Amazon SageMaker HyperPod now supports custom Kubernetes labels and taints, enabling customers to control pod scheduling and integrate seamlessly with existing Kubernetes infrastructure. Customers deploying AI workloads on HyperPod clusters orcehstrated with EKS need precise control over workload placement to prevent expensive GPU resources from being consumed by system pods and non-AI workloads, while ensuring compatibility with custom device plugins such as EFA and NVIDIA GPU operators. Previously, customers had to manually apply labels and taints using kubectl and reapply them after every node replacement, scaling, or patching operation, creating significant operational overhead.

This capability allows you to configure labels and taints at the instance group level through the CreateCluster and UpdateCluster APIs, providing a managed approach to defining and maintaining scheduling policies across the entire node lifecycle. Using the new KubernetesConfig parameter, you can specify up to 50 labels and 50 taints per instance group. Labels enable resource organization and pod targeting through node selectors, while taints repel pods without matching tolerations to protect specialized nodes. For example, you can apply NoSchedule taints to GPU instance groups to ensure only AI training jobs with explicit tolerations consume high-cost compute resources, or add custom labels that enable device plugin pods to schedule correctly. HyperPod automatically applies these configurations during node creation and maintains them across replacement, scaling, and patching operations, eliminating manual intervention and reducing operational overhead.

This feature is available in all AWS Regions where Amazon SageMaker HyperPod is available. To learn more about custom labels and taints, see the user guide.

 

​Amazon SageMaker HyperPod now supports custom Kubernetes labels and taints, enabling customers to control pod scheduling and integrate seamlessly with existing Kubernetes infrastructure. Customers deploying AI workloads on HyperPod clusters orcehstrated with EKS need precise control over workload placement to prevent expensive GPU resources from being consumed by system pods and non-AI workloads, while ensuring compatibility with custom device plugins such as EFA and NVIDIA GPU operators. Previously, customers had to manually apply labels and taints using kubectl and reapply them after every node replacement, scaling, or patching operation, creating significant operational overhead. This capability allows you to configure labels and taints at the instance group level through the CreateCluster and UpdateCluster APIs, providing a managed approach to defining and maintaining scheduling policies across the entire node lifecycle. Using the new KubernetesConfig parameter, you can specify up to 50 labels and 50 taints per instance group. Labels enable resource organization and pod targeting through node selectors, while taints repel pods without matching tolerations to protect specialized nodes. For example, you can apply NoSchedule taints to GPU instance groups to ensure only AI training jobs with explicit tolerations consume high-cost compute resources, or add custom labels that enable device plugin pods to schedule correctly. HyperPod automatically applies these configurations during node creation and maintains them across replacement, scaling, and patching operations, eliminating manual intervention and reducing operational overhead. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is available. To learn more about custom labels and taints, see the user guide.  

Today, AWS announces that AWS Compute Optimizer now supports idle resource recommendations for NAT Gateways. With this new recommendation type, you will be able to identify NAT Gateways that are unused, resulting in cost savings.

With the new unused NAT Gateway recommendation, you will be able to identify NAT Gateways that show no traffic activity over a 32-day analysis period. Compute Optimizer analyzes CloudWatch metrics including active connection count, incoming packets from source, and incoming packets from destination to validate if NAT Gateways are truly unused. To avoid recommending critical backup resources, Compute Optimizer also examines if the NAT Gateway resource is associated in any AWS Route Tables. You can view the total savings potential of these unused NAT Gateways and access detailed utilization metrics to verify unused conditions before taking action.

This new feature is available in all AWS Regions where AWS Compute Optimizer is available except the AWS GovCloud (US) and the China Regions. To learn more about the new feature updates, please visit Compute Optimizer’s product page and user guide.

