Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports enhanced search capabilities with exact-match and partial-match functionality for technical identifiers such as column and table names. This feature allows users to perform precise searches by enclosing terms within a qualifier such as double quotes (» «), helping them quickly locate assets with exact or partial technical names. For example, analysts can find specific columns faster, stewards can validate assets using naming patterns like «audit_», and engineers can identify temporary tables with prefixes like «temp_».

Building on SageMaker Catalog’s existing keyword and semantic search, this enhancement is designed for organizations managing large-scale data catalogs with complex naming conventions. For example, searching for «customer_id» returns only those assets with an exact match, while a query like «sales_» returns assets such as sales_summary and sales_data_2024. These capabilities help users quickly locate technical assets, improve data governance by reducing errors, and enhance collaboration.

Check out the product documentation to learn more about how to set up metadata rules for subscription and publishing workflows.

 

​Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker, now supports enhanced search capabilities with exact-match and partial-match functionality for technical identifiers such as column and table names. This feature allows users to perform precise searches by enclosing terms within a qualifier such as double quotes (» «), helping them quickly locate assets with exact or partial technical names. For example, analysts can find specific columns faster, stewards can validate assets using naming patterns like «audit_», and engineers can identify temporary tables with prefixes like «temp_».
Building on SageMaker Catalog’s existing keyword and semantic search, this enhancement is designed for organizations managing large-scale data catalogs with complex naming conventions. For example, searching for «customer_id» returns only those assets with an exact match, while a query like «sales_» returns assets such as sales_summary and sales_data_2024. These capabilities help users quickly locate technical assets, improve data governance by reducing errors, and enhance collaboration. Check out the product documentation to learn more about how to set up metadata rules for subscription and publishing workflows.  

Anthropic’s Claude 3.7 Sonnet hybrid reasoning model, their most intelligent model to date, is now available through cross-region inference on Bedrock in Europe (Ireland), Europe (Paris), Europe (Frankfurt), and Europe (Stockholm). Claude 3.7 Sonnet represents a significant advancement in AI capabilities, offering both quick responses and extended, step-by-step thinking made visible to the user. This new model includes strong improvements in coding and brings enhanced performance across various tasks, like instruction following, math, and physics.

Claude 3.7 Sonnet introduces a unique approach to AI reasoning by integrating it seamlessly with other capabilities. Unlike traditional models that separate quick responses from those requiring deeper thought, Claude 3.7 Sonnet allows users to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. In extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks. Amazon Bedrock customers can adjust how long the model thinks, offering a flexible trade-off between speed and answer quality. Additionally, users can control the reasoning budget by specifying a token limit, enabling more precise cost management.

Claude 3.7 Sonnet is also available on Amazon Bedrock in the US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information, see the AWS News Blog and Claude in Amazon Bedrock.

 

​Anthropic’s Claude 3.7 Sonnet hybrid reasoning model, their most intelligent model to date, is now available through cross-region inference on Bedrock in Europe (Ireland), Europe (Paris), Europe (Frankfurt), and Europe (Stockholm). Claude 3.7 Sonnet represents a significant advancement in AI capabilities, offering both quick responses and extended, step-by-step thinking made visible to the user. This new model includes strong improvements in coding and brings enhanced performance across various tasks, like instruction following, math, and physics. Claude 3.7 Sonnet introduces a unique approach to AI reasoning by integrating it seamlessly with other capabilities. Unlike traditional models that separate quick responses from those requiring deeper thought, Claude 3.7 Sonnet allows users to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. In extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks. Amazon Bedrock customers can adjust how long the model thinks, offering a flexible trade-off between speed and answer quality. Additionally, users can control the reasoning budget by specifying a token limit, enabling more precise cost management. Claude 3.7 Sonnet is also available on Amazon Bedrock in the US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information, see the AWS News Blog and Claude in Amazon Bedrock.  

Cost Optimization Hub now supports DynamoDB and MemoryDB reservation recommendations. You can filter and aggregate recommendations across both services, making it easier to identify the highest cost-saving opportunities for DynamoDB and MemoryDB.

