Today, AWS announces the addition of AWS Local Zones to the Region selector in the AWS Management Console, providing a unified experience across AWS global infrastructure. AWS Local Zones now appear alongside AWS Regions in the Console’s top navigation, making it easier for customers to quickly navigate to the Console page for managing their resources in AWS Local Zones.

Now, when customers select the Local Zones tab in the Region selector, they will see all opted-in AWS Local Zones in one place. Clicking on an AWS Local Zone takes customers directly to its parent Region’s Console page to view and manage resources. This capability streamlines navigation for customers operating across multiple AWS Local Zones parented to different AWS Regions.

This capability is available across all AWS Local Zones in public AWS Regions. To get started, navigate to the Region selector in the AWS Management Console. For more information, see the AWS Local Zones documentation.

 

​Today, AWS announces the addition of AWS Local Zones to the Region selector in the AWS Management Console, providing a unified experience across AWS global infrastructure. AWS Local Zones now appear alongside AWS Regions in the Console’s top navigation, making it easier for customers to quickly navigate to the Console page for managing their resources in AWS Local Zones.
Now, when customers select the Local Zones tab in the Region selector, they will see all opted-in AWS Local Zones in one place. Clicking on an AWS Local Zone takes customers directly to its parent Region’s Console page to view and manage resources. This capability streamlines navigation for customers operating across multiple AWS Local Zones parented to different AWS Regions.
This capability is available across all AWS Local Zones in public AWS Regions. To get started, navigate to the Region selector in the AWS Management Console. For more information, see the AWS Local Zones documentation.  

AWS Glue zero-ETL integrations are now available in the Asia Pacific (Mumbai) region. With this expansion, customers in the Asia Pacific (Mumbai) region can now use zero-ETL integrations to simplify their data pipelines, reduce data movement latency, and accelerate time-to-insight for analytics and machine learning workloads.

Zero-ETL integrations offer a set of fully managed integrations by AWS that minimizes the need to build ETL data pipelines for common ingestion and replication use cases. You can use zero-ETL to replicate data from sources such as Amazon DynamoDB, Oracle Database@AWS, self-managed databases (Oracle, SQL Server, MySQL or PostgreSQL), and supported SaaS applications including Salesforce, SAP, Zendesk, and Zoho CRM directly into target analytics data stores without writing or maintaining ETL pipelines. It automatically handles schema mapping, change data capture, and incremental data replication, eliminating the need to build and manage complex data pipelines by yourself. This allows your data engineering teams to focus on deriving value from data rather than managing infrastructure, while replicating data in your target data stores in near real-time.

To learn more, visit the AWS Glue documentation. 

 

​AWS Glue zero-ETL integrations are now available in the Asia Pacific (Mumbai) region. With this expansion, customers in the Asia Pacific (Mumbai) region can now use zero-ETL integrations to simplify their data pipelines, reduce data movement latency, and accelerate time-to-insight for analytics and machine learning workloads. Zero-ETL integrations offer a set of fully managed integrations by AWS that minimizes the need to build ETL data pipelines for common ingestion and replication use cases. You can use zero-ETL to replicate data from sources such as Amazon DynamoDB, Oracle Database@AWS, self-managed databases (Oracle, SQL Server, MySQL or PostgreSQL), and supported SaaS applications including Salesforce, SAP, Zendesk, and Zoho CRM directly into target analytics data stores without writing or maintaining ETL pipelines. It automatically handles schema mapping, change data capture, and incremental data replication, eliminating the need to build and manage complex data pipelines by yourself. This allows your data engineering teams to focus on deriving value from data rather than managing infrastructure, while replicating data in your target data stores in near real-time. To learn more, visit the AWS Glue documentation.   

Amazon Lightsail content delivery network (CDN) distributions now support IPv6-only instances as origins. This feature enables customers to use IPv6-only instances to deliver content through the Lightsail CDN distributions with low latency and high transfer speeds worldwide. With this launch, customers can run their websites and applications on cost-effective IPv6-only instances while seamlessly serving content to all end users, including those on networks that don’t yet support IPv6 connectivity.

