the Hyperparameters section, enter values for hyperparameters to use in training, such as epochs, batch size, and learning rate multiplier. If you’ve included a validation dataset, you can enable Early stopping, a technique used to prevent overfitting and stop the training process when the validation loss stops improving. You can set an early stopping threshold and patience value.

You can also select the output location where Amazon Bedrock should save the output of the job in the Output data section. Choose an AWS Identity and Access Management (IAM) custom service role with the appropriate permissions in the Service access section. To learn more, see Create a service role for model customization in the Amazon Bedrock documentation.

Finally, choose Create Fine-tuning job and wait for your fine-tuning job to start.

You can track its progress or stop it in the Jobs tab in the Custom models section.

After a model customization job is complete, you can analyze the results of the training process by looking at the files in the output Amazon Simple Storage Service (Amazon S3) folder that you specified when you submitted the job, or you can view details about the model.

Before using a customized model, you need to purchase Provisioned Throughput for Amazon Bedrock and then use the resulting provisioned model for inference. When you purchase Provisioned Throughput, you can select a commitment term, choose a number of model units, and see estimated hourly, daily, and monthly costs. To learn more about the custom model pricing for the Claude 3 Haiku model, visit Amazon Bedrock Pricing.

Now, you can test your custom model in the console playground. I choose my custom model and ask whether Anthropic’s Claude 3.5 Sonnet model is available in Amazon Bedrock.

I receive the answer:

Yes. You can use Anthropic’s most intelligent and advanced model, Claude 3.5 Sonnet in the Amazon Bedrock. You can demonstrate exceptional capabilities across a diverse range of tasks and evaluations while also outperforming Claude 3 Opus.

You can complete this job using AWS APIs, AWS SDKs, or AWS Command Line Interface (AWS CLI). To learn more about using AWS CLI, visit Code samples for model customization in the AWS documentation.

If you are using Jupyter Notebook, visit the GitHub repository and follow a hands-on guide for custom models. To build a production-level operation, I recommend reading Streamline custom model creation and deployment for Amazon Bedrock with Provisioned Throughput using Terraform on the AWS Machine Learning Blog.

Datasets and parameters
When fine-tuning Claude 3 Haiku, the first thing you should do is look at your datasets. There are two datasets that are involved in training Haiku, and that’s the Training dataset and the Validation dataset. There are specific parameters that you must follow in order to make your training successful, which are outlined in the following table.

Training data Validation data
File format JSONL
File size <= 10GB <= 1GB
Line count 32 – 10,000 lines 32 – 1,000 lines
Training + Validation Sum <= 10,000 lines
Token limit < 32,000 tokens per entry
Reserved keywords Avoid having “\nHuman:” or “\nAssistant:” in prompts

When you prepare the datasets, start with a small high-quality dataset and iterate based on tuning results. You can consider using larger models from Anthropic like Claude 3 Opus or Claude 3.5 Sonnet to help refine and improve your training data. You can also use them to generate training data for fine-tuning the Claude 3 Haiku model, which can be very effective if the larger models already perform well on your target task.

For more guidance on selecting the proper hyperparameters and preparing the datasets, read the AWS Machine Learning Blog post, Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku in Amazon Bedrock.

Demo video
Check out this deep dive demo video for a step-by-step walkthrough that will help you get started with fine-tuning Anthropic’s Claude 3 Haiku model in Amazon Bedrock.

Now available
Fine-tuning for Anthropic’s Claude 3 Haiku model in Amazon Bedrock is now generally available in the US West (Oregon) AWS Region; check the full Region list for future updates. To learn more, visit Custom models in the Amazon Bedrock documentation.

Give fine-tuning for the Claude 3 Haiku model a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

I look forward to seeing what you build when you put this new technology to work for your business.

