Amazon Bedrock Data Automation (BDA) is now generally available in Europe (Frankfurt), Europe (London), Europe (Ireland), Asia Pacific (Mumbai) and Asia Pacific (Sydney).

BDA is a feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. By leveraging BDA, developers can reduce development time and effort, making it easier to build intelligent document processing, media analysis, and other multimodal data-centric automation solutions. BDA can be used as a standalone feature or as a parser in Amazon Knowledge Bases RAG workflows.

With this launch, BDA is now available in a total of 7 AWS Regions, including US West (Oregon) and US East (N. Virginia) Regions. To learn more, visit the Bedrock Data Automation product page and the Amazon Bedrock Pricing page.

 

​Amazon Bedrock Data Automation (BDA) is now generally available in Europe (Frankfurt), Europe (London), Europe (Ireland), Asia Pacific (Mumbai) and Asia Pacific (Sydney). BDA is a feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. By leveraging BDA, developers can reduce development time and effort, making it easier to build intelligent document processing, media analysis, and other multimodal data-centric automation solutions. BDA can be used as a standalone feature or as a parser in Amazon Knowledge Bases RAG workflows. With this launch, BDA is now available in a total of 7 AWS Regions, including US West (Oregon) and US East (N. Virginia) Regions. To learn more, visit the Bedrock Data Automation product page and the Amazon Bedrock Pricing page.  

AWS Glue now supports zero-ETL integration (managed ingestion) from Amazon DynamoDB and eight applications to Amazon S3 Tables, automating the extraction and loading of data into S3 Tables from DynamoDB and applications like Salesforce, SAP, ServiceNow, and Zendesk.

S3 Tables are purpose-built for storing tabular data at scale, with built-in Apache Iceberg support. You can enable S3 Tables to work with AWS Lake Formation to support various analytics services, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. Zero-ETL integrations are fully managed by AWS and minimize the need to build and manage ETL data pipelines. With this new zero-ETL integration, you can efficiently extract and load data from DynamoDB tables or from your customer support, relationship management, and ERP applications into your S3 Table-backed data lake for analysis. Zero-ETL integration reduces users’ operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines.

Zero-ETL integration from DynamoDB and eight applications to S3 Tables is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Stockholm), Europe (Frankfurt), Europe (Ireland), South America (Sao Paulo), Asia Pacific (Seoul), Europe (London), and Canada (Central) AWS Regions.

You can create and manage integrations using either the AWS Glue console, the AWS Command Line Interface (AWS CLI), or the AWS Glue APIs. To learn more, visit What is zero-ETL and Glue zero-ETL documentation.

 

​AWS Glue now supports zero-ETL integration (managed ingestion) from Amazon DynamoDB and eight applications to Amazon S3 Tables, automating the extraction and loading of data into S3 Tables from DynamoDB and applications like Salesforce, SAP, ServiceNow, and Zendesk. S3 Tables are purpose-built for storing tabular data at scale, with built-in Apache Iceberg support. You can enable S3 Tables to work with AWS Lake Formation to support various analytics services, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. Zero-ETL integrations are fully managed by AWS and minimize the need to build and manage ETL data pipelines. With this new zero-ETL integration, you can efficiently extract and load data from DynamoDB tables or from your customer support, relationship management, and ERP applications into your S3 Table-backed data lake for analysis. Zero-ETL integration reduces users’ operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines. Zero-ETL integration from DynamoDB and eight applications to S3 Tables is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Stockholm), Europe (Frankfurt), Europe (Ireland), South America (Sao Paulo), Asia Pacific (Seoul), Europe (London), and Canada (Central) AWS Regions. You can create and manage integrations using either the AWS Glue console, the AWS Command Line Interface (AWS CLI), or the AWS Glue APIs. To learn more, visit What is zero-ETL and Glue zero-ETL documentation.  

Today, we are excited to announce that Luma AI’s Ray2 model on Amazon Bedrock now supports image-to-video generation.

