We are excited to announce Q-Index now supports seamless application level authentication for its SearchRelevantContent (SRC) API, simplifying the end-user experience when using Q-Index to retrieve enterprise content.
In the common Amazon Q-Index scenario where a 3rd party application integrates with the Q-Index SRC API, end-users first log into their application in order to use its core features, but when they need to get answers from their enterprise content, the SRC API implemented inside the 3rd party application requires the end-user to authenticate once again, this time against AWS IAM, in order to securely retrieve content from their enterprise knowledge sources. This two-stage authentication is redundant for the user.

To simplify the user authentication experience, Q-Index’s support for seamless application authentication provides a Trusted Token Issuer (TTI) mechanism for developers to issue their own security tokens, trusted by the Q-Index SRC API. End users can therefore just login once into their 3rd party applications and get answers from enterprise content using Q-Index, without authenticating a second time to use this functionality.

Q-Index’s support for seamless application authentication is available in the US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Sydney) AWS Regions. For more information, please consult our documentation.
 

 

​We are excited to announce Q-Index now supports seamless application level authentication for its SearchRelevantContent (SRC) API, simplifying the end-user experience when using Q-Index to retrieve enterprise content. In the common Amazon Q-Index scenario where a 3rd party application integrates with the Q-Index SRC API, end-users first log into their application in order to use its core features, but when they need to get answers from their enterprise content, the SRC API implemented inside the 3rd party application requires the end-user to authenticate once again, this time against AWS IAM, in order to securely retrieve content from their enterprise knowledge sources. This two-stage authentication is redundant for the user. To simplify the user authentication experience, Q-Index’s support for seamless application authentication provides a Trusted Token Issuer (TTI) mechanism for developers to issue their own security tokens, trusted by the Q-Index SRC API. End users can therefore just login once into their 3rd party applications and get answers from enterprise content using Q-Index, without authenticating a second time to use this functionality. Q-Index’s support for seamless application authentication is available in the US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Sydney) AWS Regions. For more information, please consult our documentation.    

AWS Clean Rooms now supports two enhancements to its machine learning capabilities that help you train models more efficiently and at scale to generate predictive insights in a Clean Rooms collaboration. Incremental training enables you to build upon existing model artifacts to create new models, and distributed training allows you to train models across multiple compute instances simultaneously. These capabilities help data scientists and ML practitioners accelerate data collaboration and analysis while maintaining the privacy of the training datasets.

With AWS Clean Rooms ML custom modeling, you and your partners can train and run inference on a custom ML model using collective datasets at scale without having to share sensitive intellectual property. With incremental training, you can leverage previously trained models to create new variants using expanded datasets, significantly reducing training time and compute resources. Additionally, distributed training lets you process large-scale datasets efficiently by distributing the training workload across multiple instances.

AWS Clean Rooms ML helps you and your partners apply privacy-enhancing controls to safeguard your proprietary data and ML models while generating predictive insights—all without sharing or copying one another’s raw data or models. For more information about the AWS Regions where AWS Clean Rooms ML is available, see the AWS Regions table. To learn more, visit AWS Clean Rooms ML.
 

 

​AWS Clean Rooms now supports two enhancements to its machine learning capabilities that help you train models more efficiently and at scale to generate predictive insights in a Clean Rooms collaboration. Incremental training enables you to build upon existing model artifacts to create new models, and distributed training allows you to train models across multiple compute instances simultaneously. These capabilities help data scientists and ML practitioners accelerate data collaboration and analysis while maintaining the privacy of the training datasets. With AWS Clean Rooms ML custom modeling, you and your partners can train and run inference on a custom ML model using collective datasets at scale without having to share sensitive intellectual property. With incremental training, you can leverage previously trained models to create new variants using expanded datasets, significantly reducing training time and compute resources. Additionally, distributed training lets you process large-scale datasets efficiently by distributing the training workload across multiple instances. AWS Clean Rooms ML helps you and your partners apply privacy-enhancing controls to safeguard your proprietary data and ML models while generating predictive insights—all without sharing or copying one another’s raw data or models. For more information about the AWS Regions where AWS Clean Rooms ML is available, see the AWS Regions table. To learn more, visit AWS Clean Rooms ML.    

