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AWS IoT Core introduces DeleteConnection API to streamline MQTT connection management

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AWS IoT Core now offers the DeleteConnection API, enabling programmatic disconnection of MQTT clients using their client IDs. This new capability helps enable developers to terminate MQTT connections with options to clear persistent sessions and suppress publication of Last Will and Testament messages—messages that the MQTT broker automatically publishes on a client’s behalf when it disconnects unexpectedly. Upon disconnection, the service generates lifecycle events, providing enhanced operational visibility into device connection states.

The DeleteConnection API helps developers manage device connectivity more effectively, whether redirecting devices across endpoints, troubleshooting connection issues, or handling problematic device behavior. The DeleteConnection API is now available in all AWS Regions where AWS IoT Core is supported. To learn more, visit the AWS IoT Core documentation  and AWS IoT Core API reference guide .

Amazon GameLift Streams launches Proton 9 runtime and increases service limits

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Today, Amazon GameLift Streams announces the launch of a new runtime environment, Proton 9, along with increased default service limits for all customers. This update enhances the capabilities of Amazon GameLift Streams, a service that enables game developers to stream games from AWS to players on various devices.

Proton 9 is a significant update to the Proton compatibility layer, which allows Windows games and applications to run on Linux systems. Building upon previous versions, including Proton 8, this new release aims to improve compatibility and performance for a wider range of Windows titles. The enhanced runtime environment provides game developers with more flexibility and options for deploying their games on Amazon GameLift Streams, potentially expanding their reach to a broader audience across different platforms.

This launch is available in all supported Amazon GameLift Streams regions, ensuring that game developers and players worldwide can benefit from these improvements.

For a complete list of available runtime environments, including Proton 9, see Configuration options in the Amazon GameLift Streams Developer Guide. You can also get started by accessing the AWS Management Console and navigating to the Amazon GameLift Streams service page.

Amazon SageMaker lakehouse architecture now automates optimization configuration of Apache Iceberg tables

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The Amazon SageMaker lakehouse architecture now automates optimization of Apache Iceberg tables stored in Amazon S3 with catalog-level configuration, reducing metadata overhead and improving query performance. Previously, optimizing Iceberg tables in AWS Glue Data Catalog required updating configurations for each table individually. Now, you can enable automatic optimization for new Iceberg tables with a one-time Data Catalog configuration. Once enabled, for any new table or updated table, Data Catalog continuously optimizes tables by compacting small files, removing snapshots, and unreferenced files that are no longer needed, resulting in controlled storage costs and faster queries.

You can get started by selecting the default catalog in the AWS Lake Formation console and enabling optimizations in the table optimizations configuration tab. You have the choice of additional granular control at the table configuration level, such as sort/z-order compaction strategy, thresholds for the number of small files to trigger compaction, intervals between consecutive snapshot expirations, and unreferenced data cleanup operations.

This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in 15 AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland, London, Frankfurt, Stockholm), Asia Pacific (Tokyo, Seoul, Mumbai, Singapore, Sydney, Jakarta), and South America (São Paulo). To learn more, read the blog , and visit the Data Catalog documentation .

Amazon SageMaker HyperPod now supports continuous provisioning for enhanced cluster operations

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Amazon SageMaker HyperPod now offers continuous provisioning, a new capability that enables greater flexibility and efficiency for enterprise customers running large-scale AI/ML workloads. AI/ML customers need to start training quickly, scale seamlessly, perform maintenance without disrupting operations, and have granular visibility into cluster operations. Customers also require the ability to efficiently manage dynamic inference workloads where capacity needs change frequently, making operational agility critical for successful AI initiatives.

With continuous provisioning, SageMaker HyperPod automatically provisions remaining capacity in the background while training jobs can begin immediately on available instances. HyperPod will retry in the background when it encounters node provisioning failures and ensure clusters reliably reach their desired scale without requiring any manual intervention. This helps customers reduce time-to-training and maximizes resource utilization across dynamic workloads. You can now perform concurrent operations such as scaling nodes independently, applying patches, or adjusting different instance groups simultaneously, thus increasing efficiency. The enhanced event-driven architecture provides comprehensive real-time visibility through the new Events APIs, offering complete operational history to enable faster troubleshooting and better decision-making. These capabilities enable customers to achieve improved operational agility, better resource utilization, and enhanced visibility into cluster operations, allowing AI/ML teams to focus on innovation rather than infrastructure management.

This feature is currently available for SageMaker HyperPod clusters using the EKS orchestrator. You can enable continuous provisioning by setting the NodeProvisioningMode parameter to “Continuous” when creating new HyperPod clusters using the CreateCluster API.

This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about continuous provisioning, see the Amazon SageMaker HyperPod User Guide .

Amazon CloudWatch RUM is now generally available in 2 additional AWS regions

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Amazon CloudWatch RUM, which enables customers to monitor their web applications by collecting client side performance and error data in real time, is additionally available in the following AWS Regions starting today: Asia Pacific (Thailand), and Mexico (Central).

CloudWatch RUM provides curated dashboards for web application performance experienced by real end users including anomalies in page load steps, core web vitals, and JavaScript and HTTP errors across different geolocations, browsers, and devices. Custom events and metrics sent to CloudWatch RUM can be easily configured to monitor specific parts of the application for real user interactions, troubleshoot issues, and get alerted for anomalies. CloudWatch RUM comes integrated with the application performance monitoring (APM) capability, CloudWatch Application Signals . As a result, client-side data from your application can easily be correlated with performance metrics such as errors, faults, and latency observed in your APIs (service operations) and dependencies to address the root cause.

To get started, see the RUM User Guide . Usage of CloudWatch RUM is charged on the number of collected RUM events, which refers to each data item collected by the RUM web client, as detailed here . 

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