This new feature expands upon the text-to-video generation capabilities introduced in January, providing developers with even more powerful tools for creating dynamic video content. With this update, customers can now transform static .jpeg and .png images of up to 25MB into captivating videos using the state-of-the-art Ray2 model, opening up new possibilities for content creation and visual storytelling. The addition of image-to-video generation to Ray2 on Amazon Bedrock empowers developers and content creators to bring their static visuals to life. This feature is particularly valuable for industries such as advertising, entertainment, and e-commerce, where engaging video content can significantly enhance user experience and engagement. By leveraging the power of AI to generate videos from images, businesses can save time and resources while producing high-quality, dynamic content at scale.

Luma AI’s Ray2 model is available in the US West (Oregon) AWS Region. To learn more about Ray2 and how to use it in your projects, read the AWS News Blog, visit the Luma AI in Amazon Bedrock page, the Amazon Bedrock console, or check out the Amazon Bedrock documentation.

 

​Today, we are excited to announce that Luma AI’s Ray2 model on Amazon Bedrock now supports image-to-video generation. This new feature expands upon the text-to-video generation capabilities introduced in January, providing developers with even more powerful tools for creating dynamic video content. With this update, customers can now transform static .jpeg and .png images of up to 25MB into captivating videos using the state-of-the-art Ray2 model, opening up new possibilities for content creation and visual storytelling. The addition of image-to-video generation to Ray2 on Amazon Bedrock empowers developers and content creators to bring their static visuals to life. This feature is particularly valuable for industries such as advertising, entertainment, and e-commerce, where engaging video content can significantly enhance user experience and engagement. By leveraging the power of AI to generate videos from images, businesses can save time and resources while producing high-quality, dynamic content at scale. Luma AI’s Ray2 model is available in the US West (Oregon) AWS Region. To learn more about Ray2 and how to use it in your projects, read the AWS News Blog, visit the Luma AI in Amazon Bedrock page, the Amazon Bedrock console, or check out the Amazon Bedrock documentation.  

AWS Transform for mainframe now offers enhanced reforge and business logic extraction functionality to further streamline mainframe modernization. These new capabilities help organizations reduce modernization time, improve code quality and maintainability, and optimize modernization and migration costs.

The reforge capability in AWS Transform for mainframe is now generally available, enhancing transformed Java code by restructuring complex methods, adding descriptive comments, optimizing variable usage, and improving code flow. This results in more readable and maintainable code for developers. Additionally, AWS Transform for mainframe’s business logic extraction capability now provides application-level insights, from high-level summaries to detailed business function analysis, complementing the existing file-level business logic extraction, to help users better understand their legacy applications.

These capabilities are now available in all AWS Regions where AWS Transform is offered. To learn more, visit the AWS Transform for mainframe product page, read the user guide, or get started in the AWS Transform web experience.

 

​AWS Transform for mainframe now offers enhanced reforge and business logic extraction functionality to further streamline mainframe modernization. These new capabilities help organizations reduce modernization time, improve code quality and maintainability, and optimize modernization and migration costs. The reforge capability in AWS Transform for mainframe is now generally available, enhancing transformed Java code by restructuring complex methods, adding descriptive comments, optimizing variable usage, and improving code flow. This results in more readable and maintainable code for developers. Additionally, AWS Transform for mainframe’s business logic extraction capability now provides application-level insights, from high-level summaries to detailed business function analysis, complementing the existing file-level business logic extraction, to help users better understand their legacy applications. These capabilities are now available in all AWS Regions where AWS Transform is offered. To learn more, visit the AWS Transform for mainframe product page, read the user guide, or get started in the AWS Transform web experience.  

Amazon SageMaker has simplified the process of creating, querying, and joining Amazon S3 Tables with data in Amazon S3 general purpose buckets, Amazon Redshift data warehouses, and third-party data sources by allowing customers to create S3 table buckets and the related catalogs without having to navigate between multiple AWS consoles.

