News For Dev-ops

Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow. You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also ...

See how GitHub Copilot added completions to its unified inline suggestions model, refined ghost text and editor behavior, and improved user satisfaction. Read the full article Post Updated on September 23, 2026 at 01:00AM Thanks for reading from devamazonaws.blogspot.com

AWS announces the general availability of Amazon CloudWatch Omni, an evolution of Amazon CloudWatch. Omni is an AI-powered observability experience organized around your teams and the applications they run, so that you can observe and troubleshoot your applications and agents in one place. It combines the interoperability of OpenTelemetry with the scale and reliability of CloudWatch. And it meets you wherever you work: a standalone web experience with single sign-on (SSO) for your team, or a local IDE extension for getting hands-on with your agents. With CloudWatch Omni, you create spaces in your central accounts to see telemetry across your AWS accounts and Regions, as well as other clouds, including Azure workloads. Omni automatically discovers services, maps dependencies, and surfaces golden metrics to help streamline your operational workflows. Using Omni, you can interact with telemetry however you prefer: via chat, through a guided point-and-click path in the console, or directl...

Amazon EMR 7.14 is now available with new features across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless, along with version upgrades for additional applications. This release also upgrades Apache Spark to 3.5.8 and Apache Iceberg to 1.10.1. Apache Iceberg materialized views now refresh faster on tables with updates and deletes, using change data capture to read only the data files affected by a change. On Amazon EMR on EKS, clusters now support Spark Connect endpoints for interactive Spark sessions with token-based authentication, and can run workloads on IPv6 Amazon EKS clusters. On Amazon EMR Serverless, the storage limit for Spark jobs increases from 200 GiB to 1 TiB, giving more room for shuffle data. Amazon EMR 7.14 is available in all AWS Regions where Amazon EMR is available. To learn more, visit the Amazon EMR 7.14 Release Guide , or get started by creating a cluster from the Amazon EMR console . Post Updated on September 22, 2026 at 09:00AM

C++ repositories can contain millions of lines of code spread across deeply connected source files and headers. Without a reusable index, navigating to definitions, finding references, or searching for symbols may require portions of that information to be analyzed again as you move through the repository. C++ code intelligence in GitHub Copilot CLI is now faster with support for Whole Codebase Indexing (WCI) . Powered by the Microsoft C++ Language Server , WCI uses your project’s compilation information to maintain a persistent index of symbols and relationships across source files and headers, allowing Copilot CLI to understand the codebase without repeatedly parsing it from scratch. Because the index runs directly within the CLI experience, an IDE does not need to be open to establish or maintain project understanding. Copilot CLI can use indexed declarations, definitions, references, implementations, includes, and type relationships when tracing call paths, assessing the impact o...

LangChain Deep Agents is an open-source agent harness with built-in capabilities for building LLM-powered agents and applications, including complex, multi-step workflows. A model can reason and generate responses, but it needs a harness to do useful work over time. The harness provides the tools and runtime that let the model retrieve the right context, take actions, and manage work across multiple steps. Deep Agents supplies that structure through planning, context management, a virtual filesystem, memory and skills, specialized subagents, and human approval points. Filesystems give agents a familiar way to organize and work with context. An agent can list and search files, read only what the current task needs, update artifacts, keep notes, and share work with subagents. This makes the filesystem useful as both a workspace and external memory for long-running tasks, a pattern LangChain highlights in its Deep Agents architecture . Deep Agents exposes this pattern through tools fo...