 

​Today, AWS announces that AWS Compute Optimizer now supports idle resource recommendations for NAT Gateways. With this new recommendation type, you will be able to identify NAT Gateways that are unused, resulting in cost savings. With the new unused NAT Gateway recommendation, you will be able to identify NAT Gateways that show no traffic activity over a 32-day analysis period. Compute Optimizer analyzes CloudWatch metrics including active connection count, incoming packets from source, and incoming packets from destination to validate if NAT Gateways are truly unused. To avoid recommending critical backup resources, Compute Optimizer also examines if the NAT Gateway resource is associated in any AWS Route Tables. You can view the total savings potential of these unused NAT Gateways and access detailed utilization metrics to verify unused conditions before taking action. This new feature is available in all AWS Regions where AWS Compute Optimizer is available except the AWS GovCloud (US) and the China Regions. To learn more about the new feature updates, please visit Compute Optimizer’s product page and user guide.  

AWS Glue 5.1 is now generally available, delivering improved performance, security updates, expanded Apache Iceberg capabilities, and AWS Lake Formation write support for data integration workloads.

AWS Glue is a serverless, scalable data integration service that simplifies discovering, preparing, moving, and integrating data from multiple sources. This release upgrades core engines to Apache Spark 3.5.6, Python 3.11, and Scala 2.12.18, bringing performance and security enhancements. It also updates support for open table format libraries, including Apache Hudi 1.0.2, Apache Iceberg 1.10.0, and Delta Lake 3.3.2.

AWS Glue 5.1 introduces support for Apache Iceberg format version 3.0, adding default column values, deletion vectors for merge-on-read tables, multi-argument transforms, and row lineage tracking. This release also extends AWS Lake Formation fine-grained access control to write operations (both DML and DDL) for Spark DataFrames and Spark SQL. Previously, this capability was limited to read operations only. AWS Glue 5.1 also adds full-table access control in Apache Spark for Apache Hudi and Delta Lake tables, providing more comprehensive security options for your data.

AWS Glue 5.1 is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Stockholm), Europe (Frankfurt), Europe (Spain), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Malaysia), Asia Pacific (Thailand), Asia Pacific (Mumbai), and South America (São Paulo). Visit the AWS Glue documentation for more information.

 

 

​AWS Glue 5.1 is now generally available, delivering improved performance, security updates, expanded Apache Iceberg capabilities, and AWS Lake Formation write support for data integration workloads.
AWS Glue is a serverless, scalable data integration service that simplifies discovering, preparing, moving, and integrating data from multiple sources. This release upgrades core engines to Apache Spark 3.5.6, Python 3.11, and Scala 2.12.18, bringing performance and security enhancements. It also updates support for open table format libraries, including Apache Hudi 1.0.2, Apache Iceberg 1.10.0, and Delta Lake 3.3.2.
AWS Glue 5.1 introduces support for Apache Iceberg format version 3.0, adding default column values, deletion vectors for merge-on-read tables, multi-argument transforms, and row lineage tracking. This release also extends AWS Lake Formation fine-grained access control to write operations (both DML and DDL) for Spark DataFrames and Spark SQL. Previously, this capability was limited to read operations only. AWS Glue 5.1 also adds full-table access control in Apache Spark for Apache Hudi and Delta Lake tables, providing more comprehensive security options for your data.
AWS Glue 5.1 is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Stockholm), Europe (Frankfurt), Europe (Spain), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Malaysia), Asia Pacific (Thailand), Asia Pacific (Mumbai), and South America (São Paulo). Visit the AWS Glue documentation for more information.
   

AWS Glue now supports zero-ETL for self-managed database sources. Using Glue zero-ETL, you can now setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity.

AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users’ operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift.   

AWS Glue zero-ETL for self-managed database sources are available in the following AWS Regions: US East (Ohio), Europe (Stockholm), Europe (Ireland), Europe (Frankfurt),  Canada West (Calgary), US West (Oregon), and Asia Pacific (Seoul) regions. To get started, sign into the AWS Management Console.  For more information visit the AWS Glue page or review the AWS Glue zero-ETL documentation.

