Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Multi-AZ deployment for SQL Server Web Edition. SQL Server Web Edition is specifically designed to support public and internet-accessible web pages, websites, web applications, and web services, and is used by web hosters and web value-added providers (VAPs). These applications need high availability, and automated failover to recover from hardware and database failures. Now customers can use SQL Server Web Edition with Amazon RDS Multi-AZ deployment option, which provides a high availability solution. The new feature eliminates the need for customers to use more expensive options for high availability, such as using SQL Server Standard Edition or Enterprise Edition.

To use the feature, customers simply configure their Amazon RDS for SQL Server Web Edition instance with Multi-AZ deployment option. Amazon RDS automatically provisions and maintains a standby replica in a different Availability Zone (AZ), and synchronously replicates data across the two AZs. In situations where your Multi-AZ primary database becomes unavailable, Amazon RDS automatically fails over to the standby replica, so customers can resume database operations quickly and without any administrative intervention.

For more information about Multi-AZ deployment for RDS SQL Server Web Edition, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.

 

​Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Multi-AZ deployment for SQL Server Web Edition. SQL Server Web Edition is specifically designed to support public and internet-accessible web pages, websites, web applications, and web services, and is used by web hosters and web value-added providers (VAPs). These applications need high availability, and automated failover to recover from hardware and database failures. Now customers can use SQL Server Web Edition with Amazon RDS Multi-AZ deployment option, which provides a high availability solution. The new feature eliminates the need for customers to use more expensive options for high availability, such as using SQL Server Standard Edition or Enterprise Edition. To use the feature, customers simply configure their Amazon RDS for SQL Server Web Edition instance with Multi-AZ deployment option. Amazon RDS automatically provisions and maintains a standby replica in a different Availability Zone (AZ), and synchronously replicates data across the two AZs. In situations where your Multi-AZ primary database becomes unavailable, Amazon RDS automatically fails over to the standby replica, so customers can resume database operations quickly and without any administrative intervention. For more information about Multi-AZ deployment for RDS SQL Server Web Edition, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.  

AWS announces the general availability of Cloud WAN Routing Policy providing customers fine-grained controls to optimize route management, control traffic patterns, and customize network behavior across their global wide-area networks.

AWS Cloud WAN allows you to build, monitor, and manage a unified global network that interconnects your resources in the AWS cloud and your on-premises environments. Using the new Routing Policy feature, customers can perform advanced routing techniques such as route filtering and summarization to have better control on routes exchanged between AWS Cloud WAN and external networks. This feature enables customers to build controlled routing environments to minimize route reachability blast radius, prevent sub-optimal or asymmetric connectivity patterns, and avoid over-running of route-tables due to propagation of unnecessary routes in global networks. In addition, this feature allows customers to set advanced Border Gateway Protocol (BGP) attributes to customize network traffic behavior per their individual needs and build highly resilient hybrid-cloud network architectures. This feature also provides advanced visibility in the routing databases to allow rapid troubleshooting of network issues in complex multi-path environments.

The new Routing Policy feature is available in all AWS Regions where AWS Cloud WAN is available. You can enable these features using the AWS Management Console, AWS Command Line Interface (CLI) and the AWS Software Development Kit (SDK). There is no additional charge for enabling Routing Policy on AWS Cloud WAN. For more information, see the AWS Cloud WAN documentation pages.

 

​AWS announces the general availability of Cloud WAN Routing Policy providing customers fine-grained controls to optimize route management, control traffic patterns, and customize network behavior across their global wide-area networks. AWS Cloud WAN allows you to build, monitor, and manage a unified global network that interconnects your resources in the AWS cloud and your on-premises environments. Using the new Routing Policy feature, customers can perform advanced routing techniques such as route filtering and summarization to have better control on routes exchanged between AWS Cloud WAN and external networks. This feature enables customers to build controlled routing environments to minimize route reachability blast radius, prevent sub-optimal or asymmetric connectivity patterns, and avoid over-running of route-tables due to propagation of unnecessary routes in global networks. In addition, this feature allows customers to set advanced Border Gateway Protocol (BGP) attributes to customize network traffic behavior per their individual needs and build highly resilient hybrid-cloud network architectures. This feature also provides advanced visibility in the routing databases to allow rapid troubleshooting of network issues in complex multi-path environments. The new Routing Policy feature is available in all AWS Regions where AWS Cloud WAN is available. You can enable these features using the AWS Management Console, AWS Command Line Interface (CLI) and the AWS Software Development Kit (SDK). There is no additional charge for enabling Routing Policy on AWS Cloud WAN. For more information, see the AWS Cloud WAN documentation pages.  

