Systems | Development | Analytics | API | Testing

Choosing the right test management tool is never simple, as it involves much more than creating test cases or organizing your tests into executions. We’re talking about a tool that will support the entire QA process, from connecting to your requirements to defect processing to reporting and everything in between. The two tools usually evaluated for test management within Jira are Xray and Zephyr.

As businesses race to become data-driven, the ability to quickly build and iterate on data workflows is more critical than ever. Traditional ETL and ELT processes, while powerful, often require extensive coding, long development cycles, and high maintenance overhead. Enter low-code data pipelines: a modern, visual-first paradigm enabling faster development, broader accessibility, and better scalability.

Enterprise data pipelines are no longer mere support systems—they are strategic assets central to analytics, compliance, and operational intelligence. This article offers a comprehensive overview of how enterprise ETL pipelines work, the technologies involved, common challenges, and best practices for implementation at scale in 2025.

A few weeks ago during a sprint, our QA team flagged a frustrating issue: a Playwright test that passed locally, failed in CI, then passed again all without any code change. It was slowing us down and shaking confidence across the team. Digging deeper, we found what many engineers face: Flaky tests caused by bad timing, unstable selectors, and missed auto-wait features. In fast-moving CI/CD pipelines, these issues went unnoticed until they broke builds.

Data AI Gateways are reshaping how businesses manage APIs by automating key processes like creation, security, and scaling. These platforms simplify API operations, reduce costs, and improve efficiency, making them essential for enterprises navigating AI adoption. Here's what you need to know: What They Do: Automatically generate APIs, enforce security (e.g., RBAC), and integrate multiple databases. Why They Matter: Tackle challenges like siloed systems, scaling, and AI governance.

DevOps is the delivery process that focuses on the cross-functional approach of building and shipping applications in a faster manner through automation of infrastructure, workflow as well as performance evaluation. One of the most essential components of DevOps is CI/CD, which acronym for continuous integration (CI) and continuous delivery (CD).

Django is a powerful web framework that simplifies how developers interact with databases through its Object-Relational Mapping (ORM) system. However, even with its benefits, it’s easy to fall into performance pitfalls such as the N+1 query problem. In this article, we’ll explore what N+1 queries are, why they can be an issue for your application, and how to mitigate them using Django’s best practices. Let's dive in!

Ask any JavaScript developer their most used debugging tool and chances are the answer will be console.log. It’s immediate, low friction, and available in every browser. For development, it’s fantastic. But for production and complex applications, if you rely on console.log alone, cracks begin to show. It lacks context, doesn’t persist, and makes reproducing or analyzing user-reported issues a challenge. In this article, we’ll look at smarter, scalable debugging strategies.

Today, I'm excited to announce one of our largest Kong AI Gateway releases (3.11), which ships with several new features critical in building modern and reliable AI agents in production. We strongly recommend updating to this version to get access to the latest and greatest that AI infrastructure has to offer.

We’re excited to bring you Kong Gateway Enterprise 3.11 with compelling new features to make your APIs and event streams even more powerful, including: We’ll also touch on what’s new with Konnect networking and Active Tracing. There’s a lot to unpack, so keep on reading for the full story!