Previously, only IPv4 and dual-stack instances were supported as origins for Lightsail CDN distributions. With this launch, customers can also use IPv6-only instances as origins for their Lightsail CDN distributions, making applications running on those instances accessible to all end users, regardless of whether end users have IPv6 connectivity. Lightsail CDN distributions support multiple origin types including instances, containers, buckets, and load balancers.

Amazon Lightsail is available in 16 AWS Regions including US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (London), Asia Pacific (Tokyo), and Asia Pacific (Malaysia). To get started, visit the Lightsail console. For pricing and other details, visit the Amazon Lightsail pricing.

 

​Amazon Lightsail content delivery network (CDN) distributions now support IPv6-only instances as origins. This feature enables customers to use IPv6-only instances to deliver content through the Lightsail CDN distributions with low latency and high transfer speeds worldwide. With this launch, customers can run their websites and applications on cost-effective IPv6-only instances while seamlessly serving content to all end users, including those on networks that don’t yet support IPv6 connectivity. Previously, only IPv4 and dual-stack instances were supported as origins for Lightsail CDN distributions. With this launch, customers can also use IPv6-only instances as origins for their Lightsail CDN distributions, making applications running on those instances accessible to all end users, regardless of whether end users have IPv6 connectivity. Lightsail CDN distributions support multiple origin types including instances, containers, buckets, and load balancers.
Amazon Lightsail is available in 16 AWS Regions including US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (London), Asia Pacific (Tokyo), and Asia Pacific (Malaysia). To get started, visit the Lightsail console. For pricing and other details, visit the Amazon Lightsail pricing.  

Today, we are announcing that Amazon Elastic VMware Service (Amazon EVS) now supports up to 32 ESXi hosts per environment, double the previous limit of 16 hosts.

Amazon EVS gives you flexibility in how you configure VMware Cloud Foundation (VCF) domains and clusters within an environment. You can put all your hosts into a single large cluster, spread them across several smaller clusters, or any combination that fits your needs. With this release, you can now submit a service quota increase to scale up to a total of 32 hosts and reduce the operational overhead of managing multiple environments.

This latest release is available in all regions where Amazon EVS is offered.

For more details on the steps and procedure, visit the Amazon EVS product detail page and user guide.

 

​Today, we are announcing that Amazon Elastic VMware Service (Amazon EVS) now supports up to 32 ESXi hosts per environment, double the previous limit of 16 hosts.
Amazon EVS gives you flexibility in how you configure VMware Cloud Foundation (VCF) domains and clusters within an environment. You can put all your hosts into a single large cluster, spread them across several smaller clusters, or any combination that fits your needs. With this release, you can now submit a service quota increase to scale up to a total of 32 hosts and reduce the operational overhead of managing multiple environments.
This latest release is available in all regions where Amazon EVS is offered.
For more details on the steps and procedure, visit the Amazon EVS product detail page and user guide.  

Amazon Redshift now supports writing directly to Apache Iceberg tables via the AWS Glue Data Catalog (awsdatacatalog) mount and ALTER TABLE DDL statements to modify the schema, partitioning, and properties of Apache Iceberg tables. With write access through the auto-mounted awsdatacatalog, you can land Redshift transformations in your data lake for any engine to query without creating external schemas—particularly useful for Iceberg tables federated with AWS Lake Formation. Supported ALTER TABLE operations include ADD/DROP/ALTER columns, RENAME COLUMN, SET TABLE PROPERTIES to overwrite the default compression type, and ADD/DROP/REPLACE PARTITION FIELD to adapt partitioning strategies as data volumes grow. Previously, updating the structure of Iceberg tables required deleting the table and its data, adding complexity and latency to data pipelines. Tables modified by Redshift remain compatible with other Iceberg-compatible engines, including Amazon EMR and Amazon Athena, preserving cross-engine interoperability. AWS Lake Formation permissions are supported for Iceberg write operations.