Amazon MQ now supports RabbitMQ version 4.2 which introduces native support for the AMQP 1.0 protocol, a new Raft based metadata store named Khepri, local shovels, and message priorities for quorum queues. RabbitMQ 4.2 also includes various bug fixes and performance improvements for throughput and memory management.

A key highlight of RabbitMQ 4.2 is the support of AMQP 1.0 as a core protocol offering enhanced features like modified outcome which allow consumers to modify message annotations before requeueing or dead lettering, and granular flow control, which offers benefits including letting a client application dynamically adjust how many messages it wants to receive from a specific queue. Amazon MQ has also introduced configurable resource limits for RabbitMQ 4.2 brokers which you can modify based on your application requirements. Starting from RabbitMQ 4.0, mirroring of classic queues is no longer supported. Non-replicated classic queues are still supported. Quorum queues are the only replicated and durable queue type supported on RabbitMQ 4.2 brokers, and now offer message priorities in addition to consumer priorities.

To start using RabbitMQ 4.2 on Amazon MQ, simply select RabbitMQ 4.2 when creating a new broker using the m7g instance type through the AWS Management console, AWS CLI, or AWS SDKs. Amazon MQ automatically manages patch version upgrades for your RabbitMQ 4.2 brokers, so you need to only specify the major.minor version. To learn more about the changes in RabbitMQ 4.2, see the Amazon MQ release notes and the Amazon MQ developer guide. This version is available in all regions where Amazon MQ m7g type instances are available today. 

 

​Amazon MQ now supports RabbitMQ version 4.2 which introduces native support for the AMQP 1.0 protocol, a new Raft based metadata store named Khepri, local shovels, and message priorities for quorum queues. RabbitMQ 4.2 also includes various bug fixes and performance improvements for throughput and memory management. A key highlight of RabbitMQ 4.2 is the support of AMQP 1.0 as a core protocol offering enhanced features like modified outcome which allow consumers to modify message annotations before requeueing or dead lettering, and granular flow control, which offers benefits including letting a client application dynamically adjust how many messages it wants to receive from a specific queue. Amazon MQ has also introduced configurable resource limits for RabbitMQ 4.2 brokers which you can modify based on your application requirements. Starting from RabbitMQ 4.0, mirroring of classic queues is no longer supported. Non-replicated classic queues are still supported. Quorum queues are the only replicated and durable queue type supported on RabbitMQ 4.2 brokers, and now offer message priorities in addition to consumer priorities. To start using RabbitMQ 4.2 on Amazon MQ, simply select RabbitMQ 4.2 when creating a new broker using the m7g instance type through the AWS Management console, AWS CLI, or AWS SDKs. Amazon MQ automatically manages patch version upgrades for your RabbitMQ 4.2 brokers, so you need to only specify the major.minor version. To learn more about the changes in RabbitMQ 4.2, see the Amazon MQ release notes and the Amazon MQ developer guide. This version is available in all regions where Amazon MQ m7g type instances are available today.   

You can now connect your Apache Kafka applications to Amazon MSK Serverless in the South America (São Paulo) AWS Regions.

Amazon MSK is a fully managed service for Apache Kafka and Kafka Connect that makes it easier for you to build and run applications that use Apache Kafka as a data store. Amazon MSK Serverless is a cluster type for Amazon MSK that allows you to run Apache Kafka without having to manage and scale cluster capacity. MSK Serverless automatically provisions and scales compute and storage resources, so you can use Apache Kafka on demand.

With these launches, Amazon MSK Serverless is now generally available in Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Seoul), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), Europe (Paris), Europe (London), South America (São Paulo), US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS regions. To learn more and get started, see our developer guide.

 

​You can now connect your Apache Kafka applications to Amazon MSK Serverless in the South America (São Paulo) AWS Regions. Amazon MSK is a fully managed service for Apache Kafka and Kafka Connect that makes it easier for you to build and run applications that use Apache Kafka as a data store. Amazon MSK Serverless is a cluster type for Amazon MSK that allows you to run Apache Kafka without having to manage and scale cluster capacity. MSK Serverless automatically provisions and scales compute and storage resources, so you can use Apache Kafka on demand. With these launches, Amazon MSK Serverless is now generally available in Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Seoul), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), Europe (Paris), Europe (London), South America (São Paulo), US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS regions. To learn more and get started, see our developer guide.  