With this launch, you can view, consolidate, and prioritize reservation recommendations for DynamoDB and MemoryDB across your organization’s member accounts and AWS Regions through a single dashboard. Cost Optimization Hub’s comprehensive view enables you to see these recommendations as part of your total potential savings, helping you compare and prioritize them alongside other cost-saving opportunities.

DynamoDB and MemoryDB reservation recommendations are now available in Cost Optimization Hub across all AWS Regions where Cost Optimization Hub is supported.
 

 

​Cost Optimization Hub now supports DynamoDB and MemoryDB reservation recommendations. You can filter and aggregate recommendations across both services, making it easier to identify the highest cost-saving opportunities for DynamoDB and MemoryDB. With this launch, you can view, consolidate, and prioritize reservation recommendations for DynamoDB and MemoryDB across your organization’s member accounts and AWS Regions through a single dashboard. Cost Optimization Hub’s comprehensive view enables you to see these recommendations as part of your total potential savings, helping you compare and prioritize them alongside other cost-saving opportunities. DynamoDB and MemoryDB reservation recommendations are now available in Cost Optimization Hub across all AWS Regions where Cost Optimization Hub is supported.    

Amazon Relational Database Service (Amazon RDS) for Oracle now supports R6id and M6id instances. These instances offer up to 7.6 TB of NVMe-based local storage, making them well-suited for database workloads that require access to large amounts of intermediate data beyond the instance’s memory capacity. Customers can configure their Oracle database to use the local storage for temporary tablespace and Database Smart Flash Cache.

Operations such as sorts, hash joins, and aggregations can generate large amounts of intermediate data that doesn’t fit in memory and is stored in temporary tablespace. With R6id and M6id, Customers can place temporary tablespaces in the local storage instead of the Amazon EBS volume attached to their instance to reduce latency, improve throughput, and lower the provisioned IOPS.

Customers with Oracle Enterprise Edition license can configure Database Smart Flash Cache to use the local storage. When configured, Smart Flash Cache will use the local storage to keep frequently accessed data that doesn’t fit in memory and improve the read performance of the database.

You can launch the new instance in the Amazon RDS Management Console or using the AWS CLI. Refer Amazon RDS for Oracle Pricing for available instance configurations, pricing details, and region availability.

 

​Amazon Relational Database Service (Amazon RDS) for Oracle now supports R6id and M6id instances. These instances offer up to 7.6 TB of NVMe-based local storage, making them well-suited for database workloads that require access to large amounts of intermediate data beyond the instance’s memory capacity. Customers can configure their Oracle database to use the local storage for temporary tablespace and Database Smart Flash Cache. Operations such as sorts, hash joins, and aggregations can generate large amounts of intermediate data that doesn’t fit in memory and is stored in temporary tablespace. With R6id and M6id, Customers can place temporary tablespaces in the local storage instead of the Amazon EBS volume attached to their instance to reduce latency, improve throughput, and lower the provisioned IOPS. Customers with Oracle Enterprise Edition license can configure Database Smart Flash Cache to use the local storage. When configured, Smart Flash Cache will use the local storage to keep frequently accessed data that doesn’t fit in memory and improve the read performance of the database. You can launch the new instance in the Amazon RDS Management Console or using the AWS CLI. Refer Amazon RDS for Oracle Pricing for available instance configurations, pricing details, and region availability.  

We are excited to announce that Amazon SageMaker Studio now supports recovery mode, enabling users to regain access to their JupyterLab and Code Editor applications when configuration issues prevent normal startup.

Starting today, when users encounter application startup failures due to issues such as corrupted Conda configuration or insufficient storage space, they can launch their application in recovery mode on Studio UI or using AWS CLI. When configuration issues occur, users see a warning banner with the recommended solution and can choose to run their space in recovery mode. This simplified environment provides access to essential features like terminal and file explorer, allowing users to diagnose and fix configuration issues without administrator intervention. This functionality provides users with an important self-service mechanism, helping them minimize workspace downtime.