These capabilities are available in all AWS Regions where Amazon Redshift is available. To get started, visit the Referencing Iceberg tables in Amazon Redshift and Altering table definitions sections in the Amazon Redshift Database Developer Guide.

 

​Amazon Redshift now supports writing directly to Apache Iceberg tables via the AWS Glue Data Catalog (awsdatacatalog) mount and ALTER TABLE DDL statements to modify the schema, partitioning, and properties of Apache Iceberg tables. With write access through the auto-mounted awsdatacatalog, you can land Redshift transformations in your data lake for any engine to query without creating external schemas—particularly useful for Iceberg tables federated with AWS Lake Formation. Supported ALTER TABLE operations include ADD/DROP/ALTER columns, RENAME COLUMN, SET TABLE PROPERTIES to overwrite the default compression type, and ADD/DROP/REPLACE PARTITION FIELD to adapt partitioning strategies as data volumes grow. Previously, updating the structure of Iceberg tables required deleting the table and its data, adding complexity and latency to data pipelines. Tables modified by Redshift remain compatible with other Iceberg-compatible engines, including Amazon EMR and Amazon Athena, preserving cross-engine interoperability. AWS Lake Formation permissions are supported for Iceberg write operations. These capabilities are available in all AWS Regions where Amazon Redshift is available. To get started, visit the Referencing Iceberg tables in Amazon Redshift and Altering table definitions sections in the Amazon Redshift Database Developer Guide.  

Amazon SageMaker Studio IDEs, including JupyterLab and Code Editor, now support GPU capacity reservations through SageMaker Flexible Training Plans (FTP), giving you predictable access to high-demand, high-performance computational resources within your budget. By leveraging FTP, you can achieve up to 65% cost savings compared to On-Demand instances while running ML workflows in JupyterLab or Code Editor.

FTP provides a fully self-serve procurement experience. To get started, navigate to the SageMaker FTP console and select your preferred instance type, reservation length, and start date for your Studio IDE workload. Review your order, complete the purchase, and wait for the plan to become active. When creating a Studio app from the SageMaker Studio UI, select your purchased plan from the Instance dropdown. SageMaker provisions the instance automatically with no infrastructure management required on your part. As your plan nears expiration, the IDE proactively notifies you, giving you time to save your work before the reservation ends.

To learn more about using FTP capacity reservation capability with Studio IDEs, see Using Training Plans with Studio IDEs.
To learn about launching JupyterLab and Code Editor applications in SageMaker Studio, see Studio Spaces documentation.

 

 

​Amazon SageMaker Studio IDEs, including JupyterLab and Code Editor, now support GPU capacity reservations through SageMaker Flexible Training Plans (FTP), giving you predictable access to high-demand, high-performance computational resources within your budget. By leveraging FTP, you can achieve up to 65% cost savings compared to On-Demand instances while running ML workflows in JupyterLab or Code Editor. FTP provides a fully self-serve procurement experience. To get started, navigate to the SageMaker FTP console and select your preferred instance type, reservation length, and start date for your Studio IDE workload. Review your order, complete the purchase, and wait for the plan to become active. When creating a Studio app from the SageMaker Studio UI, select your purchased plan from the Instance dropdown. SageMaker provisions the instance automatically with no infrastructure management required on your part. As your plan nears expiration, the IDE proactively notifies you, giving you time to save your work before the reservation ends.
To learn more about using FTP capacity reservation capability with Studio IDEs, see Using Training Plans with Studio IDEs. To learn about launching JupyterLab and Code Editor applications in SageMaker Studio, see Studio Spaces documentation.
   

AWS SAM CLI now supports AWS CloudFormation Language Extensions, enabling you to reduce duplication in your infrastructure as code (IaC) templates while retaining the full local development workflow. This accelerates your serverless development by letting you define resources once and iterate locally without waiting for cloud deployments.