AWS HealthImaging announces support for storing DICOM P10 files to HealthImaging via the DICOMweb STOW-RS protocol. Now it’s easier than ever to import your medical imaging data to HealthImaging.

This launch offers a synchronous data import action that is ideal for latency sensitive workflows, such as storing new medical imaging studies, annotations, and reports. With HealthImaging STOW-RS, you can import up to 1 GB of DICOM data per action. With this launch, AWS HealthImaging offers DICOMweb APIs for data import, search, and retrievals, which simplifies integrating HealthImaging with existing DICOMweb enabled applications.

AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland).

AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.

 

​AWS HealthImaging announces support for storing DICOM P10 files to HealthImaging via the DICOMweb STOW-RS protocol. Now it’s easier than ever to import your medical imaging data to HealthImaging. This launch offers a synchronous data import action that is ideal for latency sensitive workflows, such as storing new medical imaging studies, annotations, and reports. With HealthImaging STOW-RS, you can import up to 1 GB of DICOM data per action. With this launch, AWS HealthImaging offers DICOMweb APIs for data import, search, and retrievals, which simplifies integrating HealthImaging with existing DICOMweb enabled applications. AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland). AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.  

Amazon RDS Custom for Oracle now supports Multi-AZ deployments, providing high availability for business critical workloads. Amazon RDS Custom for Oracle is a managed service for running Oracle databases on AWS, with ability to customize the database environment and underlying operating system. With Multi-AZ deployments, RDS Custom for Oracle synchronously replicates data between two Availability Zones, and performs an automatic failover in case the primary database instance becomes unavailable so that customers benefit from higher availability.

To set up an RDS Custom for Oracle database instance with Multi-AZ deployment, customers can specify their configuration when they create their database instance. RDS Custom automatically provisions primary and standby database instances in the different availability zones, and synchronously replicates data to the standby instance. If the primary database instance becomes unavailable, RDS Custom automatically fails over to the standby instance without any manual intervention. To learn more about Multi-AZ deployments, see Amazon RDS Custom for Oracle user guide.

Multi-AZ deployment option is available in all AWS regions where RDS Custom for Oracle is available. For pricing details, refer to the RDS Custom for Oracle pricing page.
 

 

​Amazon RDS Custom for Oracle now supports Multi-AZ deployments, providing high availability for business critical workloads. Amazon RDS Custom for Oracle is a managed service for running Oracle databases on AWS, with ability to customize the database environment and underlying operating system. With Multi-AZ deployments, RDS Custom for Oracle synchronously replicates data between two Availability Zones, and performs an automatic failover in case the primary database instance becomes unavailable so that customers benefit from higher availability. To set up an RDS Custom for Oracle database instance with Multi-AZ deployment, customers can specify their configuration when they create their database instance. RDS Custom automatically provisions primary and standby database instances in the different availability zones, and synchronously replicates data to the standby instance. If the primary database instance becomes unavailable, RDS Custom automatically fails over to the standby instance without any manual intervention. To learn more about Multi-AZ deployments, see Amazon RDS Custom for Oracle user guide. Multi-AZ deployment option is available in all AWS regions where RDS Custom for Oracle is available. For pricing details, refer to the RDS Custom for Oracle pricing page.    

Today, AWS re:Post Private announces the launch of channels, designed to enhance collaborative knowledge sharing within companies. Channels allow companies to create secure, dedicated private spaces within their re:Post Private environment, tailored to specific teams, projects, or topics. With channels, companies invite relevant users to collaboratively solve problems and build a team-specific knowledge base. Teams can collaborate on specific topics without exposing content to the entire community in their company. Companies can manage access to channels using Identity and Access Management (IAM) Identity Center groups with centralized access management.