— Channy

Unlock the potential of your supply chain data and gain actionable insights with AWS Supply Chain Analytics

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/unlock-the-potential-of-your-supply-chain-data-and-gain-actionable-insights-with-aws-supply-chain-analytics/

Today, we’re announcing the general availability of AWS Supply Chain Analytics powered by Amazon QuickSight. This new feature helps you to build custom report dashboards using your data in AWS Supply Chain. With this feature, your business analysts or supply chain managers can perform custom analyses, visualize data, and gain actionable insights for your supply chain management operations.

Here’s how it looks:

AWS Supply Chain Analytics leverages the AWS Supply Chain data lake and provides Amazon QuickSight embedded authoring tools directly into the AWS Supply Chain user interface. This integration provides you with a unified and configurable experience for creating custom insights, metrics, and key performance indicators (KPIs) for your operational analytics.

In addition, AWS Supply Chain Analytics provides prebuilt dashboards that you can use as-is or modify based on your needs. At launch, you will have the following prebuilt dashboards:

  1. Plan-Over-Plan Variance: Presents a comparison between two demand plans, showcasing variances in both units and values across key dimensions such as product, site, and time periods.
  2. Seasonality Analytics: Presents a year-over-year view of demand, illustrating trends in average demand quantities and highlighting seasonality patterns through heatmaps at both monthly and weekly levels.

Let’s get started
Let me walk you through the features of AWS Supply Chain Analytics.

The first step is to enable AWS Supply Chain Analytics. To do this, navigate to Settings, then select Organizations and choose Analytics. Here, I can Enable data access for Analytics.

Now I can edit existing roles or create a new role with analytics access. To learn more, visit User permission roles.

Once this feature is enabled, when I log in to AWS Supply Chain I can access the AWS Supply Chain Analytics feature by selecting either the Connecting to Analytics card or Analytics on the left navigation menu.

Here, I have an embedded Amazon QuickSight interface ready for me to use. To get started, I navigate to Prebuilt Dashboards.

Then, I can select the prebuilt dashboards I need in the Supply Chain Function dropdown list:

What I like the most about this prebuilt dashboards is I can easily get started. AWS Supply Chain Analytics will prepare all the datasets, analysis, and even a dashboard for me. I select Add to begin.

Then, I navigate to the dashboard page, and I can see the results. I can also share this dashboard with my team, which improves the collaboration aspect.

If I need to include other datasets for me to build a custom dashboard, I can navigate to Datasets and select New dataset.

Here, I have AWS Supply Chain data lake as an existing dataset for me to use.

Next, I need to select Create dataset.

Then, I can select a table that I need to include into my analysis. On the Data section, I can see all available fields. All data sets that start with asc_ are generated by AWS Supply Chain, such as data from Demand Planning, Insights, Supply Planning, and others.

I can also find all the datasets I have ingested into AWS Supply Chain. To learn more on data entities, visit the AWS Supply Chain documentation page. One thing to note here is if I have not ingested data into AWS Supply Chain Data Lake, I need to ingest data before using AWS Supply Chain Analytics. To learn how to ingest data into the data lake, visit the data lake page.

At this stage, I can start my analysis. 

Now available
AWS Supply Chain Analytics is now generally available in all regions where AWS Supply Chain is offered. Give it a try to experience and transform your operations with the AWS Supply Chain Analytics.

Happy building,
— Donnie

Amazon Aurora PostgreSQL Limitless Database is now generally available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/amazon-aurora-postgresql-limitless-database-is-now-generally-available/

Today, we are announcing the general availability of Amazon Aurora PostgreSQL Limitless Database, a new serverless horizontal scaling (sharding) capability of Amazon Aurora. With Aurora PostgreSQL Limitless Database, you can scale beyond the existing Aurora limits for write throughput and storage by distributing a database workload over multiple Aurora writer instances while maintaining the ability to use it as a single database.

When we previewed Aurora PostgreSQL Limitless Database at AWS re:Invent 2023, I explained that it uses a two-layer architecture consisting of multiple database nodes in a DB shard group – either routers or shards to scale based on the workload.

