Kategorie: Amazon AWS

Amazon Aurora DSQL now supports the JSONB data type with compression

Amazon Aurora DSQL introduces support for the PostgreSQL JSONB data type with optional compression. You can now use code and tools that depend on PostgreSQL’s JSONB type with Aurora DSQL, making it easier to store semi-structured data alongside relational data. You can use the JSONB data type when creating or modifying tables to store semi-structured data such as system configuration metadata, API parameters, and event logs. With PostgreSQL compression enabled by default, larger JSONB payloads are stored more efficiently, helping reduce storage costs. Get started with Aurora DSQL for free with the AWS Free Tier. For information about Regional availability, see the AWS Region table. You can learn more about Aurora DSQL data types, including JSONB, here.
Quelle: aws.amazon.com

AWS Savings Plans Purchase Analyzer now supports target coverage analysis

Today, AWS announces target coverage analysis in Savings Plans Purchase Analyzer, a capability in AWS Billing and Cost Management that helps you plan your Savings Plans purchases based on your coverage target. Savings Plans Purchase Analyzer helps you evaluate different purchase scenarios by estimating the potential impact of Savings Plans purchases on cost, coverage, utilization, and savings. With target coverage analysis, you can set a specific percentage of On-Demand spend to be covered by Savings Plans. Savings Plans Purchase Analyzer uses your historical usage to recommend a new purchase amount to help you reach that target. You can further customize your analysis using parameters such as custom lookback period or excluding expiring Savings Plans, and compare cost, coverage, utilization, and savings across different coverage targets. You can view your recommendations through interactive charts or access your target coverage analysis via the Purchase Analyzer API. Target coverage analysis is available in all AWS Regions where Savings Plans Purchase Analyzer is available. To learn more, visit the AWS Savings Plans page and user guide.
Quelle: aws.amazon.com

Amazon OpenSearch UI is now available in GovCloud regions

Amazon OpenSearch Service expands its modernized operational analytics experience to GovCloud regions, including AWS GovCloud (US-East) and AWS GovCloud (US-West), enabling users to gain insights across data spanning managed domains and serverless collections from a single endpoint. The expansion includes Workspaces to enhance collaboration and productivity, allowing teams to create dedicated spaces. Discover is revamped to provide a unified log exploration experience supporting languages such as Piped-Processing-Language (PPL) and SQL, in addition to DQL and Lucene. Discover now features a data selector to support multiple sources, new visual design and query autocomplete for improved usability. This experience ensures users can access the latest UI enhancements, regardless of version of underlying managed cluster or collection. The expanded OpenSearch analytics helps users gain insights from their operational data by providing purpose-built features for observability, security analytics, and search use cases. With the enhanced Discover interface, users can now analyze data from multiple sources without switching tools, improving efficiency. Workspaces enable better collaboration by creating dedicated environments for teams to work on dashboards, saved queries, and other relevant content. Availability of the latest UI updates across all versions ensures uninterrupted access to the newest features and tools. OpenSearch UI can connect to OpenSearch domains (above version 1.3) and OpenSearch serverless collections. To get started, create an OpenSearch application in AWS Management Console. Learn more at Amazon OpenSearch Service Developer Guide.
Quelle: aws.amazon.com

Simplified permissions for Amazon S3 Tables and Iceberg materialized views are now available in AWS GovCloud (US) Regions

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation.
Quelle: aws.amazon.com

Amazon Bedrock AgentCore Runtime introduces interactive shells for terminal access into agent sessions

Amazon Bedrock AgentCore Runtime now supports interactive shells through a new InvokeAgentRuntimeCommandShell API, opening a persistent, PTY-backed terminal directly into a running agent session over WebSocket. This complements the existing InvokeAgentRuntimeCommand API for one-shot execution, giving developers a full terminal experience inside an isolated microVM with colors, tab completion, Ctrl+C, terminal resize, and automatic reconnect on network drop. This is particularly important for developers hosting coding agents such as Claude Code, OpenAI Codex, Amazon Kiro on AgentCore Runtime. In addition to the asynchronous command execution they already had, they can now authenticate, drop into the microVM hosting their coding agent, and interact with it like a local terminal: interact with the agent, inspect files, run ad-hoc commands, or debug the environment state. The shell carries persistent state across commands within the same session, so environment variables, working directory, and command history all behave as expected. Each interactive session is identified by a runtime session ID and a shell ID. Passing both back when reconnecting lands you in the exact same shell. Brief network drops reconnect automatically, and longer disconnects can be resumed manually using the same IDs. A single agent runtime supports up to 10 concurrent shells, allowing developers to open multiple terminals against the same or multiple microVMs and watch agents work different branches in parallel. To get started using the AgentCore CLI: `agentcore exec –it –runtime <runtime-arn>`. To learn more, see Interactive Shells (Terminals) and Shell execution in AgentCore Runtime for a comparison of both shell modes.
Quelle: aws.amazon.com

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