Channels on re:Post Private are ideal for scenarios where companies need to create dedicated, topic-specific spaces for various business functions, such as legal departments or confidential projects. This feature is also valuable for companies supporting multiple agencies or clients, allowing them to establish separate, secure environments for each entity’s cloud requirements. The applicability of channels spans across industries, with notable benefits for the public sector and financial services, where discussing sensitive information within a secure, permission-controlled group is paramount. By leveraging channels, companies can ensure that confidential discussions and collaborative efforts remain contained within the appropriate teams, enhancing both security and efficiency in knowledge sharing.

Channels are available to all re:Post Private customers globally. To get started with re:Post Private channels, visit re:Post Private page to learn more or speak with your AWS account team to request a demo.

 

​Today, AWS re:Post Private announces the launch of channels, designed to enhance collaborative knowledge sharing within companies. Channels allow companies to create secure, dedicated private spaces within their re:Post Private environment, tailored to specific teams, projects, or topics. With channels, companies invite relevant users to collaboratively solve problems and build a team-specific knowledge base. Teams can collaborate on specific topics without exposing content to the entire community in their company. Companies can manage access to channels using Identity and Access Management (IAM) Identity Center groups with centralized access management. Channels on re:Post Private are ideal for scenarios where companies need to create dedicated, topic-specific spaces for various business functions, such as legal departments or confidential projects. This feature is also valuable for companies supporting multiple agencies or clients, allowing them to establish separate, secure environments for each entity’s cloud requirements. The applicability of channels spans across industries, with notable benefits for the public sector and financial services, where discussing sensitive information within a secure, permission-controlled group is paramount. By leveraging channels, companies can ensure that confidential discussions and collaborative efforts remain contained within the appropriate teams, enhancing both security and efficiency in knowledge sharing. Channels are available to all re:Post Private customers globally. To get started with re:Post Private channels, visit re:Post Private page to learn more or speak with your AWS account team to request a demo.  

Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports AI recommendations for descriptions—including table summaries, use cases, and column-level descriptions—for custom structured assets registered programmatically. This applies to a wide range of assets, for example, Iceberg tables in Amazon S3, or datasets from third-party and internal applications.

Building on existing automated metadata capabilities for harvested assets from native services like AWS Glue and Amazon Redshift, this enhancement enables users to generate business-friendly descriptions for custom assets using large language models (LLMs) via Amazon Bedrock.

With just a few clicks, users can trigger AI-generated suggestions, review and refine descriptions, and publish enriched asset metadata directly to the catalog. This helps reduce manual documentation effort, improves metadata consistency, and accelerates asset discoverability across organizations.

Learn more about how to generate automated metadata for custom assets in our product documentation.
 

 

​Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports AI recommendations for descriptions—including table summaries, use cases, and column-level descriptions—for custom structured assets registered programmatically. This applies to a wide range of assets, for example, Iceberg tables in Amazon S3, or datasets from third-party and internal applications. Building on existing automated metadata capabilities for harvested assets from native services like AWS Glue and Amazon Redshift, this enhancement enables users to generate business-friendly descriptions for custom assets using large language models (LLMs) via Amazon Bedrock. With just a few clicks, users can trigger AI-generated suggestions, review and refine descriptions, and publish enriched asset metadata directly to the catalog. This helps reduce manual documentation effort, improves metadata consistency, and accelerates asset discoverability across organizations. Learn more about how to generate automated metadata for custom assets in our product documentation.    

Today, AWS announces the opening of a new AWS Data Transfer Terminal location within Equinix MU1 in Munich, Germany. This marks AWS’s first Data Transfer Terminal outside the United States and its debut in Europe. Data Transfer Terminal is a secure, physical location where you can bring your storage devices and upload data to AWS including Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), and others using a high throughput network connection.

Data Transfer Terminals are ideal for customers who need to transfer large amounts of data to the AWS quickly and securely. Common use cases span various industries and applications, including training data for Advanced Driver Assistance Systems (ADAS) in the automotive industry, video production data for processing in the media and entertainment industry, migrating legacy data in the financial services industry, and uploading equipment sensor data in the industrial and agricultural sectors. Once uploaded, you can immediately leverage AWS services like Amazon Athena for analysis, Amazon SageMaker for machine learning, or Amazon Elastic Compute Cloud (Amazon EC2) for application development – reducing data processing time from weeks to minutes.

