DevOps

DevOps

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.

In complex systems, failure isn't a possibility - it's a certainty. Whether it's transactions vanishing downstream, a binary storage outage grinding builds to a halt, or a vendor misstep cascading into a platform issue, we have all likely seen firsthand how incidents unfold across a wide range of technical landscapes. Often, the immediate, apparent cause points to an obvious suspect like a surge in user activity or a seemingly overloaded component, only for deeper, blameless analysis to reveal a subtle, underlying systemic flaw that was the true trigger.

AI's introduction as a dynamic factor in development enhances coding speed and quality, yet it poses new challenges to system stability, prompting developers to adapt.

Edge computing is evolving into a battleground where speed must be balanced with stringent security demands, exemplified by enterprises like Chick-fil-A leveraging Kubernetes for efficiency.

"The developer tool stack and product architecture has fundamentally changed in the AI era."