AWS Database Migration Service (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we’re excited to announce that Schema Conversion now supports conversions from SAP Adaptive Server Enterprise (ASE) database (formerly known as Sybase) to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL, powered by Generative AI capability.

Using Schema Conversion, you can automatically convert database objects from your SAP (Sybase) ASE source to an to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL target. The integrated generative AI capability intelligently handles complex code conversions that typically require manual effort, such as stored procedures, functions, and triggers. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively.

To learn more about this feature, see the documentation for using SAP (Sybase) ASE as a source for AWS DMS Schema Conversion and using SAP (Sybase) ASE as a source for AWS DMS for data migration. For details about the generative AI capability, please refer to the User Guide. For AWS DMS Schema Conversion regional availability, please refer to the Supported AWS Regions page.

 

​AWS Database Migration Service (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we’re excited to announce that Schema Conversion now supports conversions from SAP Adaptive Server Enterprise (ASE) database (formerly known as Sybase) to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL, powered by Generative AI capability. Using Schema Conversion, you can automatically convert database objects from your SAP (Sybase) ASE source to an to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL target. The integrated generative AI capability intelligently handles complex code conversions that typically require manual effort, such as stored procedures, functions, and triggers. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively. To learn more about this feature, see the documentation for using SAP (Sybase) ASE as a source for AWS DMS Schema Conversion and using SAP (Sybase) ASE as a source for AWS DMS for data migration. For details about the generative AI capability, please refer to the User Guide. For AWS DMS Schema Conversion regional availability, please refer to the Supported AWS Regions page.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in the Asia Pacific (Melbourne) Region. C7i instances are supported by custom Intel processors, available only on AWS, and offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers.

C7i instances deliver up to 15% better price-performance versus C6i instances and are a great choice for all compute-intensive workloads, such as batch processing, distributed analytics, ad-serving, and video encoding. C7i instances offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads.

C7i instances support new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. Customers can attach up to 128 EBS volumes to a C7i instance vs. up to 28 EBS volumes to a C6i instance. This allows processing of larger amounts of data, scale workloads, and improved performance over C6i instances.

To learn more, visit Amazon EC2 C7i Instances. To get started, see the AWS Management Console.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in the Asia Pacific (Melbourne) Region. C7i instances are supported by custom Intel processors, available only on AWS, and offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. C7i instances deliver up to 15% better price-performance versus C6i instances and are a great choice for all compute-intensive workloads, such as batch processing, distributed analytics, ad-serving, and video encoding. C7i instances offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads. C7i instances support new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. Customers can attach up to 128 EBS volumes to a C7i instance vs. up to 28 EBS volumes to a C6i instance. This allows processing of larger amounts of data, scale workloads, and improved performance over C6i instances. To learn more, visit Amazon EC2 C7i Instances. To get started, see the AWS Management Console.  

Amazon Braket now supports spending limits, enabling customers to set spending caps on quantum processing units (QPUs) to manage costs. With spending limits, customers can define maximum spending thresholds on a per-device basis, and Amazon Braket automatically validates each task submission doesn’t exceed the pre-configured limits. Tasks that would exceed remaining budgets are rejected before creation. For comprehensive cost management across all of Amazon Web Services, customers should continue to use the AWS Budgets feature as part of AWS Cost Management.

Spending limits are particularly valuable for research institutions managing quantum computing budgets across multiple users, for educational environments preventing accidental overspending during coursework, and for development teams experimenting with quantum algorithms. Customers can update or delete spending limits at any time as their requirements change. Spending limits apply only to on-demand tasks on quantum processing units and do not include costs for simulators, notebook instances, hybrid jobs, or tasks created during Braket Direct reservations.

Spending limits are available now in all AWS Regions where Amazon Braket is supported at no additional cost. Researchers at accredited institutions can apply for credits to support experiments on Amazon Braket through the AWS Cloud Credits for Research program. To get started, visit the Spending limits page in the Amazon Braket console and read our launch blog post.

