Kategorie: Amazon AWS

Amazon MSK Express Brokers now support automatic topic creation with Kafka Streams

Effective today, Amazon MSK Express Brokers support automatic topic creation with Kafka Streams. Customers can now deploy their Kafka Streams applications on Express Brokers without needing to manually pre-create or manage topics for stateful operations. MSK Express Brokers are designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent. Kafka Streams uses topics to store state and repartition data for stateful operations. Previously, customers running Kafka Streams with Express Brokers had to manually name and pre-create these topics before deploying their application. With this launch, these topics are created automatically when the application starts, simplifying deployment and reducing operational setup for Kafka Streams applications on Express Brokers. This capability is available today in all AWS regions where MSK Express Brokers are available. No additional configuration or setup is required to get started. To learn more, see Amazon MSK Developer Guide.
Quelle: aws.amazon.com

AWS Compute Optimizer now supports idle recommendations for six additional resource types

AWS Compute Optimizer now identifies idle resources for Amazon DynamoDB provisioned tables, Amazon ElastiCache (Redis and Valkey), Amazon MemoryDB, Amazon DocumentDB (provisioned and serverless), Amazon WorkSpaces, and Amazon SageMaker endpoints. This expansion enables you to detect unused resources across more of your AWS environment and identify potential cost savings.
Compute Optimizer analyzes utilization metrics to determine whether a resource is idle. Customers can set this lookback period based on the nature of their workloads. For each resource type, Compute Optimizer evaluates service-specific signals such as consumed capacity, cache hits, active connections, and CPU utilization. When Compute Optimizer identifies potential idle resources, it surfaces these recommendations, along with detailed utilization metrics and estimated savings in the console, enabling you to evaluate recommendations before acting. You can also view idle resource recommendations across all AWS accounts in your organization through the Cost Optimization Hub, with de-duplicated estimated savings with other recommendations on the same resources.
For more information about the AWS Regions where Compute Optimizer is available, see the AWS Region table. For more information about AWS Compute Optimizer, visit our product page and documentation. You can start using AWS Compute Optimizer through the AWS Management Console, AWS CLI, and AWS SDK.
Quelle: aws.amazon.com

AWS Cost Explorer launches intelligent cost explanations powered by Amazon Q

AWS Cost Explorer now supports ‘Analyze with Amazon Q’, a new capability that delivers comprehensive cost explanations for any report you configure in Cost Explorer. With a single button click you now can receive detailed analysis from Amazon Q Developer covering your cost trends, top cost drivers, and anomalies. All analysis uses your exact filters and time-period and provides guidance to discover optimization opportunities through follow-up questions.
Previously, cost analysis required manual investigation across multiple filters and data points. With ‘Analyze with Amazon Q’, you simply configure your Cost Explorer view and click a single button. Amazon Q analyzes your current context and delivers explanations directly in its chat panel, adapting to what you’re viewing: historical explanations for past dates, forecast explanations for future dates, or both for mixed periods. You can then ask follow-up questions to explore any insights related to your cost data in greater detail as Amazon Q maintains full conversation context throughout.
‘Analyze with Amazon Q’ is available in all commercial AWS Regions at no additional charge. To get started, visit the AWS Cost Explorer console, or view the user guide.
Quelle: aws.amazon.com

Amazon Redshift reduces manual snapshot cost for Serverless and RG instances

Amazon Redshift announces a new billing model for manual snapshots on Amazon Redshift Serverless and Amazon Redshift RG instances. With this enhancement, Amazon Redshift now meters manual snapshot storage based on the unique data blocks stored across your snapshots rather than the total size of each individual snapshot. This results in lower manual snapshot costs for customers who maintain multiple snapshots. Customers who maintain multiple manual snapshots for disaster recovery, testing, or long-term retention will see reduced storage costs. With this new billing model, you can take more frequent manual snapshots to achieve a better recovery point objective (RPO) without proportional cost increases, enabling more robust disaster recovery strategies. The new billing model automatically applies to both existing and new manual snapshots. The new manual snapshot billing model is available in all AWS commercial and AWS GovCloud (US) Regions where Amazon Redshift Serverless and Amazon Redshift RG instances are available. To learn more about Amazon Redshift snapshots, please visit our documentation or the blog.
Quelle: aws.amazon.com

AWS Application Migration Service is now AWS Transform MGN

AWS Application Migration Service (MGN) is now available as AWS Transform MGN. This name change reflects MGN’s role as the proven replication engine powering AWS Transform, the agentic migration service. You can choose between two rehosting experiences. Use the AWS Transform MGN console for direct control over replication and cutover. Or use the AWS Transform agentic workflow, where an agent handles discovery, wave planning, landing zone setup, network creation, and rehosting or containerization on your behalf, accelerating your path to AWS. AWS Transform MGN retains all of its existing compliance certifications, including FedRAMP High, HIPAA, PCI DSS, ISO, and SOC 1, 2, and 3, so you can migrate with confidence. It is available in all commercial regions and both GovCloud (US) Regions. Visit the AWS Transform MGN product page and AWS Transform MGN documentation for more information on how to rehost applications to AWS.
Quelle: aws.amazon.com

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