Kategorie: Amazon AWS

The AWS MCP Server now supports cross-account and cross-role access

Today, AWS announced cross-account and cross-role access for the AWS Model Context Protocol (MCP) Server, part of the Agent Toolkit for AWS. This feature allows developers using AI coding agents like Kiro, Claude Code, or Codex to work across multiple AWS accounts and AWS Identity and Access Management (IAM) roles within a single session, with no restarts required. Previously, switching profiles required stopping the AI coding session, updating local AWS credentials, and restarting the MCP server for every account change. Now, AI agents using the AWS MCP Server can specify a profile on each command, allowing users to switch between accounts and roles seamlessly.
Cross-account access helps developers move faster across multi-account environments. For example, a DevOps engineer can query CloudWatch logs across production and staging accounts to diagnose a performance issue, or an application developer can update a Lambda configuration in one account and adjust an S3 bucket policy in another, all within the same conversation. Each request specifies which profile to use, so there is no risk of commands reaching the wrong account.
To get started, see Multi-profile support in the Agent Toolkit for AWS user guide. The AWS MCP Server is available in the US East (N. Virginia) and Europe (Frankfurt) Regions.
Quelle: aws.amazon.com

Amazon ECS with AWS Fargate now supports 32vCPU compute configurations

Amazon Elastic Container Service (Amazon ECS) with AWS Fargate now supports 32vCPU compute configurations, enabling customers to run more demanding applications with greater flexibility and performance. AWS Fargate offers 32vCPU tasks with the following memory configurations: 60 GiB, 120 GiB, or 244 GiB, for both x86-based and ARM-based workloads on Linux. These new task sizes extend Amazon ECS’s capability to support high-performance computing use-cases, large-scale data processing, AI inference, and other compute-intensive workloads. With 32vCPUs and up to 244 GiB of memory, Amazon ECS customers can now deploy larger containers and scale applications beyond previous limits, all while leveraging the reliability, security, and scalability of AWS Fargate. To use the new 32vCPU task sizes, simply configure your task definitions to specify 32 as the vCPU value and select one of the new memory options (60, 120, or 244 GiB), then deploy your Amazon ECS services or tasks as usual via the AWS Management Console, CLI, or your infrastructure-as-code of choice. The new vCPU and memory configurations are available on both Fargate and Fargate Spot capacity providers, and existing Compute Savings Plans apply automatically. For pricing details, refer to AWS Fargate pricing page. The 32vCPU tasks are available with Amazon ECS and AWS Fargate in all AWS commercial and AWS GovCloud (US) Regions. To learn more, refer to the Amazon ECS documentation.
Quelle: aws.amazon.com

AWS Databases on Vercel now available in additional AWS Regions

Amazon Aurora PostgreSQL, Amazon Aurora DSQL, and Amazon DynamoDB serverless databases are now available on Vercel Marketplace and v0 by Vercel in additional AWS Regions, offering you more flexibility to build applications with Vercel and AWS databases from the Regions of your choice. To get started, you can describe your idea in v0 using natural language. The tool automatically generates a spec-driven design, deploys code and infrastructure, and stores your application data in the AWS database that best fits your needs with no hands-on coding or provisioning required. Vercel provides an end-to-end setup experience where you can create database resources in seconds under a new AWS account or link to an existing one, all without leaving Vercel. New AWS accounts created from Vercel include access to all three databases and $100 USD in credits, usable across any of these database options for up to six months. You can manage your plan, add payment information, and view usage details anytime from the AWS settings portal in the Vercel dashboard. To learn more, visit v0 or the AWS landing page on the Vercel Marketplace. You can now create an Aurora PostgreSQL database or Amazon DynamoDB table through Vercel from 17 AWS Regions enabled by default, and Aurora DSQL from 16 AWS Regions including: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). AWS Databases deliver security, reliability, and price performance without the operational overhead, whether you’re prototyping your next big idea or running production AI and data driven applications. For more information, visit the AWS Databases webpage.
Quelle: aws.amazon.com

