#orchestration-platform

#orchestration-platform

With this feature, you can bring entire folders, ML models, dbt adapters, utilities, directly into UDxFs and Stored Procedures without zipping, file-by-file bookkeeping, or manual updates.

Apache Kafka has cemented itself as the de facto platform for event streaming, often referred to as the 'universal data substrate' due to its extensive ecosystem that enables connectivity and processing capabilities.

If Ingress is the Legacy Path, then the Gateway API is the modern highway. In this guide, I will walk you through a complete migration demonstrating how to swap out your old Ingress controllers for Envoy Gateway. We won't just move traffic; we'll leverage Envoy's power to implement seamless request mirroring and more robust, path-based routing that was previously hidden behind complex annotations.

Building APIs is so simple. Caveat, it's not. Actually, working with tools with no security, you've got a consumer and an API service, you can pretty much get that up and running on your laptop in two or three minutes with some modern frameworks. Then, authentication and authorization comes in. You need a way to model this.

Red Hat AI Enterprise provides a foundation for modern AI workloads, including AI life-cycle management, high-performance inference at scale, agentic AI innovation, integrated observability and performance modeling, and trustworthy AI and continuous evaluation. Tools are provided for dynamic resource scaling, monitoring, and security.

"For healthcare, government, and contact center environments, reducing risk at the endpoint is essential. By aligning IGEL's immutable endpoint OS and Adaptive Secure Desktop™ with Windows 365 and Microsoft Azure Virtual Desktop, these reference architectures give organizations clear guidance for delivering secured and resilient digital workspaces."

Almost a quarter of those surveyed said they had experienced a container-related security incident in the past year. The bottleneck is rarely in detecting vulnerabilities, but mainly in what happens next. Weeks or months can pass between the discovery of a problem and the actual implementation of a solution. During that period, applications continued to run with known risks, making organizations vulnerable, reports The Register.

A North American manufacturer spent most of 2024 and early 2025 doing what many innovative enterprises did: aggressively standardizing on the public cloud by using data lakes, analytics, CI/CD, and even a good chunk of ERP integration. The board liked the narrative because it sounded like simplification, and simplification sounded like savings. Then generative AI arrived, not as a lab toy but as a mandate. "Put copilots everywhere," leadership said. "Start with maintenance, then procurement, then the call center, then engineering change orders."

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.

An observability control plane isn't just a dashboard. It's the operational authority system. It defines alert rules, routing, ownership, escalation policy, and notification endpoints. When that layer is wrong, the impact is immediate. The wrong team gets paged. The right team never hears about the incident. Your service level indicators look clean while production burns.

Over the past decade, software development has undergone a massive transformation due to continuous innovations in tools, processors and novel architectures. In the past, most applications were monoliths and then shifted to microservices, and now we find ourselves embracing composability - a paradigm that prioritizes modular, reusable, and flexible software design. Instead of writing separate, tightly coupled applications, developers now compose software using reusable business capabilities that can be plugged into multiple projects. This enables greater scalability, maintainability, and collaboration across teams and organizations. At the heart of this movement is Bit Harmony, a framework designed to make composability a first-class citizen in modern web development.

While building apps I learned that writing code is only half the journey - getting it deployed, updated, and running reliably is also just as important if not more. When I started deploying my apps to the cloud, I realized how many manual steps it took to get the app running. That's when I discovered CI/CD and GitOps tools that automate everything from testing to deployment, so developers can focus on writing code instead of wasting time on manually deploying each time.

The Harness Resilience Testing platform extends the scope of the tests provided to include application load and disaster recovery (DR) testing tools that will enable DevOps teams to further streamline workflows.

Steve Yegge thinks he has the answer. The veteran engineer - 40+ years at Amazon, Google and Sourcegraph - spent the second half of 2025 building Gas Town, an open-source orchestration system that coordinates 20 to 30 Claude Code instances working in parallel on the same codebase. He describes it as "Kubernetes for AI coding agents." The comparison isn't just marketing. It's architecturally accurate.

The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.

Docker builds images in layers, caching each one.When you rebuild, Docker reuses unchanged layers to avoid re-executing steps - this is build caching. So the order of your instructions and the size of your build context have huge impact on speed and image size. Here are the quick tips to optimize and achieve 2 times faster speed building images: 1. Place least-changing instructions at the top

Percona recently announced OpenEverest, an open-source platform for automated database provisioning and management that supports multiple database technologies. Launched initially as Percona Everest, OpenEverest can be hosted on any Kubernetes infrastructure, in the cloud, or on-premises. The main goal of the project is to avoid vendor lock-in while still providing an automated private DBaaS. Built on top of Kubernetes operators, it aims to avoid complex deployments that depend on a single cloud provider's technology.