  • Routers – Nodes that accept SQL connections from clients, send SQL commands to shards, maintain system-wide consistency, and return results to clients.
  • Shards – Nodes that store a subset of tables and full copies of data, which accept queries from routers.

There will be three types of tables that contain your data: sharded, reference, and standard.

  • Sharded tables – These tables are distributed across multiple shards. Data is split among the shards based on the values of designated columns in the table, called shard keys. They are useful for scaling the largest, most I/O-intensive tables in your application.
  • Reference tables – These tables copy data in full on every shard so that join queries can work faster by eliminating unnecessary data movement. They are commonly used for infrequently modified reference data, such as product catalogs and zip codes.
  • Standard tables – These tables are like regular Aurora PostgreSQL tables. Standard tables are all placed together on a single shard so join queries can work faster by eliminating unnecessary data movement. You can create sharded and reference tables from standard tables.

Once you have created the DB shard group and your sharded and reference tables, you can load massive amounts of data into Aurora PostgreSQL Limitless Database and query data in those tables using standard PostgreSQL queries. To learn more, visit Limitless Database architecture in the Amazon Aurora User Guide.

Getting started with Aurora PostgreSQL Limitless Database
You can get started in the AWS Management Console and AWS Command Line Interface (AWS CLI) to create a new DB cluster that uses Aurora PostgreSQL Limitless Database, add a DB shard group to the cluster, and query your data.

1. Create an Aurora PostgreSQL Limitless Database Cluster
Open the Amazon Relational Database Service (Amazon RDS) console and choose Create database. For Engine options, choose Aurora (PostgreSQL Compatible) and Aurora PostgreSQL with Limitless Database (Compatible with PostgreSQL 16.4).

For Aurora Limitless Database, enter a name for your DB shard group and values for minimum and maximum capacity measured by Aurora capacity units (ACUs) across all routers and shards. The initial number of routers and shards in a DB shard group is determined by this maximum capacity. Aurora PostgreSQL Limitless Database scales a node up to a higher capacity when its current utilization is too low to handle the load. It scales the node down to a lower capacity when its current capacity is higher than needed.

For DB shard group deployment, choose whether to create standbys for the DB shard group: no compute redundancy, one compute standby in a different Availability Zone, or two compute standbys in two different Availability Zones.

You can set the remaining DB settings to what you prefer and choose Create database. After the DB shard group are created, they’re displayed on the Databases page.

You can connect, reboot, or delete a DB shard group, or you can change the capacity, split a shard, or add a router in the DB shard group. To learn more, visit Working with DB shard groups in the Amazon Aurora User Guide.

2. Create Aurora PostgreSQL Limitless Database tables
As shared previously, Aurora PostgreSQL Limitless Database has three table types: sharded, reference, and standard. You can convert standard tables to sharded or reference tables to distribute or replicate existing standard tables or create new sharded and reference tables.

You can use variables to create sharded and reference tables by setting the table creation mode. The tables that you create will use this mode until you set a different mode. The following examples show how to use these variables to create sharded and reference tables.

For example, create a sharded table named items with a shard key composed of the item_id and item_cat columns.

SET rds_aurora.limitless_create_table_mode='sharded';
SET rds_aurora.limitless_create_table_shard_key='{"item_id", "item_cat"}';
CREATE TABLE items(item_id int, item_cat varchar, val int, item text);

Now, create a sharded table named item_description with a shard key composed of the item_id and item_cat columns and collocate it with the items table.

SET rds_aurora.limitless_create_table_collocate_with='items';
CREATE TABLE item_description(item_id int, item_cat varchar, color_id int, ...);

You can also create a reference table named colors.

