DevOps

DevOps

In Part two, we examined secure by design principles, with a approach, secure access service edge (SASE), and quantum-safe planning becoming non-negotiable foundations for the next decade. Automation is another pivotal strand to the change that's taking place. Instead of relying on manual command-line interfaces (CLI), tomorrow's networks will be defined by code, workflows, and application programming interfaces (APIs). From infrastructure as code (IaC) and observability to evolving skillsets, automation is not just about efficiency - it is becoming the DNA of modern networking.

Microsoft has released Azure Kubernetes Service (AKS) Automatic to general availability, introducing a fully managed Kubernetes offering designed to eliminate operational overhead while maintaining the full power and flexibility of the platform. The service represents Microsoft's answer to what the company calls the "Kubernetes tax"-the significant time and expertise traditionally required to configure, secure, and maintain production-grade clusters. AKS Automatic differentiates itself by providing production-ready clusters through intelligent defaults and automated operations.

One of the highlights Levi pointed to was AppTrust, JFrog's initiative to establish end-to-end trust across the software supply chain. By unifying governance, risk, and compliance capabilities into a single framework, AppTrust is designed to give enterprises more confidence that applications are secure and reliable from development through deployment. The goal is to tie disparate security and verification processes into one cohesive approach that simplifies how organizations enforce trust at scale.

Designed to be integrated with continuous integration/continuous deployment (CI/CD) platforms such as Jenkins and others, the Zencoder AI agent can resolve issues, implement fixes, improve code quality, generate and run tests, and create documentation. As such, the goal is not just to write more code faster, but rather enable DevOps teams to take advantage of AI agents running in the background to re-engineer workflows in ways that result in more applications being deployed faster, said Filev.

Airbnb has developed Impulse, an internal load testing framework designed to improve the reliability and performance of its microservices. The tool enables distributed, large-scale testing and allows engineering teams to run self-service, context-aware load tests integrated with CI pipelines. By simulating production-like traffic and interactions, Impulse helps engineers identify bottlenecks and errors before changes reach production. According to the Airbnb engineering team, Impulse is already in use in several customer support backend services and is under review for broader adoption.

Apache Airflow, a leading open-source orchestration framework, provides the structure and flexibility required to implement complex GenAI workflows with relatively basic Python code. In this tutorial, you'll learn how to use Airflow to orchestrate a basic RAG pipeline that includes embedding book descriptions with the OpenAI API, ingesting the embeddings into a PostgreSQL database with pgvector installed, and querying the database for books that match a user-provided mood.

Redis Enterprise 7.2 comes to its official end of life in February 2026, so what should users do in this situation and what lessons can they take away for the end-of-life management experiences that they will inevitably experience with other platform and tools? Redis is good, but when a version update drives users into an alleyway, what should they do? As an open source, in-memory data store known for its ability to act as a distributed cache, message broker and database, Redis is lauded for its high-performance, low-latency read/write speeds achieved through memory data storage. Come February next year, Redis software application developers, data science professionals and other connected operations staff will need to have been doing some prudent planning.

After switching to Karpenter with about 70% spot instance usage, our monthly compute costs dropped by 70%. That's a significant reduction that freed up substantial budget for new features and infrastructure improvements. Burninova's implementation involved replacing the traditional Kubernetes Cluster Autoscaler with Karpenter, and also moving to multi-architecture setup with both AMD64 and ARM64 instances.

Enhanced root cause analysis- When issues occur, having access to complete, unsampled data dramatically improves your ability to identify root causes and troubleshoot issuesquickly. Instead of extrapolating from sampled data points, your team can analyze the full context of system behavior leading up to and during incidents. Eliminating cardinality constraints- Teams can focus on analysis of key historical data to predict and prevent future occurrences rather than complex data preprocessing, multiple monitoring tiers, or custom aggregation logic.

Snowflake Trail enhances observability by allowing developers to monitor data quality, pipelines, and applications, ultimately improving workflow optimization and troubleshooting capabilities.

A new platform named kubriX has been launched into the developer community, claiming to create a fully functional Internal Developer Platform (IDP) without extensive custom development. The platform, developed by contributors including developer advocate Artem Lajko, who has written an extensive post about it, integrates established tools such as Argo CD, Kargo, Backstage, and Keycloak into what its creators describe as a ready-to-use solution for teams seeking to implement a modern IDP.

Businesses worldwide have pushed cloud computing spending to $912.77 billion in 2025 up from $156.4 billion in 2020. But here's the interesting thing: businesses are no longer merely making the switch to cut costs. They're chasing operational nimbleness that on-premises infrastructure cannot keep up with. It's a shift that follows how entertainment platforms like download 1xbet app Saudi Arabia utilize distributed computing to support millions of simultaneous users across different geographic locations.

Let's dig into what this really means, why it matters, and where we go from here. But then I thought a bit more. It's not just necessary-it's overdue. And not only for national security systems. This gap in software understanding exists across nearly every enterprise and agency in the public and private sector. The real challenge is not recognizing the problem. It's addressing it early, systemically and sustainably-especially in a DevSecOps context.

The implementation of OpenTelemetry is designed to enable DevOps teams with a simple line of code to begin collecting telemetry data from multiple applications that yield actionable insights in less than five minutes.

A stack-based approach to IDPs emphasizes reusability, autonomy, and visibility, creating a standardized but flexible system where teams can define and deploy their own devops stack.