Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data.

The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a «Troubleshoot with AI» option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail.

The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.

 

​Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data. The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a «Troubleshoot with AI» option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail. The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in US East (N. Virginia) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.

Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.

You can start using Amazon EC2 G7 instances today in three AWS Regions: US East (N. Virginia and Ohio), and US West (Oregon). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.

To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in US East (N. Virginia) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.
Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.
You can start using Amazon EC2 G7 instances today in three AWS Regions: US East (N. Virginia and Ohio), and US West (Oregon). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.  

AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hyderabad) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances.

I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS).

To learn more, visit the I7ie instances page.

 

​AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hyderabad) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.  

Today, Amazon Location Service announced new enhancements to its Places APIs that give developers greater control over address name formatting, multilingual address, travel-optimized POI search, and drive-through data. These capabilities span the Geocode, ReverseGeocode, GetPlace, Suggest, Autocomplete, SearchNearby, and SearchText APIs. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing.

Developers can now control how address component names are returned using the new AddressNamesMode parameter — choosing between matched (echoing input), normalized (canonical names), or administrative (government hierarchy names) — and override behavior per component with AddressNamesVariant. The new AddressTranslations parameter returns place name translations in 50+ languages, making it easier to build multilingual applications. A TravelMode parameter optimizes Suggest and SearchText results for users on the move, improving relevance for navigation and in-vehicle scenarios. Additionally, GetPlace, Suggest, SearchNearby, and SearchText now return a DriveThrough attribute indicating whether a place offers drive-through service — useful for logistics, food delivery, and navigation applications. The Geocode API also now supports a new Parsing.AdditionalInfo response field with additional detail about how the input address was interpreted.

Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West).

To get started, see the Amazon Location Service Places API reference, or learn more about Places in the Amazon Location Service Developer Guide.

 

​Today, Amazon Location Service announced new enhancements to its Places APIs that give developers greater control over address name formatting, multilingual address, travel-optimized POI search, and drive-through data. These capabilities span the Geocode, ReverseGeocode, GetPlace, Suggest, Autocomplete, SearchNearby, and SearchText APIs. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing. Developers can now control how address component names are returned using the new AddressNamesMode parameter — choosing between matched (echoing input), normalized (canonical names), or administrative (government hierarchy names) — and override behavior per component with AddressNamesVariant. The new AddressTranslations parameter returns place name translations in 50+ languages, making it easier to build multilingual applications. A TravelMode parameter optimizes Suggest and SearchText results for users on the move, improving relevance for navigation and in-vehicle scenarios. Additionally, GetPlace, Suggest, SearchNearby, and SearchText now return a DriveThrough attribute indicating whether a place offers drive-through service — useful for logistics, food delivery, and navigation applications. The Geocode API also now supports a new Parsing.AdditionalInfo response field with additional detail about how the input address was interpreted. Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West). To get started, see the Amazon Location Service Places API reference, or learn more about Places in the Amazon Location Service Developer Guide.  

AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach.

Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow.

Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.

 

​AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach.
Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow.
Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.  

Amazon DocumentDB (with MongoDB compatibility) now supports R8g.24xlarge and R8g.48xlarge database instances. R8g instances are powered by AWS Graviton4 processors and feature DDR5 memory, enabling customers to achieve higher throughput and support larger working sets in memory. With R8g.24xlarge (96 vCPUs, 768 GiB memory) and R8g.48xlarge (192 vCPUs, 1,536 GiB memory), customers can run more demanding workloads such as high-concurrency transactional applications, large-scale document processing, and memory-intensive operational workloads.

Customers can get started with R8g.24xlarge and R8g.48xlarge instances through the AWS Management Console, CLI, and SDK by modifying their existing Amazon DocumentDB database cluster or creating a new one. R8g instances are available for Amazon DocumentDB 5.0+ on both Standard and IO-Optimized cluster storage configurations. For more information including region availability, visit our pricing page and documentation.

 

​Amazon DocumentDB (with MongoDB compatibility) now supports R8g.24xlarge and R8g.48xlarge database instances. R8g instances are powered by AWS Graviton4 processors and feature DDR5 memory, enabling customers to achieve higher throughput and support larger working sets in memory. With R8g.24xlarge (96 vCPUs, 768 GiB memory) and R8g.48xlarge (192 vCPUs, 1,536 GiB memory), customers can run more demanding workloads such as high-concurrency transactional applications, large-scale document processing, and memory-intensive operational workloads. Customers can get started with R8g.24xlarge and R8g.48xlarge instances through the AWS Management Console, CLI, and SDK by modifying their existing Amazon DocumentDB database cluster or creating a new one. R8g instances are available for Amazon DocumentDB 5.0+ on both Standard and IO-Optimized cluster storage configurations. For more information including region availability, visit our pricing page and documentation.  

AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously.

The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024.

AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.

 

​AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously.
The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024.
AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.  

You can now connect AI agents directly to the AWS MCP Server using AWS Sign-In. Agents connect using industry-standard OAuth without requiring additional authentication software. Existing AWS identities, sign-in methods, IAM permissions, and governance controls you have already set up continue to apply.

Developers can authorize agents interactively through a browser or programmatically using non-interactive (headless) authorization. Administrators can govern OAuth access using familiar IAM policies together with new OAuth capabilities, including global condition keys, token introspection and revocation APIs, dynamic client registration, and CloudTrail audit events.

To learn more, see the OAuth Support for the AWS MCP Server blogpost, Sign-In with OAuth 2.0 in the AWS Sign-In User Guide, and Setting up the AWS MCP Server in the Agent Toolkit for AWS User Guide.