Users can now create tables, load data, and run queries using the Query Editor or Jupyter Notebook within SageMaker Unified Studio. For administrators, the update includes the ability to enable analytics integration with S3 for their AWS account and create custom profiles. Project owners can use these profiles to set up projects with pre-configured catalogs and S3 Tables support, reducing the manual configuration steps required to get started.

This updated S3 Tables experience in SageMaker Unified Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada(Central), South America (São Paulo), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo),Asia Pacific (Sydney), Europe (Paris), Europe (Stockholm), Europe (London), and Europe (Frankfurt).

To get started with the updated S3 Tables workflow in SageMaker Unified Studio, see the Amazon SageMaker documentation.

 

​Amazon SageMaker has simplified the process of creating, querying, and joining Amazon S3 Tables with data in Amazon S3 general purpose buckets, Amazon Redshift data warehouses, and third-party data sources by allowing customers to create S3 table buckets and the related catalogs without having to navigate between multiple AWS consoles. Users can now create tables, load data, and run queries using the Query Editor or Jupyter Notebook within SageMaker Unified Studio. For administrators, the update includes the ability to enable analytics integration with S3 for their AWS account and create custom profiles. Project owners can use these profiles to set up projects with pre-configured catalogs and S3 Tables support, reducing the manual configuration steps required to get started. This updated S3 Tables experience in SageMaker Unified Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada(Central), South America (São Paulo), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo),Asia Pacific (Sydney), Europe (Paris), Europe (Stockholm), Europe (London), and Europe (Frankfurt). To get started with the updated S3 Tables workflow in SageMaker Unified Studio, see the Amazon SageMaker documentation.  

Today, AWS announces the developer preview of the AWS API model context protocol (MCP) server, a new tool that enables foundation models (FMs) to interact with any AWS API through natural language by creating and executing syntactically correct and valid CLI commands.

With the AWS API MCP Server, customers using popular MCP clients can streamline tasks like troubleshooting workloads, managing application deployments, and exploring AWS services and capabilities more easily, by issuing natural language requests that the host FM can translate into API calls.

The AWS API MCP Server allows MCP clients to discover supported AWS APIs and make calls to them through the host FM, enabling actions such as inspecting, creating, and modifying AWS resources. The server provides secure access control through AWS Identity and Access Management (IAM) credentials and pre-configured API permissions, ensuring that FMs can only access or perform authorized actions on permitted AWS APIs.

The AWS API MCP Server is released as an open source project and available now. Visit the AWS Labs GitHub repository to download, deploy, and start experimenting with natural language interaction with AWS APIs today.

 

​Today, AWS announces the developer preview of the AWS API model context protocol (MCP) server, a new tool that enables foundation models (FMs) to interact with any AWS API through natural language by creating and executing syntactically correct and valid CLI commands. With the AWS API MCP Server, customers using popular MCP clients can streamline tasks like troubleshooting workloads, managing application deployments, and exploring AWS services and capabilities more easily, by issuing natural language requests that the host FM can translate into API calls. The AWS API MCP Server allows MCP clients to discover supported AWS APIs and make calls to them through the host FM, enabling actions such as inspecting, creating, and modifying AWS resources. The server provides secure access control through AWS Identity and Access Management (IAM) credentials and pre-configured API permissions, ensuring that FMs can only access or perform authorized actions on permitted AWS APIs. The AWS API MCP Server is released as an open source project and available now. Visit the AWS Labs GitHub repository to download, deploy, and start experimenting with natural language interaction with AWS APIs today.  

Today, AWS announces the preview release of AWS Knowledge Model Context Protocol (MCP) Server, a new tool that surfaces authoritative AWS knowledge in an LLM-compatible format, including documentation, blog posts, What’s New announcements, and Well-Architected best practices.

AWS Knowledge MCP Server enables clients and foundation models (FMs) that support MCP to ground their responses in trusted AWS context, guidance, and best practices, providing the guidance needed for accurate reasoning and consistent execution, while reducing manual context management. Customers can now focus on business problems instead of searching for information manually.

