Kategorie: Amazon AWS

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

Amazon SageMaker Unified Studio now supports a localized experience in twelve languages

Amazon SageMaker Unified Studio enhanced its global accessibility by introducing support for twelve languages across the user interface. Supported languages include English (American), Chinese (Simplified and Traditional), French, German, Indonesian, Italian, Japanese, Korean, Portuguese (Brazilian), Spanish, and Turkish. With this launch, data engineers, analysts, and data scientists across global teams can navigate, build, and collaborate in the language they are most comfortable with, reducing friction and improving productivity. Your preferred language is automatically detected based on your browser’s default language settings. You can also set your preferred language by choosing ‘Language selector’ in your profile settings and selecting the language. The selected language applies across the entire SageMaker Unified Studio user interface. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available, in both AWS IAM Identity Center-based and IAM-based domains. To learn more, visit the Amazon SageMaker Unified Studio documentation.
Quelle: aws.amazon.com

Amazon Keyspaces (for Apache Cassandra) now provides CDC iterator position

Amazon Keyspaces (for Apache Cassandra) now returns an iterator position in the GetRecords response for change data capture (CDC) streams, indicating whether a consumer has reached the tip of the stream or whether additional records may be available. Amazon Keyspaces is a scalable, serverless, and managed Apache Cassandra-compatible database service that lets customers run Cassandra workloads on AWS without managing infrastructure. CDC streams capture row-level changes to Keyspaces tables so customers can integrate with downstream analytics, replication, and event-driven applications.
Previously, customers polled CDC streams at a fixed cadence regardless of whether new records were available, leading to inefficient resource usage and unnecessary CDC consumption costs. With iterator position, customers can now adapt polling frequency based on whether the iterator is at the tip of the stream or has records pending, lowering CDC consumption costs while maintaining timely data processing. The GetRecords response now includes an iteratorDescription structure with an iteratorPosition field that returns either AT_TIP or BEHIND_TIP, enabling customers to optimize their data integration pipelines and event-driven architectures.
This feature is available in all AWS Regions where Amazon Keyspaces CDC is supported. To use it, customers need to update to the latest AWS SDK. To learn more, visit the Amazon Keyspaces product page and see Working with change data capture (CDC) streams in the Amazon Keyspaces Developer Guide.
Quelle: aws.amazon.com

ARC Region switch adds Amazon Aurora scaling and Amazon Neptune global database failover

Amazon Application Recovery Controller (ARC) Region switch helps customers orchestrate the failover of their multi-Region applications to achieve a bounded recovery time in the event of a Regional impairment. Today, we are announcing three new execution blocks — the Amazon Aurora serverless scaling execution block, the Amazon Aurora provisioned scaling execution block, and the Amazon Neptune global database failover execution block — which automate database scaling and failover for multi-Region workloads. Customers running Amazon Aurora global database in active-passive configurations typically maintain a scaled-down secondary cluster to minimize cost. During failover, they must manually right-size and scale the secondary cluster to handle production traffic before routing requests — adding critical minutes to recovery time. The new Amazon Aurora serverless and Amazon Aurora provisioned scaling execution blocks automate right-sizing and scaling the secondary cluster as part of the Region switch plan, so it’s ready for production traffic when failover completes. Customers running Amazon Neptune global database face a similar challenge: failover requires scripting or manually deciding whether to switchover or detach-and-promote depending on the outage type — all under the pressure of an active incident. The new Amazon Neptune global database failover execution block automates both planned switchover and unplanned failover scenarios within a single plan, eliminating custom scripting during recovery. All three blocks support cross-account orchestration, enabling a single plan to coordinate database operations across multiple accounts and Regions. To learn more, read documentation of Amazon Aurora provisioned scaling, Amazon Aurora serverless scaling and Amazon Neptune global database failover
Quelle: aws.amazon.com

OpenAI GPT-5.4 generally available on Amazon Bedrock in AWS GovCloud (US-West)

Amazon Bedrock now supports GPT‑5.4 from OpenAI in AWS GovCloud (US-West) — giving government and regulated industry customers access to OpenAI’s most capable frontier model for professional work, backed by the enterprise-grade security and goverment compliance scope of AWS GovCloud (US). 
GPT‑5.4 supports native computer-use capabilities, and deep reasoning across coding, documents, and multi-step agentic tasks — all running on Bedrock’s high-performance inference engine with isolated queues and durable state for fault-tolerant workloads. Your data stays in-partition and is never used to train models.
For Regional availability of  GPT-5.4 see the AWS Regions page. Read the launch blog to learn more, for documentation and a step-by-step walkthrough, see the Amazon Bedrock docs and the getting started blog.
Quelle: aws.amazon.com

AWS Step Functions adds AgentCore-powered agentic reasoning step

AWS Step Functions now enables you to add AI agent reasoning steps to your workflow through an optimized integration with the managed harness (currently in preview) in Amazon Bedrock AgentCore. AWS Step Functions is a visual workflow service that orchestrates AWS services with built-in error handling, parallel execution, and human approval steps. The AgentCore harness lets you declare an agent through configuration where you specify the model, tools, and behavior. AgentCore provides the managed environment that runs the agent loop end-to-end.
 
With this integration, you can automate reasoning tasks in your workflow such as classifying a document or extracting elements from an unstructured form. You can run multiple agents in parallel or in sequence at different decision points in a single workflow and add human approval before critical actions. The workflow execution history shows agent input, output, token usage, and duration with links to agent turn details in Amazon CloudWatch, so you can trace and audit every agent decision. You can reuse an existing harness or create a new one directly from the Workflow Studio, the Step Functions visual builder. With per-invocation overrides such as the model, system prompt, and tools, you can adapt the agent to each workflow context without duplicating configurations. Agent context can be persisted across invocations using a session ID that works within or across workflow executions.
 
The harness integration is available in the following AWS Regions where the AgentCore harness preview is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Asia Pacific (Sydney). Standard Step Functions pricing applies for workflow execution with no additional integration charges, and standard Amazon Bedrock and AgentCore pricing applies for model inference and associated AgentCore resources.
 
To learn more about adding agentic reasoning to your workflows, visit AWS Step Functions documentation. 
Quelle: aws.amazon.com