Developers frequently need to define multiple similar resources, such as Lambda functions, DynamoDB tables, or SNS topics, from a single template definition. However, developers who use SAM CLI to build, test, and deploy their serverless applications previously could not process templates that use CloudFormation Language Extensions. This required choosing between reducing template duplication and using SAM CLI for local development. Now, SAM CLI processes Language Extensions in memory for local operations while preserving your original template for CloudFormation deployment. You can define your resources once and test them locally across all SAM CLI commands, catching errors like invalid syntax or missing dependencies before deploying. This shortens your iteration cycles and reduces time spent debugging failed deployments in the cloud.

To get started, download or update SAM CLI to the latest version. Add the AWS::LanguageExtensions transform to your SAM template and use Fn::ForEach to generate multiple resources from a single definition. SAM CLI commands including sam build, sam local invoke, sam sync, sam local start-api, and sam validate will automatically expand your loops and process each generated resource. You can invoke expanded functions by name, for example sam local invoke AlphaFunction. SAM CLI also supports Fn::Length, Fn::ToJsonString, Fn::FindInMap with DefaultValue, and conditional DeletionPolicy and UpdateReplacePolicy attributes.

To learn more, visit the SAM CLI developer guide and launch blog post.

 

​AWS SAM CLI now supports AWS CloudFormation Language Extensions, enabling you to reduce duplication in your infrastructure as code (IaC) templates while retaining the full local development workflow. This accelerates your serverless development by letting you define resources once and iterate locally without waiting for cloud deployments.
Developers frequently need to define multiple similar resources, such as Lambda functions, DynamoDB tables, or SNS topics, from a single template definition. However, developers who use SAM CLI to build, test, and deploy their serverless applications previously could not process templates that use CloudFormation Language Extensions. This required choosing between reducing template duplication and using SAM CLI for local development. Now, SAM CLI processes Language Extensions in memory for local operations while preserving your original template for CloudFormation deployment. You can define your resources once and test them locally across all SAM CLI commands, catching errors like invalid syntax or missing dependencies before deploying. This shortens your iteration cycles and reduces time spent debugging failed deployments in the cloud.
To get started, download or update SAM CLI to the latest version. Add the AWS::LanguageExtensions transform to your SAM template and use Fn::ForEach to generate multiple resources from a single definition. SAM CLI commands including sam build, sam local invoke, sam sync, sam local start-api, and sam validate will automatically expand your loops and process each generated resource. You can invoke expanded functions by name, for example sam local invoke AlphaFunction. SAM CLI also supports Fn::Length, Fn::ToJsonString, Fn::FindInMap with DefaultValue, and conditional DeletionPolicy and UpdateReplacePolicy attributes.
To learn more, visit the SAM CLI developer guide and launch blog post.  

Today, AWS announces that the AWS Partner Central agents now accelerate opportunity creation through natural language conversation. AWS Partner Central agents, released on March 16, 2026, are AI-powered capabilities built on Amazon Bedrock AgentCore that help partners surface pipeline insights, advance deals with next-step recommendations, and identify funding opportunities. With this update, partners create opportunities through a short conversation instead of completing a multi-step form, so partner sales teams spend less time on data entry and more time selling.

Partners describe a deal in natural language, upload meeting notes, proposals, or call transcripts (PDF, DOCX, Excel, TXT), or clone an existing opportunity. The agent extracts the information, enriches customer details, and recommends improvements — such as adding missing context, correcting field values, or strengthening the business problem statement — so partners submit higher-quality opportunities, improve pipeline hygiene, and shorten sales cycles. Partners use the feature in the AWS Console through Amazon Q chat, and programmatically through Model Context Protocol (MCP), so sales teams create opportunities from their existing tools.

AWS Partner Central agents are available in all commercial AWS Regions. To learn more about agentic capabilities in AWS Partner Central, review this blog. Partners can start using agents by visiting AWS Partner Central in the AWS console and accessing opportunities, after reviewing the agents guide, and to integrate agents into your existing tools, visit the Partner Central agents MCP server guide.