The server is publicly accessible at no cost and does not require an AWS account. Usage is subject to rate limits. Give your developers and agents access to the most up-to-date AWS information today by configuring your MCP clients to use the AWS Knowledge MCP Server endpoint, and follow the Getting Started guide for setup instructions.

 

​Today, AWS announces the preview release of AWS Knowledge Model Context Protocol (MCP) Server, a new tool that surfaces authoritative AWS knowledge in an LLM-compatible format, including documentation, blog posts, What’s New announcements, and Well-Architected best practices. AWS Knowledge MCP Server enables clients and foundation models (FMs) that support MCP to ground their responses in trusted AWS context, guidance, and best practices, providing the guidance needed for accurate reasoning and consistent execution, while reducing manual context management. Customers can now focus on business problems instead of searching for information manually.
The server is publicly accessible at no cost and does not require an AWS account. Usage is subject to rate limits. Give your developers and agents access to the most up-to-date AWS information today by configuring your MCP clients to use the AWS Knowledge MCP Server endpoint, and follow the Getting Started guide for setup instructions.  

Amazon EBS now provides visibility into the EBS volume initialization status for volumes created from EBS snapshots. You can use this status to determine when your volume becomes fully initialized when restoring from a snapshot and is fully ready to support latency-sensitive applications.

EBS volumes that are created from EBS snapshots undergo volume initialization, in which the storage blocks from the snapshot must be downloaded from Amazon S3 and written to the volume before you can access them. The volume initialization rate fluctuates throughout the initialization process, which could make completion times unpredictable. During initialization you may notice increased I/O latency and reduced performance. Our new volume initialization status feature allows you to validate when all blocks have been downloaded and written to the volume, enabling fully provisioned performance on your volume. Using this status, you can time your application launches to align with volume initialization completion. Monitor initialization progress in real-time and launch your applications only when volumes are fully ready, ensuring optimal performance from the start. When creating volumes with Provisioned Rate for Volume Initialization, you will also be able to see the estimated completion time for your volume initialization.

Volume initialization status is accessible by default for all EBS volumes. It is available in all AWS Regions, including the AWS GovCloud (US) Regions and AWS China Regions. You can start using this feature today through the AWS Management Console, AWS Command Line Interface (CLI), or AWS SDKs. To learn more about the new volume initialization status and how to access it, please visit the EBS initialize volume documentation. 

 

​Amazon EBS now provides visibility into the EBS volume initialization status for volumes created from EBS snapshots. You can use this status to determine when your volume becomes fully initialized when restoring from a snapshot and is fully ready to support latency-sensitive applications. EBS volumes that are created from EBS snapshots undergo volume initialization, in which the storage blocks from the snapshot must be downloaded from Amazon S3 and written to the volume before you can access them. The volume initialization rate fluctuates throughout the initialization process, which could make completion times unpredictable. During initialization you may notice increased I/O latency and reduced performance. Our new volume initialization status feature allows you to validate when all blocks have been downloaded and written to the volume, enabling fully provisioned performance on your volume. Using this status, you can time your application launches to align with volume initialization completion. Monitor initialization progress in real-time and launch your applications only when volumes are fully ready, ensuring optimal performance from the start. When creating volumes with Provisioned Rate for Volume Initialization, you will also be able to see the estimated completion time for your volume initialization. Volume initialization status is accessible by default for all EBS volumes. It is available in all AWS Regions, including the AWS GovCloud (US) Regions and AWS China Regions. You can start using this feature today through the AWS Management Console, AWS Command Line Interface (CLI), or AWS SDKs. To learn more about the new volume initialization status and how to access it, please visit the EBS initialize volume documentation.   

Amazon Nova Sonic—a speech-to-speech foundation model—now supports French, Italian, and German, expanding on its existing coverage of English and Spanish. This update includes six additional expressive voices, offering both masculine and feminine-sounding options, to help developers create more natural and inclusive conversational AI experiences across a wider range of languages.