Amazon EKS Capabilities now supports Amazon CloudWatch Vended Logs

Amazon Elastic Kubernetes Service (Amazon EKS) Capabilities can now be configured as log delivery sources using Amazon CloudWatch Vended Logs. This enables customers to monitor and troubleshoot their EKS Capabilities for Argo CD, AWS Controllers for Kubernetes (ACK), and kro (Kubernetes Resource Orchestrator) by monitoring logs collected from the managed controllers that run in AWS-managed infrastructure. Customers can enable log delivery for each capability using CloudWatch APIs or the AWS Console. Logs are configured as a CloudWatch Vended Logs delivery source, enabling reliable, secure log delivery to CloudWatch Logs, Amazon S3, or Amazon Kinesis Data Firehose destinations. This feature is available in all AWS Regions where the EKS Capabilities feature is supported. Standard CloudWatch Vended Logs pricing applies based on the chosen destination. There is no additional EKS charge. To learn more about EKS Capabilities, visit the Amazon EKS documentation.
Quelle: aws.amazon.com

Amazon SageMaker Data Agent integrates business context into conversations

Amazon SageMaker Data Agent now integrates with SageMaker Catalog business context and metadata, enabling data practitioners to discover datasets and generate more accurate SQL and Python code using business terminology instead of cryptic technical table names. This integration allows the Data Agent to leverage the business context that companies have invested months curating in their SageMaker Catalog, including those synced from Collibra, Atlan, and Alation, to deliver more accurate data discovery and code generation.
With this capability, data practitioners can ask questions like “Calculate customer retention rate” or “What data do I have on customer churn?” and the Data Agent will search glossary terms, custom metadata forms, asset summaries, and README content to identify the correct tables and columns. The agent generates more accurate code on first attempt by understanding business context, plans multi-step workflows with the correct sequence of tables and transformations, and respects data governance by checking subscription status and providing access request links when needed. Organizations maximize their existing catalog investment without changing the current data workflows, reducing time-to-insight, and enabling data teams to work in business language rather than deciphering technical table names.
This integration is available in SageMaker Unified Studio notebooks and Query Editor in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Amazon SageMaker Unified Studio page and Amazon SageMaker Data Agent documentation.
Quelle: aws.amazon.com

Amazon MQ is now available in the AWS European Sovereign Cloud (Germany) Region

You can now deploy Amazon MQ for RabbitMQ in the AWS European Sovereign Cloud (Germany) Region. This new independent cloud for Europe is located entirely within the EU, designed to help customers in regulated industries and public sector organizations meet their sovereignty requirements. Amazon MQ is a managed message broker service that makes it easy to set up and operate message brokers in the cloud. Amazon MQ for RabbitMQ manages the provisioning, patching, and maintenance of RabbitMQ brokers, letting you focus on building applications without managing messaging infrastructure. You can migrate existing RabbitMQ workloads without rewriting application code and benefit from the same familiar APIs and protocols. Amazon MQ for RabbitMQ in the AWS European Sovereign Cloud supports RabbitMQ engine version 4.2 and Graviton3-based m7g instance types for high-performance messaging ranging from m7g.medium to m7g.16xlarge. To get started, see the Amazon MQ product page or the Amazon MQ Developer Guide.
Quelle: aws.amazon.com

Amazon Cognito now supports multi-Region replication

Amazon Cognito now supports multi-Region replication, enabling you to synchronize user and machine identity data — including credentials, user pool configurations, and federation setups — to a secondary user pool in a standby Region you designate in near real-time. This capability helps you improve the resilience of your authentication system by providing a standby replica that can accept traffic in case there is a regional service disruption. In the event of a disruption in the primary Region, you can redirect traffic to the secondary user pool. Signed-in users continue accessing their applications without re-authenticating, and registered users can sign in with their existing credentials. Authentication methods continue to work in the secondary Region, including username/password, federation with social identity and SAML/OIDC providers, and machine-to-machine authorization flows. Multi-Region replication is available as an add-on for user pools in Essentials or Plus feature tiers. You can start using this feature in the following AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), and South America (São Paulo). To get started, configure multi-Region replication using the AWS Management Console, AWS Command Line Interface (CLI), or AWS Software Development Kits (SDKs) by adding a replica user pool. Visit the pricing page for pricing details and the developer guide for instructions.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio now supports notebook scheduling

