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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

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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

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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
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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.
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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. 
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