 

​AWS Glue now supports zero-ETL for self-managed database sources. Using Glue zero-ETL, you can now setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity. AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users’ operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift.    AWS Glue zero-ETL for self-managed database sources are available in the following AWS Regions: US East (Ohio), Europe (Stockholm), Europe (Ireland), Europe (Frankfurt),  Canada West (Calgary), US West (Oregon), and Asia Pacific (Seoul) regions. To get started, sign into the AWS Management Console.  For more information visit the AWS Glue page or review the AWS Glue zero-ETL documentation.  

Amazon CloudWatch now offers configuring deletion protection on your CloudWatch log groups, helping customers safeguard their critical logging data from accidental or unintended deletion. This feature provides an additional layer of protection for logs maintaining audit trails, compliance records, and operational logs that must be preserved.

With deletion protection enabled, administrators can prevent unintended deletions of their most important log groups. Once enabled, log groups cannot be deleted until the protection is explicitly turned off, helping safeguard critical operational, security, and compliance data. This protection is particularly valuable for preserving audit logs and production application logs needed for troubleshooting and analysis.

Log group deletion protection is available in all AWS commercial Regions.

You can enable deletion protection during log group creation or on existing log groups using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. For more information, visit the Amazon CloudWatch Logs User Guide..

 

​Amazon CloudWatch now offers configuring deletion protection on your CloudWatch log groups, helping customers safeguard their critical logging data from accidental or unintended deletion. This feature provides an additional layer of protection for logs maintaining audit trails, compliance records, and operational logs that must be preserved. With deletion protection enabled, administrators can prevent unintended deletions of their most important log groups. Once enabled, log groups cannot be deleted until the protection is explicitly turned off, helping safeguard critical operational, security, and compliance data. This protection is particularly valuable for preserving audit logs and production application logs needed for troubleshooting and analysis. Log group deletion protection is available in all AWS commercial Regions. You can enable deletion protection during log group creation or on existing log groups using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. For more information, visit the Amazon CloudWatch Logs User Guide..  

Amazon Lex now allows you to use Large Language Models (LLMs) as the primary option to understand customer intent across voice and chat interactions. With this capability, your voice and chat bots can better understand customer requests, handle complex utterances, maintain accuracy despite spelling errors, and extract key information from verbose inputs. When customer intent is unclear, bots can intelligently ask follow-up questions to fulfill requests accurately. For example, when a customer says “I need help with my flight,” the LLM automatically clarifies whether the customer wants to check their flight status, upgrade their flight, or change their flight.

This feature is available in all AWS commercial regions where Amazon Connect and Lex operate. To learn more, visit the Amazon Lex documentation or explore the Amazon Connect website to learn how Amazon Connect and Amazon Lex deliver seamless end-customer self-service experiences. 

 

​Amazon Lex now allows you to use Large Language Models (LLMs) as the primary option to understand customer intent across voice and chat interactions. With this capability, your voice and chat bots can better understand customer requests, handle complex utterances, maintain accuracy despite spelling errors, and extract key information from verbose inputs. When customer intent is unclear, bots can intelligently ask follow-up questions to fulfill requests accurately. For example, when a customer says “I need help with my flight,” the LLM automatically clarifies whether the customer wants to check their flight status, upgrade their flight, or change their flight. This feature is available in all AWS commercial regions where Amazon Connect and Lex operate. To learn more, visit the Amazon Lex documentation or explore the Amazon Connect website to learn how Amazon Connect and Amazon Lex deliver seamless end-customer self-service experiences.   

AWS Health now includes two new properties in its event schema – actionability and persona – enabling customers to identify the most relevant events. These properties allow organizations to programmatically identify events requiring customer action and direct them to relevant teams. The enhanced event schema is accessible through both the AWS Health API and Health EventBridge communication channels, improving operational efficiency and team coordination.

AWS customers receive various operational notifications and scheduled changes, including Planned Lifecycle Events. With the new actionability property, teams can quickly distinguish between events requiring action and those shared for awareness. The persona property streamlines event routing and visibility to specific teams like security and billing, ensuring critical information reaches appropriate stakeholders. These structured properties streamline integration with existing operational tools, allowing teams to effectively identify and remediate affected resources while maintaining appropriate visibility across the organization.

