We are pleased to announce that the memory allocation per OpenSearch Compute Unit (OCU) for Amazon OpenSearch Ingestion has been increased from 8GB to 15GB. One OCU now comes default with 2vCPU and 15GB of memory, allowing customers to leverage greater in-memory processing for their data ingestion pipelines without modifying existing configurations.

With the increased memory per OCU, Amazon OpenSearch Ingestion is better equipped to handle memory-intensive processing tasks such as trace analytics, aggregations, and enrichment operations. Customers can now build more complex and high-throughput ingestion pipelines with reduced risk of out-of-memory failures.

The increased memory for OCUs are now available in all AWS Regions where Amazon OpenSearch Ingestion is currently offered at no additional cost. You can take advantage of these improvements by updating your existing pipelines or creating new pipelines through the Amazon OpenSearch Service console or APIs at no additional cost.

To learn more, see the Amazon OpenSearch Ingestion webpage and the Amazon OpenSearch Service Developer Guide.

 

​We are pleased to announce that the memory allocation per OpenSearch Compute Unit (OCU) for Amazon OpenSearch Ingestion has been increased from 8GB to 15GB. One OCU now comes default with 2vCPU and 15GB of memory, allowing customers to leverage greater in-memory processing for their data ingestion pipelines without modifying existing configurations. With the increased memory per OCU, Amazon OpenSearch Ingestion is better equipped to handle memory-intensive processing tasks such as trace analytics, aggregations, and enrichment operations. Customers can now build more complex and high-throughput ingestion pipelines with reduced risk of out-of-memory failures. The increased memory for OCUs are now available in all AWS Regions where Amazon OpenSearch Ingestion is currently offered at no additional cost. You can take advantage of these improvements by updating your existing pipelines or creating new pipelines through the Amazon OpenSearch Service console or APIs at no additional cost. To learn more, see the Amazon OpenSearch Ingestion webpage and the Amazon OpenSearch Service Developer Guide.  

Amazon Web Services announces the availability of AlwaysOn running mode for WorkSpaces Pools, designed for customers who want their streaming to start right away. With AlwaysOn mode, users will have their virtual desktop session provisioned in seconds, allowing them to be productive immediately. Customers can now choose between AlwaysOn running mode and the currently available AutoStop mode, which only bills an hourly usage fee when a customer logs into their session. With AutoStop, streaming starts after a short amount of start-up time, but customers can better optimize on cost for unused instances.

Amazon WorkSpaces Pools enables customers to reduce costs by sharing a pool of virtual desktops across a group of users who get a fresh desktop every time they log in. With application settings being saved in a central storage repository, simplified management via a single console and set of clients, the ability to support Microsoft 365 Apps for enterprise, and the new running mode options, WorkSpaces Pools offer the flexibility customers expect.

AlwaysOn for WorkSpaces Pools is now available in all regions where WorkSpaces Pools is supported. For pricing information, visit Amazon WorkSpaces Pricing. To learn more about AlwaysOn for WorkSpaces Pools and to get started, view the documentation here.

 

​Amazon Web Services announces the availability of AlwaysOn running mode for WorkSpaces Pools, designed for customers who want their streaming to start right away. With AlwaysOn mode, users will have their virtual desktop session provisioned in seconds, allowing them to be productive immediately. Customers can now choose between AlwaysOn running mode and the currently available AutoStop mode, which only bills an hourly usage fee when a customer logs into their session. With AutoStop, streaming starts after a short amount of start-up time, but customers can better optimize on cost for unused instances. Amazon WorkSpaces Pools enables customers to reduce costs by sharing a pool of virtual desktops across a group of users who get a fresh desktop every time they log in. With application settings being saved in a central storage repository, simplified management via a single console and set of clients, the ability to support Microsoft 365 Apps for enterprise, and the new running mode options, WorkSpaces Pools offer the flexibility customers expect. AlwaysOn for WorkSpaces Pools is now available in all regions where WorkSpaces Pools is supported. For pricing information, visit Amazon WorkSpaces Pricing. To learn more about AlwaysOn for WorkSpaces Pools and to get started, view the documentation here.  

Today, Simple Email Service (SES) Mail Manager announces the addition of a Debug logging level for Mail Manager traffic policies. This new logging level provides more detailed visibility on incoming connections to a customer’s Mail Manager ingress endpoint and makes it easier to troubleshoot delivery challenges quickly, using familiar event destinations such as Cloudwatch, Kinesis, and S3.