Amazon SageMaker Unified Studio now enables you to schedule, parameterize, and orchestrate notebook runs directly from the notebook interface without managing external orchestration infrastructure. This makes it easier for customers to take notebooks from experimentation to production, automating recurring workloads such as daily reports, data quality checks, and model retraining.
You can trigger on-demand background runs on dedicated compute without interrupting interactive sessions and create scheduled or recurring runs. With notebook parameterization, you can reuse a single notebook across different inputs, for example, generating shipping performance reports for multiple carriers, by defining parameters and overriding their values per schedule or on-demand run. You can also orchestrate multi-notebook workflows using the Notebook Operator in the Workflows tool, chaining notebooks so that outputs from one run feed as inputs to the next. When a scheduled or background run fails, AI-assisted troubleshooting using SageMaker Data Agent helps you identify the root cause and suggests fixes directly in the notebook, reducing time to resolution. You can also use the Data Agent to create schedules and start notebook runs using natural language, without having to navigate. To get started, open a notebook in your SageMaker Unified Studio project, choose the menu on the Run all button, and select Run in background. To create a schedule, choose the schedule icon in the notebook header or ask the Data Agent to set one up for you.
You can use notebook scheduling in all AWS Regions where Amazon SageMaker Unified Studio is supported. To learn more, see the AWS blog and user guide.
Quelle: aws.amazon.com

Amazon SageMaker Data Agent now supports conversation history

Amazon SageMaker Data Agent, available in SageMaker Unified Studio now supports conversation history, enabling data practitioners to maintain continuity across analytical sessions. Data analysts and data scientists can now seamlessly reference previous agent-generated code, resume multi-step analyses, and review past troubleshooting interactions within their notebooks and Query Editor workflows.
With conversation history, you can pick up exactly where you left off by accessing a scrollable list of past conversations through the clock icon in the chat panel header. Each conversation includes auto-generated titles and timestamps for easy identification. Whether you’re resuming complex multi-step analyses, reusing agent-generated code, or continuing troubleshooting from earlier notebook runs, conversation history keeps the context preserved. Data teams save time, eliminate rework, and move faster across concurrent projects, staying focused on insights rather than rebuilding context.
Conversation history is available in all AWS Regions where Amazon SageMaker Data Agent is currently available. To learn more about Amazon SageMaker Data Agent and how to leverage conversation history in your analytical workflows, visit the Amazon SageMaker product page or explore the Amazon SageMaker Unified Studio documentation.
Quelle: aws.amazon.com

AWS IoT Device Management adds MQTT session data to connectivity status API

AWS IoT Device Management adds MQTT session data to connectivity status API, enabling you to troubleshoot connectivity issues and audit connection patterns across your Internet of things (IoT) device fleet. This launch brings AWS IoT Device Management’s existing connectivity status API to full parity with AWS IoT Core’s recently launched GetConnection API, enabling you to retrieve detailed connection and MQTT session information for the IoT device by its thing name. In addition to the connection status, timestamp, and disconnect reason already available, you now get visibility into MQTT session timeout and session expiry values, along with optional socket level details such as source and destination IP addresses, ports, and client VPC endpoint ID. Access to socket information is controlled through granular IAM policies, so you can restrict it to the teams that need it. A key advantage of the connectivity status API over AWS IoT Core’s GetConnection API is data retention. While GetConnection retains connection and session details for 30 minutes after a device disconnects, the connectivity status API stores this information indefinitely. This means you can investigate disconnect reasons, review session metadata, and troubleshoot issues long after a device goes offline. This enhancement is available in all AWS regions where AWS IoT Device Management is supported. AWS IoT Device Management only supports devices registered in AWS IoT Core Thing Registry. To learn more, visit the AWS IoT Device Management documentation and reference guide.
Quelle: aws.amazon.com