This enhancement is available across all AWS Commercial and AWS GovCloud (US) Regions. To learn more about implementing these new properties, see the AWS Health User Guide and the API and EventBridge schema documentation.

 

​AWS Health now includes two new properties in its event schema – actionability and persona – enabling customers to identify the most relevant events. These properties allow organizations to programmatically identify events requiring customer action and direct them to relevant teams. The enhanced event schema is accessible through both the AWS Health API and Health EventBridge communication channels, improving operational efficiency and team coordination. AWS customers receive various operational notifications and scheduled changes, including Planned Lifecycle Events. With the new actionability property, teams can quickly distinguish between events requiring action and those shared for awareness. The persona property streamlines event routing and visibility to specific teams like security and billing, ensuring critical information reaches appropriate stakeholders. These structured properties streamline integration with existing operational tools, allowing teams to effectively identify and remediate affected resources while maintaining appropriate visibility across the organization. This enhancement is available across all AWS Commercial and AWS GovCloud (US) Regions. To learn more about implementing these new properties, see the AWS Health User Guide and the API and EventBridge schema documentation.  

Amazon S3 Block Public Access (BPA) now allows organization-level control through AWS Organizations, allowing you to standardize and enforce S3 public access settings across all accounts in your AWS organization through a single policy configuration.

S3 Block Public Access at the organization level uses a single configuration that controls all public access settings across accounts within your organization. When you attach the policy at the root or Organizational Unit (OU)-level of your organization, it propagates to all sub-accounts within that scope, and new member accounts automatically inherit the policy. Alternatively, you can choose to apply the policy to specific accounts for more granular control. To get started, navigate to the AWS Organizations console and use the «Block all public access» checkbox or JSON editor. Additionally, you can use AWS CloudTrail to audit or keep track of policy attachment as well as enforcement for member accounts.

This feature is available in the AWS Organizations console as well as AWS CLI/SDK, in all AWS Regions where AWS Organizations and Amazon S3 are supported, with no additional charges. For more information, visit the AWS Organizations User Guide and Amazon S3 Block Public Access documentation.

 

​Amazon S3 Block Public Access (BPA) now allows organization-level control through AWS Organizations, allowing you to standardize and enforce S3 public access settings across all accounts in your AWS organization through a single policy configuration. S3 Block Public Access at the organization level uses a single configuration that controls all public access settings across accounts within your organization. When you attach the policy at the root or Organizational Unit (OU)-level of your organization, it propagates to all sub-accounts within that scope, and new member accounts automatically inherit the policy. Alternatively, you can choose to apply the policy to specific accounts for more granular control. To get started, navigate to the AWS Organizations console and use the «Block all public access» checkbox or JSON editor. Additionally, you can use AWS CloudTrail to audit or keep track of policy attachment as well as enforcement for member accounts. This feature is available in the AWS Organizations console as well as AWS CLI/SDK, in all AWS Regions where AWS Organizations and Amazon S3 are supported, with no additional charges. For more information, visit the AWS Organizations User Guide and Amazon S3 Block Public Access documentation.  

Amazon Quick Suite, the AI-powered workspace helping organizations get answers from their enterprise data and move swiftly from insights to action, enhances Quick Research with access to specialized third-party datasets.

Quick Research transforms how business professionals tackle complex business problems by completing weeks of data discovery, analysis, and insight generation in minutes. Today, Quick Research launches its partner ecosystem with industry intelligence providers S&P Global, FactSet, and IDC, with more to come. Users with existing subscriptions can combine these authoritative datasets with all of their business data and real-time web search, accelerating their path to deeper insights and strategic decision-making. Additionally, all users have access to decades of US Patent and Trademark Office data along with millions of PubMed citations and abstracts in biomedical and life sciences literature.