This feature is available in all AWS Regions where Amazon SageMaker Studio is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, visit our documentation.

 

​We are excited to announce that Amazon SageMaker Studio now supports recovery mode, enabling users to regain access to their JupyterLab and Code Editor applications when configuration issues prevent normal startup. Starting today, when users encounter application startup failures due to issues such as corrupted Conda configuration or insufficient storage space, they can launch their application in recovery mode on Studio UI or using AWS CLI. When configuration issues occur, users see a warning banner with the recommended solution and can choose to run their space in recovery mode. This simplified environment provides access to essential features like terminal and file explorer, allowing users to diagnose and fix configuration issues without administrator intervention. This functionality provides users with an important self-service mechanism, helping them minimize workspace downtime. This feature is available in all AWS Regions where Amazon SageMaker Studio is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, visit our documentation.  

Amazon Connect now provides the ability to dynamically set the text-to-speech (TTS) voices, language, or speaking styles used in voice bots and interactive voice response (IVR). These new capabilities enable you to deliver a more personalized experience for each of your end customers. For example, you can have a desired voice configured dynamically based on the primary speaking language set in their customer profile. These new capabilities are configurable in the “Set Voice” block in Amazon Connect flows and can be configured in the drag-and-drop flow designer or public APIs.

To learn more, see the public documentation on Set Voice block and the Amazon Connect Administrator Guide. These features are available in all AWS regions where Amazon Connect is available. To learn more about Amazon Connect, the AWS contact center as a service solution on the cloud, please visit the Amazon Connect website.
 

 

​Amazon Connect now provides the ability to dynamically set the text-to-speech (TTS) voices, language, or speaking styles used in voice bots and interactive voice response (IVR). These new capabilities enable you to deliver a more personalized experience for each of your end customers. For example, you can have a desired voice configured dynamically based on the primary speaking language set in their customer profile. These new capabilities are configurable in the “Set Voice” block in Amazon Connect flows and can be configured in the drag-and-drop flow designer or public APIs. To learn more, see the public documentation on Set Voice block and the Amazon Connect Administrator Guide. These features are available in all AWS regions where Amazon Connect is available. To learn more about Amazon Connect, the AWS contact center as a service solution on the cloud, please visit the Amazon Connect website.    

Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL versions 16.8, 15.12, 14.17 and 13.20. Please note, this release supports the versions released by the PostgreSQL community on February 20,2025 which replaces the previous February 13, 2025 release. These releases contain product improvements and bug fixes made by the PostgreSQL community, along with Aurora-specific security and feature improvements such as dynamic resizing of the allocated space for Optimized Reads-enabled temporary objects on Aurora I/O-Optimized clusters and new features for Babelfish. For more details, please refer to the release notes.

These releases are now available in all commercial AWS regions and AWS GovCloud (US) Regions, except China regions. You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more. For a full feature parity list across regions, head to our feature parity page.

Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.
 

 

​Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL versions 16.8, 15.12, 14.17 and 13.20. Please note, this release supports the versions released by the PostgreSQL community on February 20,2025 which replaces the previous February 13, 2025 release. These releases contain product improvements and bug fixes made by the PostgreSQL community, along with Aurora-specific security and feature improvements such as dynamic resizing of the allocated space for Optimized Reads-enabled temporary objects on Aurora I/O-Optimized clusters and new features for Babelfish. For more details, please refer to the release notes. These releases are now available in all commercial AWS regions and AWS GovCloud (US) Regions, except China regions. You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more. For a full feature parity list across regions, head to our feature parity page. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.    

Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports Graviton3-based M7g instances for both Standard brokers and Express brokers for MSK Provisioned clusters in Middle East (UAE) AWS region.

Graviton M7G instances for Standard brokers deliver up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances. When you use Graviton instance on Express brokers, you can realize even more benefits with up to 3x more throughput per broker, scale up to 20x faster, and reduce recovery time by 90% compared to standard Apache Kafka brokers.

To learn more, check out our blogs on MSK Express brokers and M7G based Standard brokers. To get started, visit the Amazon MSK console.