SET rds_aurora.limitless_create_table_mode='reference';
CREATE TABLE colors(color_id int primary key, color varchar);

You can find information about Limitless Database tables by using the rds_aurora.limitless_tables view, which contains information about tables and their types.

postgres_limitless=> SELECT * FROM rds_aurora.limitless_tables;

 table_gid | local_oid | schema_name | table_name  | table_status | table_type  | distribution_key
-----------+-----------+-------------+-------------+--------------+-------------+------------------
         1 |     18797 | public      | items       | active       | sharded     | HASH (item_id, item_cat)
         2 |     18641 | public      | colors      | active       | reference   | 

(2 rows)

You can convert standard tables into sharded or reference tables. During the conversion, data is moved from the standard table to the distributed table, then the source standard table is deleted. To learn more, visit Converting standard tables to limitless tables in the Amazon Aurora User Guide.

3. Query Aurora PostgreSQL Limitless Database tables
Aurora PostgreSQL Limitless Database is compatible with PostgreSQL syntax for queries. You can query your Limitless Database using psql or any other connection utility that works with PostgreSQL. Before querying tables, you can load data into Aurora Limitless Database tables by using the COPY command or by using the data loading utility.

To run queries, connect to the cluster endpoint, as shown in Connecting to your Aurora Limitless Database DB cluster. All PostgreSQL SELECT queries are performed on the router to which the client sends the query and shards where the data is located.

To achieve a high degree of parallel processing, Aurora PostgreSQL Limitless Database utilizes two querying methods: single-shard queries and distributed queries, which determines whether your query is single-shard or distributed and processes the query accordingly.

  • Single-shard query – A query where all the data needed for the query is on one shard. The entire operation can be performed on one shard, including any result set generated. When the query planner on the router encounters a query like this, the planner sends the entire SQL query to the corresponding shard.
  • Distributed query – A query run on a router and more than one shard. The query is received by one of the routers. The router creates and manages the distributed transaction, which is sent to the participating shards. The shards create a local transaction with the context provided by the router, and the query is run.

For examples of single-shard queries, you use the following parameters to configure the output from the EXPLAIN command.

postgres_limitless=> SET rds_aurora.limitless_explain_options = shard_plans, single_shard_optimization;
SET

postgres_limitless=> EXPLAIN SELECT * FROM items WHERE item_id = 25;

                     QUERY PLAN
--------------------------------------------------------------
 Foreign Scan  (cost=100.00..101.00 rows=100 width=0)
   Remote Plans from Shard postgres_s4:
         Index Scan using items_ts00287_id_idx on items_ts00287 items_fs00003  (cost=0.14..8.16 rows=1 width=15)
           Index Cond: (id = 25)
 Single Shard Optimized
(5 rows) 

To learn more about the EXPLAIN command, see EXPLAIN in the PostgreSQL documentation.

For examples of distributed queries, you can insert new items named Book and Pen into the items table.

postgres_limitless=> INSERT INTO items(item_name)VALUES ('Book'),('Pen')

This makes a distributed transaction on two shards. When the query runs, the router sets a snapshot time and passes the statement to the shards that own Book and Pen. The router coordinates an atomic commit across both shards, and returns the result to the client.

You can use distributed query tracing, a tool to trace and correlate queries in PostgreSQL logs across Aurora PostgreSQL Limitless Database. To learn more, visit Querying Limitless Database in the Amazon Aurora User Guide.

Some SQL commands aren’t supported. For more information, see Aurora Limitless Database reference in the Amazon Aurora User Guide.

Things to know
Here are a couple of things that you should know about this feature:

  • Compute – You can only have one DB shard group per DB cluster and set the maximum capacity of a DB shard group to 16–6144 ACUs. Contact us if you need more than 6144 ACUs. The initial number of routers and shards is determined by the maximum capacity that you set when you create a DB shard group. The number of routers and shards doesn’t change when you modify the maximum capacity of a DB shard group. To learn more, see the table of the number of routers and shards in the Amazon Aurora User Guide.
  • Storage – Aurora PostgreSQL Limitless Database only supports the Amazon Aurora I/O-Optimized DB cluster storage configuration. Each shard has a maximum capacity of 128 TiB. Reference tables have a size limit of 32 TiB for the entire DB shard group. To reclaim storage space by cleaning up your data, you can use the vacuuming utility in PostgreSQL.
  • Monitoring – You can use Amazon CloudWatch, Amazon CloudWatch Logs, or Performance Insights to monitor Aurora PostgreSQL Limitless Database. There are also new statistics functions and views and wait events for Aurora PostgreSQL Limitless Database that you can use for monitoring and diagnostics.

