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.  

AWS Transform for mainframe, previewed as “Amazon Q Developer transformation capabilities for mainframe” at re:Invent 2024, is now generally available. AWS Transform is the first agentic AI service for modernizing mainframe applications at scale—accelerating modernization of IBM z/OS applications from years to months.

Powered by a specialized AI agent leveraging 19 years of AWS experience, AWS Transform streamlines the entire transformation process—from initial analysis and planning to code documentation and refactoring—helping organizations to modernize faster, reduce risk and cost, and achieve better outcomes in the cloud.

This release introduces significant new capabilities. Enhanced analysis features help teams identify cyclomatic complexity, homonyms, and duplicate IDs across codebases, with new export and import functions for file classification and in-UI file viewing and comparison. Documentation generation now supports larger codebases with improved performance and recovery capabilities, including an AI-powered chat experience for querying generated documentation. 

Teams can use improved decomposition features to manage dependencies and domain creation, while new deployment templates streamline environment setup for modernized applications. The service also introduces flexible job management, allowing teams to modify objectives and focus on specific transformation steps during reruns. 

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

To learn more, read the blog post, register for the upcoming launch webinar, or get started in the AWS Transform web experience.

 

​AWS Transform for mainframe, previewed as “Amazon Q Developer transformation capabilities for mainframe” at re:Invent 2024, is now generally available. AWS Transform is the first agentic AI service for modernizing mainframe applications at scale—accelerating modernization of IBM z/OS applications from years to months.
Powered by a specialized AI agent leveraging 19 years of AWS experience, AWS Transform streamlines the entire transformation process—from initial analysis and planning to code documentation and refactoring—helping organizations to modernize faster, reduce risk and cost, and achieve better outcomes in the cloud.
This release introduces significant new capabilities. Enhanced analysis features help teams identify cyclomatic complexity, homonyms, and duplicate IDs across codebases, with new export and import functions for file classification and in-UI file viewing and comparison. Documentation generation now supports larger codebases with improved performance and recovery capabilities, including an AI-powered chat experience for querying generated documentation. 
Teams can use improved decomposition features to manage dependencies and domain creation, while new deployment templates streamline environment setup for modernized applications. The service also introduces flexible job management, allowing teams to modify objectives and focus on specific transformation steps during reruns. 
AWS Transform for mainframe is available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).
To learn more, read the blog post, register for the upcoming launch webinar, or get started in the AWS Transform web experience.  

At re:Invent 2024, AWS introduced the preview of Amazon Q Developer transformation capabilities for VMware. That innovation has evolved into AWS Transform for VMware—a first-of-its-kind agentic AI service that’s now generally available. Powered by large language models, graph neural networks, and the deep experience of AWS in enterprise workload migrations, AWS Transform simplifies VMware modernization at scale. Customers and partners can now move faster, reduce migration risk, and modernize with confidence.

VMware environments have long been foundational to enterprise IT, but rising costs and vendor uncertainty are prompting organizations to rethink their strategies. Despite the urgency, VMware workload migration has historically been slow and error-prone. AWS Transform changes that. With agentic AI, AWS Transform automates the full modernization lifecycle—from discovery and dependency mapping to network translation and Amazon Elastic Compute Cloud (Amazon EC2) optimization. Certain tasks that once took weeks can now be completed in minutes. In testing, AWS generated migration wave plans for 500 VMs in just 15 minutes and performed networking translations up to 80x faster than traditional methods. Partners in pilot programs have cut execution times by up to 90%. 

Beyond speed, AWS Transform delivers precision and transparency. A shared workspace brings together infrastructure teams, app owners, partners, and AWS experts to resolve blockers and maintain alignment. Built-in human-in-the-loop controls confirm all artifacts are validated before execution. As enterprises aim to break free from legacy constraints and tap into the value of their data, AWS Transform offers a streamlined path to modern, cloud-native architectures. Customers can seamlessly integrate with 200+ AWS services—including analytics, serverless, and generative AI—to accelerate innovation and reduce long-term costs.

Start your VMware modernization journey with AWS Transform. Read the launch blog, explore the documentation, register for the launch webinar, or check out the interactive demo.
 

 

​At re:Invent 2024, AWS introduced the preview of Amazon Q Developer transformation capabilities for VMware. That innovation has evolved into AWS Transform for VMware—a first-of-its-kind agentic AI service that’s now generally available. Powered by large language models, graph neural networks, and the deep experience of AWS in enterprise workload migrations, AWS Transform simplifies VMware modernization at scale. Customers and partners can now move faster, reduce migration risk, and modernize with confidence.
VMware environments have long been foundational to enterprise IT, but rising costs and vendor uncertainty are prompting organizations to rethink their strategies. Despite the urgency, VMware workload migration has historically been slow and error-prone. AWS Transform changes that. With agentic AI, AWS Transform automates the full modernization lifecycle—from discovery and dependency mapping to network translation and Amazon Elastic Compute Cloud (Amazon EC2) optimization. Certain tasks that once took weeks can now be completed in minutes. In testing, AWS generated migration wave plans for 500 VMs in just 15 minutes and performed networking translations up to 80x faster than traditional methods. Partners in pilot programs have cut execution times by up to 90%. 
Beyond speed, AWS Transform delivers precision and transparency. A shared workspace brings together infrastructure teams, app owners, partners, and AWS experts to resolve blockers and maintain alignment. Built-in human-in-the-loop controls confirm all artifacts are validated before execution. As enterprises aim to break free from legacy constraints and tap into the value of their data, AWS Transform offers a streamlined path to modern, cloud-native architectures. Customers can seamlessly integrate with 200+ AWS services—including analytics, serverless, and generative AI—to accelerate innovation and reduce long-term costs.
Start your VMware modernization journey with AWS Transform. Read the launch blog, explore the documentation, register for the launch webinar, or check out the interactive demo.    

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