 

​Amazon Braket now supports spending limits, enabling customers to set spending caps on quantum processing units (QPUs) to manage costs. With spending limits, customers can define maximum spending thresholds on a per-device basis, and Amazon Braket automatically validates each task submission doesn’t exceed the pre-configured limits. Tasks that would exceed remaining budgets are rejected before creation. For comprehensive cost management across all of Amazon Web Services, customers should continue to use the AWS Budgets feature as part of AWS Cost Management. Spending limits are particularly valuable for research institutions managing quantum computing budgets across multiple users, for educational environments preventing accidental overspending during coursework, and for development teams experimenting with quantum algorithms. Customers can update or delete spending limits at any time as their requirements change. Spending limits apply only to on-demand tasks on quantum processing units and do not include costs for simulators, notebook instances, hybrid jobs, or tasks created during Braket Direct reservations. Spending limits are available now in all AWS Regions where Amazon Braket is supported at no additional cost. Researchers at accredited institutions can apply for credits to support experiments on Amazon Braket through the AWS Cloud Credits for Research program. To get started, visit the Spending limits page in the Amazon Braket console and read our launch blog post.  

AWS Glue now supports full snapshot and incremental load ingestion for new SAP entities using zero-ETL integrations. This enhancement introduces full snapshot data ingestion for SAP entities that lack complete change data capture (CDC) functionality, while also providing incremental data loading capabilities for SAP entities that don’t support the Operational Data Provisioning (ODP) framework. These new features work alongside existing capabilities for ODP-supported SAP entities, to give customers the flexibility to implement zero-ETL data ingestion strategies across diverse SAP environments.

Fully managed AWS zero-ETL integrations eliminate the engineering overhead associated with building custom ETL data pipelines. This new zero-ETL functionality enables organizations to ingest data from multiple SAP applications into Amazon Redshift or the lakehouse architecture of Amazon SageMaker to address scenarios where SAP entities lack deletion tracking flags or don’t support the Operational Data Provisioning (ODP) framework. Through full snapshot ingestion for entities without deletion tracking and timestamp-based incremental loading for non-ODP systems, zero-ETL integrations reduce operational complexity while saving organizations weeks of engineering effort that would otherwise be required to design, build, and test custom data pipelines across diverse SAP application environments.

This feature is available in all AWS Regions where AWS Glue zero-ETL is currently available.

To get started with the enhanced zero-ETL coverage for SAP sources refer to the AWS Glue zero-ETL user guide.

 

​AWS Glue now supports full snapshot and incremental load ingestion for new SAP entities using zero-ETL integrations. This enhancement introduces full snapshot data ingestion for SAP entities that lack complete change data capture (CDC) functionality, while also providing incremental data loading capabilities for SAP entities that don’t support the Operational Data Provisioning (ODP) framework. These new features work alongside existing capabilities for ODP-supported SAP entities, to give customers the flexibility to implement zero-ETL data ingestion strategies across diverse SAP environments. Fully managed AWS zero-ETL integrations eliminate the engineering overhead associated with building custom ETL data pipelines. This new zero-ETL functionality enables organizations to ingest data from multiple SAP applications into Amazon Redshift or the lakehouse architecture of Amazon SageMaker to address scenarios where SAP entities lack deletion tracking flags or don’t support the Operational Data Provisioning (ODP) framework. Through full snapshot ingestion for entities without deletion tracking and timestamp-based incremental loading for non-ODP systems, zero-ETL integrations reduce operational complexity while saving organizations weeks of engineering effort that would otherwise be required to design, build, and test custom data pipelines across diverse SAP application environments. This feature is available in all AWS Regions where AWS Glue zero-ETL is currently available. To get started with the enhanced zero-ETL coverage for SAP sources refer to the AWS Glue zero-ETL user guide.  

Starting today, customers can run Apple macOS Tahoe (version 26) as Amazon Machine Images (AMIs) on Amazon EC2 Mac instances. Apple macOS Tahoe is the latest major macOS version, and introduces multiple new features and performance improvements over prior macOS versions including running Xcode version 26.0 or later (which includes the latest SDKs for iOS, iPadOS, macOS, tvOS, watchOS, and visionOS).

Backed by Amazon Elastic Block Store (EBS), EC2 macOS AMIs are AWS-supported images that are designed to provide a stable, secure, and high-performance environment for developer workloads running on EC2 Mac instances. EC2 macOS AMIs include the AWS Command Line Interface, Command Line Tools for Xcode, Amazon SSM Agent, and Homebrew. The AWS Homebrew Tap includes the latest versions of AWS packages included in the AMIs.

