Tag: SysAdmin

Amazon SageMaker Unified Studio now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete picture of data transformations and dependencies across your entire lineage graph, helping you quickly identify all upstream sources and downstream consumers of your datasets. Previously, SageMaker Unified Studio showed the lineage graph as it existed at a specific point in time, which is useful for troubleshooting and investigating specific data processing events. The aggregated view now provides a complete picture of data transformations and dependencies across multiple levels of the lineage graph. You can use this view to understand the full scope of jobs impacting your datasets and to identify all upstream sources and downstream consumers. The aggregated view is available as the default lineage view in Amazon SageMaker Unified Stu

AWS Blu Insights capabilities are now available as part of AWS Transform, enabling customers to launch mainframe refactoring projects from the AWS Transform console. This launch unifies all three mainframe modernization patterns — refactor, replatform, and reimagine — within AWS Transform for mainframe. Code transformation is now offered at no cost, replacing the previous lines-of-code based pricing model. With this launch, you can access AWS Transform for mainframe refactor directly from the AWS Transform console using your existing AWS credentials. The mandatory three-level certification requirement to access the Transformation Center has been removed, lowering the friction to exploring refactor projects. Self-paced training content remains available within the application for those who want to build deeper knowledge. AWS Transform for mainframe refactor is available in 18 AWS Regions. In reg

SageMaker Training Plans allows you to reserve GPU capacity within specified time frames in cluster sizes of up to 64 instances. Today, Amazon SageMaker AI announces that Training Plans can now be extended when your AI workloads take longer than anticipated, ensuring uninterrupted access to capacity. You can extend plans by 1-day increments up to 14 days, or 7-day increments up to 182 days (26 weeks). Extensions can be initiated via API or the SageMaker console. Once the extension is purchased the workload continues to run un-interrupted without you needing to reconfgure the workload. SageMaker AI helps you create the most cost-efficient training plans that fits within your timeline and AI budget. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the AI workloads on these compute resources without requiring an

You can now create provisioned Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters with Express brokers in Africa (Cape Town) and Asia Pacific (Taipei) regions. Express brokers are a new broker type for Amazon MSK Provisioned designed to deliver up to 3x more throughput per broker, scale up to 20x faster, and reduce recovery time by 90% as compared to standard Apache Kafka brokers. Express brokers come pre-configured with Kafka best practices by default, support all Kafka APIs, and provide the same low-latency performance that Amazon MSK customers expect, so they can continue using existing client applications without any changes. To get started, create a new cluster with Express brokers through the Amazon MSK console or the Amazon CLI and read our Amazon MSK Developer Guide for more information.

Amazon Bedrock AgentCore Runtime now supports InvokeAgentRuntimeCommand, a new API that lets you execute shell commands directly inside a running AgentCore Runtime session. Developers can send a command, stream the output in real time over HTTP/2, and receive the exit code — without building custom command execution logic in their containers. AI agents often operate in workflows where deterministic operations such as running tests, installing dependencies, or executing git commands need to run alongside LLM-powered reasoning. Previously, developers had to build custom logic inside their containers to distinguish agent invocations from shell commands, spawn child processes, capture stdout and stderr, and handle timeouts. InvokeAgentRuntimeCommand eliminates this undifferentiated work by providing a platform-level API for command execution. Commands run inside the same container, filesystem, and environment as the agent session, and can execute concurrently with agent invoca

Amazon Corretto 26, a Feature Release (FR) version, is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. You can download Corretto 26 for Linux, Windows, and macOS from our downloads page. Corretto 26 will be supported through October 2026. HTTP/3 Support - Java applications can now use the latest HTTP/3 protocol, which is faster and more efficient than older HTTP versions (JEP 517) Ahead-of-Time Object Caching - Applications can start up faster by pre-caching commonly used objects, working with any garbage collector (JEP 516) Enhanced Pattern Matching - Developers can write cleaner code when checking

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Additional Storage Volumes, Resource Governor, and SQL Server 2019 with SQL Server Developer Edition. SQL Server Developer Edition is an ideal choice to build and test applications because it includes all the functionality of Enterprise edition, and is free of license charges for use as a development and test system, not as production server. You can use Additional Storage Volumes to your Amazon RDS for SQL Server Developer Edition instances, which provide you up to 256 TiB, 4X more storage. You can also use SQL Server Resource Governor, which lets you manage workload and resource consumption by defining resource pools and workload groups to control CPU and memory usage, enabling more realistic performance testing. Amazon RDS for SQL Server Developer Edition now also supports SQL Server 2019 (CU32 GDR - 15.0.4455.2), so you can mat

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 select AWS Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation.

Starting today, customers can use Amazon Bedrock in the Asia Pacific (New Zealand) Region to easily build and scale generative AI applications using a variety of foundation models (FMs) as well as powerful tools to build generative AI applications. Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models (LLMs) and other FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, OpenAI, Stability AI, as well as Amazon via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustainable growth from generative AI while maintaining privacy and security.

Amazon CloudWatch now allows customers to automatically enable Amazon Elastic Compute Cloud (EC2) detailed monitoring across their AWS Organization. Customers can create enablement rules in CloudWatch Ingestion that automatically enable detailed monitoring for both existing and newly launched EC2 instances matching the rule scope, ensuring consistent metrics collection at 1-minute intervals across their EC2 instances. EC2 detailed monitoring enablement rules can be scoped to the whole organization, specific accounts, or specific resources based on resource tags to standardize the configuration across EC2 instances. For example, the central DevOps team can create an enablement rule to automatically turn on detailed monitoring for EC2 instances with specific tags, e.g., env:production, and ensure Auto Scaling policies respond quickly to changes in instance utilization. CloudWatch's auto-enablement capability is available in all AWS commercial regions. Detailed monitori