With Debug level logs, customers can now log every possible evaluation and action within a Mail Manager traffic policy, along with envelope data for the email message being evaluated for traffic permission. This enables customers to determine whether their traffic policy is working as expected or to isolate incoming message parameters which are not covered by the current configuration. When used in conjunction with rules engine logging, debug logging for traffic policies charts a full picture of message arrival into Mail Manager and its disposition by the rules engine. Debug logging for traffic policies is intended to be used during active troubleshooting but otherwise left disabled, as its output can be verbose for high-volume Mail Manager instances. While SES does not charge an additional fee for this logging feature, customers may incur costs from their chosen event destination.

Debug logging for traffic policies is available in all 17 AWS non-opt-in Regions within the AWS commercial partition. To learn more about Mail Manager logging options, see the SES Mail Manager Logging Guide.

 

​Today, Simple Email Service (SES) Mail Manager announces the addition of a Debug logging level for Mail Manager traffic policies. This new logging level provides more detailed visibility on incoming connections to a customer’s Mail Manager ingress endpoint and makes it easier to troubleshoot delivery challenges quickly, using familiar event destinations such as Cloudwatch, Kinesis, and S3. With Debug level logs, customers can now log every possible evaluation and action within a Mail Manager traffic policy, along with envelope data for the email message being evaluated for traffic permission. This enables customers to determine whether their traffic policy is working as expected or to isolate incoming message parameters which are not covered by the current configuration. When used in conjunction with rules engine logging, debug logging for traffic policies charts a full picture of message arrival into Mail Manager and its disposition by the rules engine. Debug logging for traffic policies is intended to be used during active troubleshooting but otherwise left disabled, as its output can be verbose for high-volume Mail Manager instances. While SES does not charge an additional fee for this logging feature, customers may incur costs from their chosen event destination. Debug logging for traffic policies is available in all 17 AWS non-opt-in Regions within the AWS commercial partition. To learn more about Mail Manager logging options, see the SES Mail Manager Logging Guide.  

AWS Parallel Computing Service (PCS) now supports Slurm version 24.11 with support for managed accounting. Using this feature, you can enable accounting on your PCS clusters to monitor cluster usage, enforce resource limits, and manage fine-grained access control to specific queues or compute node groups. PCS manages the accounting database for your cluster, eliminating the need for you to setup and manage a separate accounting database.

You can enable this feature on your PCS cluster in just a few clicks using the AWS Management Console. Visit our getting started and accounting documentation pages to learn more about accounting and see release notes to learn more about Slurm 24.11.

AWS Parallel Computing Service (AWS PCS) is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. To learn more about PCS, refer to the service documentation. For pricing details and region availability, see the PCS Pricing Page and AWS Region Table.

 

​AWS Parallel Computing Service (PCS) now supports Slurm version 24.11 with support for managed accounting. Using this feature, you can enable accounting on your PCS clusters to monitor cluster usage, enforce resource limits, and manage fine-grained access control to specific queues or compute node groups. PCS manages the accounting database for your cluster, eliminating the need for you to setup and manage a separate accounting database. You can enable this feature on your PCS cluster in just a few clicks using the AWS Management Console. Visit our getting started and accounting documentation pages to learn more about accounting and see release notes to learn more about Slurm 24.11. AWS Parallel Computing Service (AWS PCS) is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. To learn more about PCS, refer to the service documentation. For pricing details and region availability, see the PCS Pricing Page and AWS Region Table.  

Amazon RDS for PostgreSQL 18 Beta 1 is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 18 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 18 Beta 1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database.

PostgreSQL 18 includes significant updates to query execution and I/O operations. Query execution is enhanced with «skip scan» support for multicolumn B-tree indexes and optimized WHERE clause handling for OR and IN (…) conditions. Parallel execution capabilities are expanded through parallel GIN index builds and enhanced join operations. Observability improvements include detailed buffer access statistics in EXPLAIN ANALYZE and enhanced I/O utilization monitoring capabilities. Please refer to the PostgreSQL community announcement for more details.

Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the preview environment.

Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.
 

 

​Amazon RDS for PostgreSQL 18 Beta 1 is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 18 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 18 Beta 1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database. PostgreSQL 18 includes significant updates to query execution and I/O operations. Query execution is enhanced with «skip scan» support for multicolumn B-tree indexes and optimized WHERE clause handling for OR and IN (…) conditions. Parallel execution capabilities are expanded through parallel GIN index builds and enhanced join operations. Observability improvements include detailed buffer access statistics in EXPLAIN ANALYZE and enhanced I/O utilization monitoring capabilities. Please refer to the PostgreSQL community announcement for more details. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the preview environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.    