Apple macOS Tahoe AMIs are available for Apple silicon EC2 Mac instances and are published to all AWS regions where Apple silicon EC2 Mac instances are available today. Customers can get started with macOS Tahoe AMIs via the AWS Console, Command Line Interface (CLI), or API. Learn more about EC2 Mac instances here or get started with an EC2 Mac instance here. You can also subscribe to EC2 macOS AMI release notifications here.

 

​Starting today, customers can run Apple macOS Tahoe (version 26) as Amazon Machine Images (AMIs) on Amazon EC2 Mac instances. Apple macOS Tahoe is the latest major macOS version, and introduces multiple new features and performance improvements over prior macOS versions including running Xcode version 26.0 or later (which includes the latest SDKs for iOS, iPadOS, macOS, tvOS, watchOS, and visionOS). Backed by Amazon Elastic Block Store (EBS), EC2 macOS AMIs are AWS-supported images that are designed to provide a stable, secure, and high-performance environment for developer workloads running on EC2 Mac instances. EC2 macOS AMIs include the AWS Command Line Interface, Command Line Tools for Xcode, Amazon SSM Agent, and Homebrew. The AWS Homebrew Tap includes the latest versions of AWS packages included in the AMIs. Apple macOS Tahoe AMIs are available for Apple silicon EC2 Mac instances and are published to all AWS regions where Apple silicon EC2 Mac instances are available today. Customers can get started with macOS Tahoe AMIs via the AWS Console, Command Line Interface (CLI), or API. Learn more about EC2 Mac instances here or get started with an EC2 Mac instance here. You can also subscribe to EC2 macOS AMI release notifications here.  

Amazon SageMaker Unified Studio now supports long-running sessions with corporate identities through AWS IAM Identity Center’s trusted identity propagation (TIP) capability. This feature enables data scientists, data engineers, and analytics professionals to achieve uninterrupted workflow continuity and improved productivity. Users can now initiate interactive notebooks from Amazon SageMaker Unified Studio and data processing sessions on Amazon EMR (EC2, EKS, Serverless) and AWS Glue that continue running in the background using their corporate credentials, even when they log off or their session expires.

With this capability, you can now launch resource-intensive complex data processing sessions, or exploratory analytics flows and step away from your workstations without interrupting progress. Sessions automatically maintain corporate identity permissions through IAM Identity Center’s trusted identity propagation, ensuring consistent security and access controls throughout execution. You can start multi-hour or multi-day workflows knowing the jobs will persist through network disconnections, laptop shutdowns, or credential refresh cycles, with sessions running for up to 90 days (default 7 days). This eliminates the productivity bottleneck of monitoring long-running processes and enables more efficient resource utilization across data teams.

Long running sessions are available in Amazon SageMaker Unified Studio in all existing SageMaker Unified Studio regions. To learn more about user background sessions, see Amazon EMR on EC2, Amazon EMR Serverless, AWS Glue and Amazon EMR on EKS documentation.

 

​Amazon SageMaker Unified Studio now supports long-running sessions with corporate identities through AWS IAM Identity Center’s trusted identity propagation (TIP) capability. This feature enables data scientists, data engineers, and analytics professionals to achieve uninterrupted workflow continuity and improved productivity. Users can now initiate interactive notebooks from Amazon SageMaker Unified Studio and data processing sessions on Amazon EMR (EC2, EKS, Serverless) and AWS Glue that continue running in the background using their corporate credentials, even when they log off or their session expires. With this capability, you can now launch resource-intensive complex data processing sessions, or exploratory analytics flows and step away from your workstations without interrupting progress. Sessions automatically maintain corporate identity permissions through IAM Identity Center’s trusted identity propagation, ensuring consistent security and access controls throughout execution. You can start multi-hour or multi-day workflows knowing the jobs will persist through network disconnections, laptop shutdowns, or credential refresh cycles, with sessions running for up to 90 days (default 7 days). This eliminates the productivity bottleneck of monitoring long-running processes and enables more efficient resource utilization across data teams. Long running sessions are available in Amazon SageMaker Unified Studio in all existing SageMaker Unified Studio regions. To learn more about user background sessions, see Amazon EMR on EC2, Amazon EMR Serverless, AWS Glue and Amazon EMR on EKS documentation.