Business professionals from any industry can now access and analyze multiple data sources in one unified workspace, eliminating the need to switch between platforms. For example, a financial analyst can evaluate investment opportunities using FactSet’s financial data alongside real-time web search and internal market reports, while energy teams can optimize trading strategies using S&P Global’s commodity data combined with insights from their strategy teams. Similarly, sales and product teams can spot emerging trends faster by leveraging IDC’s industry intelligence with their customer data. By bringing critical data sources together in one place, organizations can move from insight to action with greater speed and confidence.

Quick Research’s third-party data integration is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland). To learn more, read our User Guide. 

 

​Amazon Quick Suite, the AI-powered workspace helping organizations get answers from their enterprise data and move swiftly from insights to action, enhances Quick Research with access to specialized third-party datasets. Quick Research transforms how business professionals tackle complex business problems by completing weeks of data discovery, analysis, and insight generation in minutes. Today, Quick Research launches its partner ecosystem with industry intelligence providers S&P Global, FactSet, and IDC, with more to come. Users with existing subscriptions can combine these authoritative datasets with all of their business data and real-time web search, accelerating their path to deeper insights and strategic decision-making. Additionally, all users have access to decades of US Patent and Trademark Office data along with millions of PubMed citations and abstracts in biomedical and life sciences literature. Business professionals from any industry can now access and analyze multiple data sources in one unified workspace, eliminating the need to switch between platforms. For example, a financial analyst can evaluate investment opportunities using FactSet’s financial data alongside real-time web search and internal market reports, while energy teams can optimize trading strategies using S&P Global’s commodity data combined with insights from their strategy teams. Similarly, sales and product teams can spot emerging trends faster by leveraging IDC’s industry intelligence with their customer data. By bringing critical data sources together in one place, organizations can move from insight to action with greater speed and confidence. Quick Research’s third-party data integration is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland). To learn more, read our User Guide.   

Amazon Route 53 is excited to release the accelerated recovery option for managing DNS records in public hosted zones. Accelerated recovery targets a 60-minute recovery time objective (RTO) for regaining the ability to make DNS changes to your DNS records in Route 53 public hosted zones, if AWS services in US East (N. Virginia) become temporarily unavailable.

The Route 53 public DNS service API is used by customers today for making changes to DNS records in order to facilitate software deployments, run infrastructure operations, and onboard new users. Customers in banking, financial technology (FinTech), and software-as-a-service (SaaS) in particular need a predictable and short RTO for meeting business continuity and disaster recovery objectives. In the past, if AWS services in US East (N. Virginia) became unavailable, customers would not be able to modify or recreate DNS records to point users and internal services to updated endpoints. Now, when you enable the accelerated recovery option on your Route 53 public hosted zone, you can make changes to Route 53 public DNS records (Resource Record Sets) in that hosted zone soon after such an interruption, most often in less than one hour.

Accelerated recovery for managing public DNS records is available globally, except in AWS GovCloud and Amazon Web Services in China. There is no additional charge for using this feature. To learn more about the accelerated recovery option, visit our documentation.

 

​Amazon Route 53 is excited to release the accelerated recovery option for managing DNS records in public hosted zones. Accelerated recovery targets a 60-minute recovery time objective (RTO) for regaining the ability to make DNS changes to your DNS records in Route 53 public hosted zones, if AWS services in US East (N. Virginia) become temporarily unavailable. The Route 53 public DNS service API is used by customers today for making changes to DNS records in order to facilitate software deployments, run infrastructure operations, and onboard new users. Customers in banking, financial technology (FinTech), and software-as-a-service (SaaS) in particular need a predictable and short RTO for meeting business continuity and disaster recovery objectives. In the past, if AWS services in US East (N. Virginia) became unavailable, customers would not be able to modify or recreate DNS records to point users and internal services to updated endpoints. Now, when you enable the accelerated recovery option on your Route 53 public hosted zone, you can make changes to Route 53 public DNS records (Resource Record Sets) in that hosted zone soon after such an interruption, most often in less than one hour. Accelerated recovery for managing public DNS records is available globally, except in AWS GovCloud and Amazon Web Services in China. There is no additional charge for using this feature. To learn more about the accelerated recovery option, visit our documentation.