Tag: SysAdmin

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, a

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 con

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 f

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 b

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

AWS Compute Optimizer now lets you extend the lookback period of your Amazon EBS volume and Amazon ECS rightsizing recommendations from the default 14 days to 32 days, at no additional cost. A longer lookback period allows Compute Optimizer to account for monthly utilization patterns, such as month-end processing, when generating rightsizing recommendations. This can help you make better optimization decisions for your workload, leading to better cost and performance outcomes. AWS Compute Optimizer supports 32-day lookback periods for five types of recommendations: EC2 instance, EC2 Auto Scaling group, RDS database, EBS volume, and ECS service. You can set the lookback period at the organization, account, or resource level through the console, AWS SDK, or AWS CLI. This feature is available in all AWS Regions where AWS Compute Optimizer is available, except the AWS GovCloud (US) Regions and the China Regions. To learn more, see the AWS Compute Optimizer

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 a

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 p

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 Ident

Amazon Elastic Container Service (Amazon ECS) Managed Instances now supports AWS Trainium and AWS Inferentia, purpose-built AI accelerators designed to deliver scalable performance and cost efficiency for training and inference across a broad range of generative AI workloads. Amazon ECS Managed Instances is a fully managed compute option designed to eliminate infrastructure management overhead while giving you access to the full capabilities of Amazon EC2. By offloading infrastructure operations to AWS, ECS Managed Instances helps you quickly launch and scale your workloads, while enhancing performance and reducing your total cost of ownership. With ECS Managed Instances, you get the application performance you want and the simplicity you need. Now you can create an ECS Managed Instances capacity