Now available
Amazon Aurora PostgreSQL Limitless Database is available today with PostgreSQL 16.4 compatibility in the AWS US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) Regions.

Give Aurora PostgreSQL Limitless Database a try in the Amazon Aurora console. For more information, visit the Amazon Aurora User Guide and send feedback to AWS re:Post for Amazon Aurora or through your usual AWS support contacts.

— Channy

Simplify and enhance Amazon S3 static website hosting with AWS Amplify Hosting

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/simplify-and-enhance-amazon-s3-static-website-hosting-with-aws-amplify/

We are announcing an integration between AWS Amplify Hosting and Amazon Simple Storage Service (Amazon S3). Now, you can deploy static websites with content stored in your S3 buckets and serve over a content delivery network (CDN) with just a few clicks.

AWS Amplify Hosting is a fully managed service for hosting static sites that handles various aspects of deploying a website. It gives you benefits such as custom domain configuration with SSL, redirects, custom headers, and deployment on a globally available CDN powered by Amazon CloudFront.

When deploying a static website, Amplify remembers the connection between your S3 bucket and deployed website, so you can easily update your website with a single click when you make changes to website content in your S3 bucket. Using AWS Amplify Hosting is the recommended approach for static website hosting because it offers more streamlined and faster deployment without extensive setup.

Here’s how the integration works starting from the Amazon S3 console:

Deploying a static website using the Amazon S3 console
Let’s use this new integration to host a personal website directly from my S3 bucket.

To get started, I navigate to my bucket in the Amazon S3 console . Here’s the list of all the content in that S3 bucket:

To use the new integration with AWS Amplify Hosting, I navigate to the Properties section, then I scroll down until I find Static website hosting and select Create Amplify app.

Then, it redirects me to the Amplify page and populates the details from my S3 bucket. Here, I configure my App name and the Branch name. Then, I select Save and deploy.

Within seconds, AWS Amplify has deployed my static website, and I can visit the site by selecting Visit deployed URL. If I make any subsequent changes in my S3 bucket for my static website, I need to redeploy my application in the Amplify console by selecting the Deploy updates button.

I can also use the AWS Command Line Interface (AWS CLI) for programmatic deployment. To do that, I need to get the values for required parameters, such as APP_ID and BRANCH_NAME from my AWS Amplify dashboard. Here’s the command I use for deployment:

aws amplify start-deployment --appId APP_ID --branchName BRANCH_NAME --sourceUrlType=BUCKET_PREFIX --sourceUrl s3://S3_BUCKET/S3_PREFIX

After Amplify Hosting generates a URL for my website, I can optionally configure a custom domain for my static website. To do that, I navigate to my apps in AWS Amplify and select Custom domains in the navigation pane. Then, I select Add domain to start configuring a custom domain for my static website. Learn more about setting up custom domains in the Amplify Hosting User Guide.

In the following screenshot, I have my static website configured with my custom domain. Amplify also issues an SSL/TLS certificate for my domain so that all traffic is secured through HTTPS.

Now, I have my static site ready, and I can check it out at https://donnie.id.

Things you need to know
More available features – AWS Amplify Hosting has more features you can use for your static websites. Visit the AWS Amplify product page to learn more.

Deployment options – You can get started deploying a static website from Amazon S3 using the Amplify Hosting console, AWS CLI, or AWS SDKs.

Pricing – For pricing information, visit Amazon S3 pricing page and AWS Amplify pricing page.

Availability – Amplify Hosting integration with Amazon S3 is now available in AWS Regions where Amplify Hosting is available. 

Start building your static website with this new integration. To learn more about Amazon S3 static website hosting with AWS Amplify, visit the AWS Amplify Hosting User Guide. 