Today, AWS Glue Studio announces support for additional compressed file types, Excel files (as source), and XML and Tableau’s Hyper files (as target). We are also introducing the option to select the number of output files for an S3 target. These enhancements will allow you to use visual ETL jobs for additional data processing workflows not supported today, for example loading data from an Excel file into a single XML file output.

The new experience will now enable you to have one single file as the output of your Glue job, or to specify a custom number for the output files. Further, Glue now supports Excel files via S3 file source nodes, and XML or Tableau Hyper files for S3 file target nodes. New compression types that will be available to use are: LZ4 , SNAPPY, DEFLATE, LZO, BROTLI, ZSTD and ZLIB.

These new features are now available in all AWS commercial Regions and AWS GovCloud (US) Regions where AWS Glue is available. Access the AWS Regional Services List for the most up-to-date availability information.

To learn more, visit the AWS Glue documentation.
 

 

​Today, AWS Glue Studio announces support for additional compressed file types, Excel files (as source), and XML and Tableau’s Hyper files (as target). We are also introducing the option to select the number of output files for an S3 target. These enhancements will allow you to use visual ETL jobs for additional data processing workflows not supported today, for example loading data from an Excel file into a single XML file output. The new experience will now enable you to have one single file as the output of your Glue job, or to specify a custom number for the output files. Further, Glue now supports Excel files via S3 file source nodes, and XML or Tableau Hyper files for S3 file target nodes. New compression types that will be available to use are: LZ4 , SNAPPY, DEFLATE, LZO, BROTLI, ZSTD and ZLIB. These new features are now available in all AWS commercial Regions and AWS GovCloud (US) Regions where AWS Glue is available. Access the AWS Regional Services List for the most up-to-date availability information. To learn more, visit the AWS Glue documentation.    

AWS CodeBuild now supports remote Docker image build servers, allowing you to speed up image build requests. You can provision a fully managed Docker server that maintains a persistent cache across builds. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages.

Centralized image building increases efficiency by reusing cached layers and reducing provisioning plus network transfer latency. CodeBuild automatically configures your build environment to use the remote server when running Docker commands. The Docker server is then readily available to run parallel build requests that can each use the shared layer cache, reducing the overall build latency and optimizing build speed.

This feature is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page.

Get started with CodeBuild’s blog post for setting up a Docker image builder in your CodeBuild project, or visit our documentation. To learn how to get started with CodeBuild, visit the AWS CodeBuild product page.

 

​AWS CodeBuild now supports remote Docker image build servers, allowing you to speed up image build requests. You can provision a fully managed Docker server that maintains a persistent cache across builds. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages. Centralized image building increases efficiency by reusing cached layers and reducing provisioning plus network transfer latency. CodeBuild automatically configures your build environment to use the remote server when running Docker commands. The Docker server is then readily available to run parallel build requests that can each use the shared layer cache, reducing the overall build latency and optimizing build speed. This feature is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page. Get started with CodeBuild’s blog post for setting up a Docker image builder in your CodeBuild project, or visit our documentation. To learn how to get started with CodeBuild, visit the AWS CodeBuild product page.  

Amazon SageMaker Catalog integrates with Amazon S3 Tables, making it easy to discover, share, and govern S3 Tables for users to access and query the data with all Apache Iceberg–compatible tools and engines. With Amazon SageMaker Catalog, built on Amazon DataZone, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or just ask Amazon Q Developer with natural language to find your data.

S3 Tables deliver the first cloud object store with built-in Apache Iceberg support. Data publishers can onboard S3 tables to SageMaker Lakehouse and enhance their discoverability by adding them to the SageMaker Catalog. Publishers have the flexibility to either directly publish tables or enrich them with valuable business metadata, making it easier for all users to understand and find the data they need. On the consumption side, users can search for relevant tables, request access through a subscription workflow (subject to publisher approval), and leverage this data for advanced analytics and AI development projects. This end-to-end workflow significantly improves data accessibility, governance, and utilization of S3 Tables across the organization.

SageMaker Catalog with S3 Tables support is available in all AWS Regions where Amazon SageMaker is available.

To learn more, visit Amazon SageMaker. Get started with S3 Tables and publish using user documentation.
 