In addition, Amazon Nova Sonic now integrates with LiveKit, an open-source WebRTC platform, and Pipecat, an open-source framework for building voice and multimodal AI agents. These integrations simplify the development of low-latency, real-time voice applications by removing the need to manage complex audio pipelines and streaming infrastructure. As an added capability, Nova Sonic now also supports integrations with Vonage and Twilio, extending deployment flexibility for telephony and communications use cases.

Amazon Nova Sonic is a speech-to-speech foundation model that delivers real-time, human-like voice conversations with low latency. Available in Amazon Bedrock via the bidirectional streaming API, the model understands streaming speech in various speaking styles and generates expressive speech responses that dynamically adapt to the prosody of input speech.

Amazon Nova Sonic is now available globally on Amazon Bedrock in three AWS Region. To learn more, read the AWS News Blog, Amazon Nova Sonic product page, and User Guide. To get started, visit the Amazon Bedrock Console.

 

​Amazon Nova Sonic—a speech-to-speech foundation model—now supports French, Italian, and German, expanding on its existing coverage of English and Spanish. This update includes six additional expressive voices, offering both masculine and feminine-sounding options, to help developers create more natural and inclusive conversational AI experiences across a wider range of languages. In addition, Amazon Nova Sonic now integrates with LiveKit, an open-source WebRTC platform, and Pipecat, an open-source framework for building voice and multimodal AI agents. These integrations simplify the development of low-latency, real-time voice applications by removing the need to manage complex audio pipelines and streaming infrastructure. As an added capability, Nova Sonic now also supports integrations with Vonage and Twilio, extending deployment flexibility for telephony and communications use cases. Amazon Nova Sonic is a speech-to-speech foundation model that delivers real-time, human-like voice conversations with low latency. Available in Amazon Bedrock via the bidirectional streaming API, the model understands streaming speech in various speaking styles and generates expressive speech responses that dynamically adapt to the prosody of input speech. Amazon Nova Sonic is now available globally on Amazon Bedrock in three AWS Region. To learn more, read the AWS News Blog, Amazon Nova Sonic product page, and User Guide. To get started, visit the Amazon Bedrock Console.  

Today, AWS introduces the AWS AI League, a program that helps organizations upskill their workforce by combining a fun competition with hands-on learning using AWS AI services such as Amazon SageMaker AI and Amazon Bedrock. The program offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization and prompt engineering. Enterprises can apply to receive AWS credits to host internal AWS AI League competitions, fostering a culture of innovation within their organizations. Individual developers can also participate in the AWS AI League at select AWS Summits and AWS re:Invent, giving them a chance to compete while engaging with cutting-edge AI technologies and gaining skills crucial for developing advanced AI solutions.

AWS is committing up to $2 million in AWS credits and a championship prize pool of $25,000 to reward top performers at AWS re:Invent 2025. This significant investment underscores AWS commitment to advancing AI skills across the workforce and accelerating innovation in the field of generative AI.

For more information about the AWS AI League and how to participate, please visit the AWS AI League page. 

 

​Today, AWS introduces the AWS AI League, a program that helps organizations upskill their workforce by combining a fun competition with hands-on learning using AWS AI services such as Amazon SageMaker AI and Amazon Bedrock. The program offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization and prompt engineering. Enterprises can apply to receive AWS credits to host internal AWS AI League competitions, fostering a culture of innovation within their organizations. Individual developers can also participate in the AWS AI League at select AWS Summits and AWS re:Invent, giving them a chance to compete while engaging with cutting-edge AI technologies and gaining skills crucial for developing advanced AI solutions. AWS is committing up to $2 million in AWS credits and a championship prize pool of $25,000 to reward top performers at AWS re:Invent 2025. This significant investment underscores AWS commitment to advancing AI skills across the workforce and accelerating innovation in the field of generative AI. For more information about the AWS AI League and how to participate, please visit the AWS AI League page.