Tag: SysAdmin

AWS Glue Interactive Sessions now support Apache Spark Connect, using which you can now develop and run Apache Spark applications from your preferred environment, including managed notebooks in Amazon SageMaker Unified Studio, or your preferred notebook environments and IDEs like Jupyter, Visual Studio Code, while running them on AWS Glue's serverless infrastructure without managing clusters. With Spark Connect, you submit Spark jobs to AWS Glue Interactive Sessions using a thin client architecture that decouples your client application from the Spark execution environment. This unlocks workflows like ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production, all from the tools you already use. Spark Connect also simplifies upgrades and improves stability by isolating client dependencies from the server-side Spark runtime. For observability, you get real-time session monitoring via the Spark UI, history

AWS HealthOmics now streams workflow engine logs to Amazon CloudWatch in real time, enabling customers to monitor workflow execution progress as it happens. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. Real-time engine log streaming accelerates iterative workflow development and debugging by giving researchers, bioinformaticians, and workflow developers immediate access to execution details during a run. The streamed engine logs provide visibility into workflow orchestration events, task scheduling details, import/export activity, and full stack traces on errors — all routed into the engine log stream in real time. Customers can set up CloudWatch alarms on log patterns to detect anomalies early, build dashboards for ongoing monitoring, and integrate with existing observability tooling.

Amazon Aurora MySQL-Compatible Edition and Amazon Relational Database Service (RDS) for MySQL now offer Amazon RDS Extended Support for MySQL 5.7 through June 30, 2029, from the previous end date of February 28, 2027. This applies to Aurora MySQL version 2 (with MySQL 5.7 compatibility) and RDS for MySQL version 5.7, giving customers additional time to plan and complete their upgrades to a supported major version while continuing to receive critical security patches and bug fixes. RDS Extended Support delivers security patches for critical and high CVEs, bug fixes for critical operational issues, and access to AWS Support within the standard Aurora and RDS SLAs. There is no price increase with this extension, and customers using RDS Extended Support for MySQL 5.7 will continue to pay Year 3 pricing through June 30, 2029. For pricing details, see

AWS Graviton5-based M9g database (DB) instances are now generally available for Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB. Graviton5-based instances provide up to a 30% performance improvement and up to a 23% price/performance improvement for on-demand pricing over Graviton4-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload. AWS Graviton5 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. M9g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, M9g DB instances offer up to 192 vCPU, up to 100Gbps enhanced networking bandwidth, and up to 72Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). These instances are now available in the US East (N. Virginia, Ohio), US West (Or

AWS DevOps Agent now offers a release management capability in preview, reviewing code changes for release readiness and running autonomous release testing to help you ship code to production safely and with confidence. With this addition, AWS DevOps Agent now works across both delivery and operations. It accelerates and validates the deployment of code changes, then keeps your applications running optimally across AWS, multicloud, and on-prem environments, so your team ships faster, reduces MTTR, and achieves operational excellence. With release readiness review, AWS DevOps Agent evaluates code changes for production safety during code generation by checking for drift from your internal standards, dependency impacts, and access controls. It maps cross-repository dependencies to surface breaking changes before commit and uses deterministic proofs to review that infrastructure changes do not drift from AWS Well-Architected best practices. With release testing, AWS DevOps Age

AWS announces the availability of bmn-cx3a instances on second-generation AWS Outposts racks. Bmn-cx3a instances feature 5th Gen AMD EPYC processors with a maximum frequency of 4.1 GHz and NVIDIA ConnectX-7 (CX7) network interface cards, delivering up to 800 Gbps of bare-metal accelerated network bandwidth operating at near line rate. Bmn-cx3a instances offer up to 256 cores and 1.5 TB of memory across two sizes, bmn-cx3a.metal-32xl and bmn-cx3a.metal-64xl, with 2x 8 TB NVMe SSD storage. With native Layer 2 (L2) multicast and hardware Precision Time Protocol (PTP) support, bmn-cx3a instances are designed for high-throughput workloads such as real-time market data ingestion and distribution, market and risk analytics, telecom 5G core network applications, and media distribution. Bmn-cx3a instances on AWS Outposts racks are available in all countries and regions where second-generation Outposts racks are supported. For a current list of AWS Regions and countries/terr

Today, AWS announces multiple new features for Amazon Quick, including autonomous agents, multi-dataset analytics capabilities, and a redesigned activity feed. Amazon Quick is the AI assistant that connects to popular business applications and learns user workflows. These new capabilities enable Quick to handle recurring tasks continuously while providing unified analytics across multiple data sources. With autonomous agents, users can describe tasks in natural language and set granular autonomy levels—from step-by-step approval to broad goal-based execution. Agents operate continuously to automate workflows like following up on stalled deals, summarizing regulatory changes, and processing purchase orders, eliminating manual repetitive work and notification overload. The new multi-dataset analytics feature enables users to query across data sources including Snowflake and relational databases using natural language, without requiring technical data preparation or pre-

Today, AWS announces the preview of business context and semantic search for AWS Glue Data Catalog, helping you discover and understand data by semantic meaning. You can now enrich your Glue Data Catalog tables, including those backed by S3 Tables, with glossary terms and custom metadata fields. You can also add skills to the catalog that direct agents to additional context about your data. With business context indexed alongside technical metadata, you can use the new Glue Search API to find data by semantic meaning, and ground your AI agents in trusted definitions rather than inferred context. You can use the new search capability to find tables in the catalog both by their structure, such as schema and table format, and by the business meaning you attach through glossary terms and descriptive metadata fields. This means an analyst exploring data or an agent reasoning about it can retrieve a table's definition, what its data represents, and how to use it correctly, in a si

Today, AWS announces the general availability of the managed agent harness in Amazon Bedrock AgentCore, taking teams from idea to working agents in minutes. An agent is more than a model. If the model is the brain, the harness is the body: everything the brain needs to get work done. It runs the orchestration loop, executes tools, manages the context window, persists state across turns, recovers from failures, and isolates each session. The harness shapes how well an agent performs as much as the model does, and building a durable one is where most teams spend their time today. AgentCore harness provides that layer as a managed capability. Instead of coding the loop, customers define an agent in configuration: the model it uses, the tools it calls, the skills it accesses, and the instructions it follows, and AgentCore assembles and runs that loop. From that single definition, a production-grade agent runs in minutes in its own isolated environment, with a filesystem and shell, memor

Today, AWS announces that Amazon Bedrock AgentCore now supports Bedrock Guardrails in policy, giving enterprises deeper safety and security controls as they scale AI agents in production. AgentCore policy is an authorization capability within Amazon Bedrock AgentCore that controls which actions AI agents are authorized to take. Guardrails give enterprises defenses against the top security and safety risks with AI agent workloads, including prompt injection attacks and sensitive data exposure. Guardrails can evaluate the outputs of every authorized agent action and inputs of every call to a gateway target (tools, agents, and models) in real-time, helping detect and block prompt injection attacks, harmful content, and sensitive information exposure before they reach downstream systems. Guardrail results are evaluated in policy at the AgentCore gateway perimeter, outside the agent's code, ensuring consistent enforcement regardless of agent autonomy. All policy evaluations are