To learn more, visit the Data Transfer Terminal product page and documentation. To get started, make a reservation at your nearby Data Transfer Terminal in the AWS Console.
 

 

​Today, AWS announces the opening of a new AWS Data Transfer Terminal location within Equinix MU1 in Munich, Germany. This marks AWS’s first Data Transfer Terminal outside the United States and its debut in Europe. Data Transfer Terminal is a secure, physical location where you can bring your storage devices and upload data to AWS including Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), and others using a high throughput network connection. Data Transfer Terminals are ideal for customers who need to transfer large amounts of data to the AWS quickly and securely. Common use cases span various industries and applications, including training data for Advanced Driver Assistance Systems (ADAS) in the automotive industry, video production data for processing in the media and entertainment industry, migrating legacy data in the financial services industry, and uploading equipment sensor data in the industrial and agricultural sectors. Once uploaded, you can immediately leverage AWS services like Amazon Athena for analysis, Amazon SageMaker for machine learning, or Amazon Elastic Compute Cloud (Amazon EC2) for application development – reducing data processing time from weeks to minutes. To learn more, visit the Data Transfer Terminal product page and documentation. To get started, make a reservation at your nearby Data Transfer Terminal in the AWS Console.    

On June 30, 2025, AWS announced that Amazon Aurora MySQL-Compatible Edition and Amazon RDS for MySQL now support zero-ETL integration with Amazon SageMaker, enabling near real-time data availability for analytics workloads. This integration automatically extracts and loads data from MySQL tables into your lakehouse where it’s immediately accessible through various analytics engines and machine learning tools. The data synced into the lakehouse is compatible with Apache Iceberg open standards, enabling you to use your preferred analytics tools and query engines such as SQL, Apache Spark, BI, and AI/ML tools.

Through a simple no-code interface, you can create and maintain an up-to-date replica of your MySQL data in your lakehouse without impacting production workloads. The integration features comprehensive, fine-grained access controls that are consistently enforced across all analytics tools and engines, ensuring secure data sharing throughout your organization. As a complement to the existing zero-ETL integrations with Amazon Redshift, this solution reduces operational complexity while enabling you to derive immediate insights from your operational data.

Amazon Aurora MySQL and Amazon RDS for MySQL zero-ETL integration with Amazon SageMaker is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), South America (Sao Paulo), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Europe (Frankfurt), Europe (Ireland), Europe (London), and Europe (Stockholm) AWS Regions.

To learn more, visit What is zero-ETL. To begin using this new integration, visit the zero-ETL documentation for your database service: Aurora MySQL or RDS for MySQL.

 

​On June 30, 2025, AWS announced that Amazon Aurora MySQL-Compatible Edition and Amazon RDS for MySQL now support zero-ETL integration with Amazon SageMaker, enabling near real-time data availability for analytics workloads. This integration automatically extracts and loads data from MySQL tables into your lakehouse where it’s immediately accessible through various analytics engines and machine learning tools. The data synced into the lakehouse is compatible with Apache Iceberg open standards, enabling you to use your preferred analytics tools and query engines such as SQL, Apache Spark, BI, and AI/ML tools. Through a simple no-code interface, you can create and maintain an up-to-date replica of your MySQL data in your lakehouse without impacting production workloads. The integration features comprehensive, fine-grained access controls that are consistently enforced across all analytics tools and engines, ensuring secure data sharing throughout your organization. As a complement to the existing zero-ETL integrations with Amazon Redshift, this solution reduces operational complexity while enabling you to derive immediate insights from your operational data. Amazon Aurora MySQL and Amazon RDS for MySQL zero-ETL integration with Amazon SageMaker is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), South America (Sao Paulo), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Europe (Frankfurt), Europe (Ireland), Europe (London), and Europe (Stockholm) AWS Regions. To learn more, visit What is zero-ETL. To begin using this new integration, visit the zero-ETL documentation for your database service: Aurora MySQL or RDS for MySQL.  

Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports AI recommendations for descriptions—including table summaries, use cases, and column-level descriptions—for custom structured assets registered programmatically. This applies to a wide range of assets, for example, Iceberg tables in Amazon S3, or datasets from third-party and internal applications.

Building on existing automated metadata capabilities for harvested assets from native services like AWS Glue and Amazon Redshift, this enhancement enables users to generate business-friendly descriptions for custom assets using large language models (LLMs) via Amazon Bedrock.

With just a few clicks, users can trigger AI-generated suggestions, review and refine descriptions, and publish enriched asset metadata directly to the catalog. This helps reduce manual documentation effort, improves metadata consistency, and accelerates asset discoverability across organizations.

Learn more about how to generate automated metadata for custom assets in our product documentation.
 

 

​Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports AI recommendations for descriptions—including table summaries, use cases, and column-level descriptions—for custom structured assets registered programmatically. This applies to a wide range of assets, for example, Iceberg tables in Amazon S3, or datasets from third-party and internal applications. Building on existing automated metadata capabilities for harvested assets from native services like AWS Glue and Amazon Redshift, this enhancement enables users to generate business-friendly descriptions for custom assets using large language models (LLMs) via Amazon Bedrock. With just a few clicks, users can trigger AI-generated suggestions, review and refine descriptions, and publish enriched asset metadata directly to the catalog. This helps reduce manual documentation effort, improves metadata consistency, and accelerates asset discoverability across organizations. Learn more about how to generate automated metadata for custom assets in our product documentation.    

Amazon Web Services (AWS) has introduced Amazon ECS Optimized Windows AMIs compatible with Windows Server 2025, offering two distinct platforms: 2025-Core and 2025-Full. These AMIs are specifically engineered to support Windows container deployments on Amazon ECS. Each AMI comes ready-to-use with essential components and optimizations tailored for running containerized workloads, streamlining the container deployment process.

Windows Server 2025 provides enhanced performance optimization and improved resource utilization compared to previous versions, allowing for more efficient container operations. It features advanced security capabilities, including better isolation between containers to prevent container escape attacks and stronger kernel boundaries between containers and host. Windows Server 2025 also introduces enhanced networking capabilities like network namespace isolation and better integration with the Host Netwroking Service (HNS) that enable faster container communication and reduced latency.

Customers can find and launch Windows ECS instances directly from the Amazon EC2 Console or through API or CLI commands. Windows ECS Optimized AMIs can be run with all available pricing options for windows ECS instances and are enabled across all Public, AWS GovCloud (US) and China Regions of AWS. For more details on getting the best out of AWS EC2 instances running Windows Server 2025 check out the Windows on AWS page and the guide on AWS Windows ECS AMIs.

 

​Amazon Web Services (AWS) has introduced Amazon ECS Optimized Windows AMIs compatible with Windows Server 2025, offering two distinct platforms: 2025-Core and 2025-Full. These AMIs are specifically engineered to support Windows container deployments on Amazon ECS. Each AMI comes ready-to-use with essential components and optimizations tailored for running containerized workloads, streamlining the container deployment process. Windows Server 2025 provides enhanced performance optimization and improved resource utilization compared to previous versions, allowing for more efficient container operations. It features advanced security capabilities, including better isolation between containers to prevent container escape attacks and stronger kernel boundaries between containers and host. Windows Server 2025 also introduces enhanced networking capabilities like network namespace isolation and better integration with the Host Netwroking Service (HNS) that enable faster container communication and reduced latency. Customers can find and launch Windows ECS instances directly from the Amazon EC2 Console or through API or CLI commands. Windows ECS Optimized AMIs can be run with all available pricing options for windows ECS instances and are enabled across all Public, AWS GovCloud (US) and China Regions of AWS. For more details on getting the best out of AWS EC2 instances running Windows Server 2025 check out the Windows on AWS page and the guide on AWS Windows ECS AMIs.