 

​You can now connect AI agents directly to the AWS MCP Server using AWS Sign-In. Agents connect using industry-standard OAuth without requiring additional authentication software. Existing AWS identities, sign-in methods, IAM permissions, and governance controls you have already set up continue to apply.
Developers can authorize agents interactively through a browser or programmatically using non-interactive (headless) authorization. Administrators can govern OAuth access using familiar IAM policies together with new OAuth capabilities, including global condition keys, token introspection and revocation APIs, dynamic client registration, and CloudTrail audit events.
To learn more, see the OAuth Support for the AWS MCP Server blogpost, Sign-In with OAuth 2.0 in the AWS Sign-In User Guide, and Setting up the AWS MCP Server in the Agent Toolkit for AWS User Guide.  

Amazon Timestream for InfluxDB now publishes events to Amazon EventBridge when your database instances or clusters undergo state changes. Events are emitted for lifecycle operations including creation, deletion, compute and storage scaling, parameter group updates, maintenance windows, and reboot — covering both successful completions and failures.

With this capability, customers can use Amazon EventBridge rules to programmatically react to database operations without polling the API for status. DevOps teams can build automation workflows that trigger when a scaling operation completes, operations teams can route failure events for immediate alerting, and compliance teams can persist all events to Amazon CloudWatch Logs or Amazon S3 for audit trails. Events are published to the default Amazon EventBridge event bus in your account with source aws.timestream-influxdb, supporting content-based filtering and routing to any EventBridge target including AWS Lambda functions, AWS Step Functions, Amazon SQS queues, Amazon SNS topics, and cross-account event buses.

This capability is available in all AWS Regions where Amazon Timestream for InfluxDB is available. Standard Amazon EventBridge pricing applies for rule evaluation and target delivery. To get started, open the Amazon EventBridge console and create a rule with source aws.timestream-influxdb. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.

 

​Amazon Timestream for InfluxDB now publishes events to Amazon EventBridge when your database instances or clusters undergo state changes. Events are emitted for lifecycle operations including creation, deletion, compute and storage scaling, parameter group updates, maintenance windows, and reboot — covering both successful completions and failures. With this capability, customers can use Amazon EventBridge rules to programmatically react to database operations without polling the API for status. DevOps teams can build automation workflows that trigger when a scaling operation completes, operations teams can route failure events for immediate alerting, and compliance teams can persist all events to Amazon CloudWatch Logs or Amazon S3 for audit trails. Events are published to the default Amazon EventBridge event bus in your account with source aws.timestream-influxdb, supporting content-based filtering and routing to any EventBridge target including AWS Lambda functions, AWS Step Functions, Amazon SQS queues, Amazon SNS topics, and cross-account event buses. This capability is available in all AWS Regions where Amazon Timestream for InfluxDB is available. Standard Amazon EventBridge pricing applies for rule evaluation and target delivery. To get started, open the Amazon EventBridge console and create a rule with source aws.timestream-influxdb. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.  

Amazon SageMaker HyperPod now supports deep health checks for Slurm-orchestrated clusters created with continuous provisioning, enabling you to proactively verify GPU accelerator health on running instances at any time. Continuous provisioning lets you start training quickly and scale instance groups asynchronously without all-or-nothing failures, and you can now pair that flexibility with comprehensive hardware validation as instances come online. This capability addresses a critical challenge where even a single unhealthy node can waste hours of compute time and delay critical workloads.

With deep health checks, you can target entire instance groups or specific instances to run comprehensive hardware stress tests and connectivity tests before committing compute resources to a job. Because continuous provisioning adds worker nodes to your Slurm cluster asynchronously as capacity becomes available, you can run deep health checks on each new node as it comes online, validating hardware before scheduling jobs on it and without interrupting workloads already running on healthy nodes. Progress and results are visible at both the instance group and instance level through the SageMaker console and APIs, providing complete visibility into GPU health, network connectivity, and multi-node communication performance. Instances undergoing checks are automatically isolated from workload scheduling and returned to service upon passing. When paired with HyperPod’s automatic node recovery capability, instances that fail are automatically rebooted or replaced, ensuring cluster health.

This capability is available in all regions where Amazon SageMaker HyperPod is available. To learn more about on-demand deep health checks and continuous provisioning, see the Amazon SageMaker HyperPod User Guide.

 

​Amazon SageMaker HyperPod now supports deep health checks for Slurm-orchestrated clusters created with continuous provisioning, enabling you to proactively verify GPU accelerator health on running instances at any time. Continuous provisioning lets you start training quickly and scale instance groups asynchronously without all-or-nothing failures, and you can now pair that flexibility with comprehensive hardware validation as instances come online. This capability addresses a critical challenge where even a single unhealthy node can waste hours of compute time and delay critical workloads. With deep health checks, you can target entire instance groups or specific instances to run comprehensive hardware stress tests and connectivity tests before committing compute resources to a job. Because continuous provisioning adds worker nodes to your Slurm cluster asynchronously as capacity becomes available, you can run deep health checks on each new node as it comes online, validating hardware before scheduling jobs on it and without interrupting workloads already running on healthy nodes. Progress and results are visible at both the instance group and instance level through the SageMaker console and APIs, providing complete visibility into GPU health, network connectivity, and multi-node communication performance. Instances undergoing checks are automatically isolated from workload scheduling and returned to service upon passing. When paired with HyperPod’s automatic node recovery capability, instances that fail are automatically rebooted or replaced, ensuring cluster health. This capability is available in all regions where Amazon SageMaker HyperPod is available. To learn more about on-demand deep health checks and continuous provisioning, see the Amazon SageMaker HyperPod User Guide.