Happy building,

— Donnie

AWS Weekly Roundup: New code editor in AWS Lambda console, Amazon Q Business analytics, Claude 3.5 upgrades, and more (October 28, 2024)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-new-code-editor-in-aws-lambda-console-amazon-q-business-analytics-claude-3-5-upgrades-and-more-october-28-2024/

Two weeks ago, I had the wonderful opportunity to host subject matter experts from across Asia Pacific in the global 24 Hours of Amazon Q live stream event. This continuous 24-hour stream offered insights from AWS experts on Amazon Q Developer and Amazon Q Business, featuring use cases, product demos, and Q&A sessions.

The highlight for me was that I learned a lot from them. Since then, I’ve tried to integrate Amazon Q Business into my workflow. If you’re curious about what Amazon Q can do for you, check out the on-demand replay on Twitch.

Last week’s launches
Here’s a recap of AWS launches that caught my attention last week:

AWS Lambda console now features a new code editor based on Code-OSS (VS Code – Open Source) — AWS Lambda introduces a new code editing experience in the AWS console based on the popular Code-OSS, Visual Studio Code Open Source code editor. You can use your preferred coding environment and tools in the Lambda console.

Amazon Bedrock Custom Model Import now generally available — Amazon Bedrock now allows customers to import and use their customized models alongside existing foundation models through a single, unified API. This feature supports leveraging fine-tuned models or developing proprietary models based on popular open-source architectures without managing infrastructure or model lifecycle tasks.

EC2 Image Builder now supports building and testing macOS images — EC2 Image Builder adds support for creating and managing machine images for macOS workloads, in addition to existing Windows and Linux support. It streamlines image management processes and reduces the operational overhead of maintaining macOS images.

Upgraded Claude 3.5 Sonnet from Anthropic (available now), computer use (public beta), and Claude 3.5 Haiku (coming soon) in Amazon Bedrock — Anthropic’s Claude 3.5 model family in Amazon Bedrock receives significant upgrades, including improved intelligence for Claude 3.5 Sonnet and new computer use capabilities in public beta. These enhancements support building more advanced AI applications, automating complex tasks, and leveraging improved reasoning capabilities for various use cases.

Amazon Connect now offers screen sharing — Amazon Connect introduces screen sharing capabilities for agents. This feature is available in multiple regions and can be easily integrated into existing voice and video calling setups. This feature gives you opportunity to personalize and improve customer experiences.

Amazon Aurora launches Global Database writer endpoint — Amazon Aurora now supports a highly available and fully managed Global Database writer endpoint. This feature simplifies routing for applications and eliminates the need for application code changes after initiating cross-region Global Database Switchover or Failover operations.

Gain deeper insights into Amazon Q Business with new analytics and conversation insights — Amazon Q Business now offers an analytics dashboard and integration with Amazon CloudWatch Logs. You now have comprehensive insights into the usage of Amazon Q Business application environments and Amazon Q Apps, facilitating monitoring, analysis, and optimization of usage.

Announcing the new Resiliency widget on myApplications — AWS introduces a new Resiliency widget on myApplications, offering enhanced visibility and control over application resilience. You can start a resilience assessment directly from the myApplications dashboard and gain actionable insights.

From community.aws
Here’s my top 5 personal favorites posts from community.aws:

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

AWS GenAI Lofts – Gain deep insights, get your questions answered, and learn all you need to know to start building your next innovation at AWS GenAI Lofts: Seoul (October 30–November 6), São Paulo (through November 20), and Paris (through November 25).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs. Upcoming AWS Community Days are in: Malta (November 8), Malaysia, Chile (November 9), Indonesia (November 23), Kochi, India (December 14).

AWS re:Invent – Registration is now open for the annual tech extravaganza, taking place December 2–6 in Las Vegas. Learn about new product launches, watch demos, and get behind-the-scenes insights during five headline-making keynotes.

You can browse all upcoming in-person and virtual events.

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

— Donnie

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