 

​Amazon SageMaker Catalog integrates with Amazon S3 Tables, making it easy to discover, share, and govern S3 Tables for users to access and query the data with all Apache Iceberg–compatible tools and engines. With Amazon SageMaker Catalog, built on Amazon DataZone, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or just ask Amazon Q Developer with natural language to find your data. S3 Tables deliver the first cloud object store with built-in Apache Iceberg support. Data publishers can onboard S3 tables to SageMaker Lakehouse and enhance their discoverability by adding them to the SageMaker Catalog. Publishers have the flexibility to either directly publish tables or enrich them with valuable business metadata, making it easier for all users to understand and find the data they need. On the consumption side, users can search for relevant tables, request access through a subscription workflow (subject to publisher approval), and leverage this data for advanced analytics and AI development projects. This end-to-end workflow significantly improves data accessibility, governance, and utilization of S3 Tables across the organization. SageMaker Catalog with S3 Tables support is available in all AWS Regions where Amazon SageMaker is available. To learn more, visit Amazon SageMaker. Get started with S3 Tables and publish using user documentation.    

Today, AWS announces the general availability of migration assessment capabilities in AWS Transform. Migration assessment in AWS Transform analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Simply upload your infrastructure data and AWS Transform will deliver a comprehensive analysis that typically takes weeks in just minutes.

Powered by agentic AI, AWS Transform removes weeks of manual analysis by providing instant visibility into your infrastructure and automatically discovering cost optimization opportunities. AWS Transform produces a business case including key highlights from your server inventory, a summary of current infrastructure, multiple TCO scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included), and tenancy options.

AWS Transform for migration assessments is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).

Ready to get started? Visit the AWS Transform web experience or read our blog post to learn more.

 

​Today, AWS announces the general availability of migration assessment capabilities in AWS Transform. Migration assessment in AWS Transform analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Simply upload your infrastructure data and AWS Transform will deliver a comprehensive analysis that typically takes weeks in just minutes.
Powered by agentic AI, AWS Transform removes weeks of manual analysis by providing instant visibility into your infrastructure and automatically discovering cost optimization opportunities. AWS Transform produces a business case including key highlights from your server inventory, a summary of current infrastructure, multiple TCO scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included), and tenancy options.
AWS Transform for migration assessments is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).
Ready to get started? Visit the AWS Transform web experience or read our blog post to learn more.  

AWS Transform for .NET, previewed as “Amazon Q Developer transformation capabilities for .NET porting,” is now generally available. As the first agentic AI service for modernizing .NET applications at scale, AWS Transform helps you to modernize Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. It supports transforming a wide range of .NET project types including MVC, WCF, Web API, class libraries, console apps, and unit test projects.

The agentic transformation begins with a code assessment of your repositories from GitHub, GitLab, or Bitbucket. It identifies .NET versions, project types, and interproject dependencies and generates a tailored modernization plan. You can customize and prioritize the transformation sequence based on your business objectives or architectural complexity before initiating the AI-powered modernization process. Once started, AWS Transform for .NET automatically converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository. It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through the AWS Transform dashboards or interactive chat and receive email notifications with links to transformed .NET code. For workloads that need further human input, your developers can continue refinement using the Visual Studio extension in AWS Transform. The scalable experience of AWS Transform enables consistent modernization across a large application portfolio while moving to cross-platform .NET, unlocking performance, portability, and long-term maintainability.

AWS Transform for .NET is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt). 

To learn more, read the blog, visit the webpage, or review the documentation.

 

​AWS Transform for .NET, previewed as “Amazon Q Developer transformation capabilities for .NET porting,” is now generally available. As the first agentic AI service for modernizing .NET applications at scale, AWS Transform helps you to modernize Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. It supports transforming a wide range of .NET project types including MVC, WCF, Web API, class libraries, console apps, and unit test projects.
The agentic transformation begins with a code assessment of your repositories from GitHub, GitLab, or Bitbucket. It identifies .NET versions, project types, and interproject dependencies and generates a tailored modernization plan. You can customize and prioritize the transformation sequence based on your business objectives or architectural complexity before initiating the AI-powered modernization process. Once started, AWS Transform for .NET automatically converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository. It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through the AWS Transform dashboards or interactive chat and receive email notifications with links to transformed .NET code. For workloads that need further human input, your developers can continue refinement using the Visual Studio extension in AWS Transform. The scalable experience of AWS Transform enables consistent modernization across a large application portfolio while moving to cross-platform .NET, unlocking performance, portability, and long-term maintainability.
AWS Transform for .NET is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt). 
To learn more, read the blog, visit the webpage, or review the documentation.