 

​Today, AWS announces that the AWS Partner Central agents now accelerate opportunity creation through natural language conversation. AWS Partner Central agents, released on March 16, 2026, are AI-powered capabilities built on Amazon Bedrock AgentCore that help partners surface pipeline insights, advance deals with next-step recommendations, and identify funding opportunities. With this update, partners create opportunities through a short conversation instead of completing a multi-step form, so partner sales teams spend less time on data entry and more time selling. Partners describe a deal in natural language, upload meeting notes, proposals, or call transcripts (PDF, DOCX, Excel, TXT), or clone an existing opportunity. The agent extracts the information, enriches customer details, and recommends improvements — such as adding missing context, correcting field values, or strengthening the business problem statement — so partners submit higher-quality opportunities, improve pipeline hygiene, and shorten sales cycles. Partners use the feature in the AWS Console through Amazon Q chat, and programmatically through Model Context Protocol (MCP), so sales teams create opportunities from their existing tools. AWS Partner Central agents are available in all commercial AWS Regions. To learn more about agentic capabilities in AWS Partner Central, review this blog. Partners can start using agents by visiting AWS Partner Central in the AWS console and accessing opportunities, after reviewing the agents guide, and to integrate agents into your existing tools, visit the Partner Central agents MCP server guide.  

Amazon CloudWatch Logs now supports retrieving up to 100,000 results using the Logs Insights query language. Customers can specify the limit in their query using the LIMIT command.

Previously, customers were limited to 10,000 results and had to split their queries into smaller time ranges to retrieve all results. With this launch, customers can view a larger set of results and use existing features such as patterns, visualization, and export on the full 100,000 result set. The GetQueryResults API has also been updated to support pagination; each invocation can return up to 10,000 results along with a token that can be used to fetch the next set of results.

The increased query result limits are available in all commercial AWS regions. You can execute queries and view up to 100,000 results using the Amazon CloudWatch console, AWS CLI, AWS CDK, and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.

 

​Amazon CloudWatch Logs now supports retrieving up to 100,000 results using the Logs Insights query language. Customers can specify the limit in their query using the LIMIT command. Previously, customers were limited to 10,000 results and had to split their queries into smaller time ranges to retrieve all results. With this launch, customers can view a larger set of results and use existing features such as patterns, visualization, and export on the full 100,000 result set. The GetQueryResults API has also been updated to support pagination; each invocation can return up to 10,000 results along with a token that can be used to fetch the next set of results. The increased query result limits are available in all commercial AWS regions. You can execute queries and view up to 100,000 results using the Amazon CloudWatch console, AWS CLI, AWS CDK, and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.  

Amazon EMR Serverless is now generally available in six additional AWS Regions – Asia Pacific (Hyderabad), Asia Pacific (Malaysia), Asia Pacific (New Zealand), Asia Pacific (Taipei), Asia Pacific (Thailand), and Mexico (Central).

Amazon EMR Serverless is a deployment option in Amazon EMR that makes it simple and cost effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. With EMR Serverless, you can run your Apache Spark and Apache Hive applications without having to configure, optimize, tune, or manage clusters. EMR Serverless offers fine-grained automatic scaling, fast launch times, customizable worker configurations, and support for batch, interactive and streaming workloads.

To get started, visit the Amazon EMR Serverless User Guide. For pricing info, visit the EMR Serverless pricing page.

 

​Amazon EMR Serverless is now generally available in six additional AWS Regions – Asia Pacific (Hyderabad), Asia Pacific (Malaysia), Asia Pacific (New Zealand), Asia Pacific (Taipei), Asia Pacific (Thailand), and Mexico (Central). Amazon EMR Serverless is a deployment option in Amazon EMR that makes it simple and cost effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. With EMR Serverless, you can run your Apache Spark and Apache Hive applications without having to configure, optimize, tune, or manage clusters. EMR Serverless offers fine-grained automatic scaling, fast launch times, customizable worker configurations, and support for batch, interactive and streaming workloads. To get started, visit the Amazon EMR Serverless User Guide. For pricing info, visit the EMR Serverless pricing page.