 

​Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports Graviton3-based M7g instances for both Standard brokers and Express brokers for MSK Provisioned clusters in Middle East (UAE) AWS region. Graviton M7G instances for Standard brokers deliver up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances. When you use Graviton instance on Express brokers, you can realize even more benefits with up to 3x more throughput per broker, scale up to 20x faster, and reduce recovery time by 90% compared to standard Apache Kafka brokers. To learn more, check out our blogs on MSK Express brokers and M7G based Standard brokers. To get started, visit the Amazon MSK console.  

Amazon MQ is now available in two new regions, Asia Pacific (Thailand) and Mexico (Central). With this launch, Amazon MQ is now available in a total of 36 regions.

Amazon MQ is a managed message broker service for open-source Apache ActiveMQ and RabbitMQ that makes it easier to set up and operate message brokers on AWS. Amazon MQ reduces your your operational responsibilities by managing the provisioning, setup, and maintenance of message brokers for you. Because Amazon MQ connects to your current applications with industry-standard APIs and protocols, you can more easily migrate to AWS without having to rewrite code.

For more information, please visit the Amazon MQ product page, and see the AWS Region Table for complete regional availability.

 

​Amazon MQ is now available in two new regions, Asia Pacific (Thailand) and Mexico (Central). With this launch, Amazon MQ is now available in a total of 36 regions. Amazon MQ is a managed message broker service for open-source Apache ActiveMQ and RabbitMQ that makes it easier to set up and operate message brokers on AWS. Amazon MQ reduces your your operational responsibilities by managing the provisioning, setup, and maintenance of message brokers for you. Because Amazon MQ connects to your current applications with industry-standard APIs and protocols, you can more easily migrate to AWS without having to rewrite code. For more information, please visit the Amazon MQ product page, and see the AWS Region Table for complete regional availability.  

Amazon Aurora PostgreSQL-Compatible Edition now supports pgvector 0.8.0, an open-source extension for PostgreSQL for storing vector embeddings in your database. pgvector provides vector similarity search capabilities that enables Aurora use in generative artificial intelligence (AI) semantic search and retrieval-augemented generation (RAG) applications. pgvector 0.8.0 includes improvements to PostgreSQL query planner’s selection of index when filters are present, which can deliver better query performance and improve search result quality.

pgvector 0.8.0 improves data filtering using conditions in WHERE clauses and joins that can improve query performance and usability. Additionally, the iterative index scans help prevent ‘overfiltering’, ensuring generation of sufficient results to satisfy the conditions of a query. If an initial index scan doesn’t satisfy the query conditions, pgvector will continue to search the index until it hits a configurable threshold. pgvector 0.8.0 also has performance improvements for searching and building HNSW indexes.

pgvector 0.8.0 is available in Amazon Aurora clusters running PostgreSQL 16.8, 15.12, 14.17, and 13.20 and higher in all AWS Regions including AWS GovCloud (US) Regions, except China. You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more.

Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.

 

​Amazon Aurora PostgreSQL-Compatible Edition now supports pgvector 0.8.0, an open-source extension for PostgreSQL for storing vector embeddings in your database. pgvector provides vector similarity search capabilities that enables Aurora use in generative artificial intelligence (AI) semantic search and retrieval-augemented generation (RAG) applications. pgvector 0.8.0 includes improvements to PostgreSQL query planner’s selection of index when filters are present, which can deliver better query performance and improve search result quality. pgvector 0.8.0 improves data filtering using conditions in WHERE clauses and joins that can improve query performance and usability. Additionally, the iterative index scans help prevent ‘overfiltering’, ensuring generation of sufficient results to satisfy the conditions of a query. If an initial index scan doesn’t satisfy the query conditions, pgvector will continue to search the index until it hits a configurable threshold. pgvector 0.8.0 also has performance improvements for searching and building HNSW indexes. pgvector 0.8.0 is available in Amazon Aurora clusters running PostgreSQL 16.8, 15.12, 14.17, and 13.20 and higher in all AWS Regions including AWS GovCloud (US) Regions, except China. You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.