The inclusion of a stable test runner in Node.js(version 18+) has generated significant debate in the community, given the abundance of established third-party testing frameworks. While its arrival naturally sparks comparisons to existing tools, this article won't focus on justifying its place in the ecosystem. Instead, we'll embark on a hands-on exploration of the test runner's core capabilities, from writing and executing tests to organization and customization features.

Techbio company BioCorteX uses BigQuery to store its Carbon Knowledge and Carbon Mirror knowledge graphs to help develop drugs at scale.

Let’s start with a statement everyone can agree on: AI coding is now a permanent fixture of the software development lifecycle. Even if your team hasn’t introduced it officially yet, it’s only a matter of time before one of your peers kicks off the discussion or pushes a PR that, perhaps even unbeknownst to you, is entirely AI-generated. Now, let’s rub some folks the wrong way: AI coding in a microservices architecture is a recipe for disaster.

Exploratory testing is highly effective in uncovering hidden issues, but many teams need help when used to manual testing. Teams, products, and users constantly evolve, meaning risks impacting quality are not static or entirely predictable. This dynamic environment requires a flexible approach to identify and address potential issues. This blog post explains how to enhance your overall testing strategy by blending exploratory testing with your team's manual testing routines.

Last month at our Qlik Connect conference, I outlined a vision for combining the best of Qlik Data Integration, Talend, and Stitch to deliver market-leading data integration and quality solutions for your AI, analytics, and operational initiatives. The reception was overwhelmingly positive, and the live demonstrations generated huge excitement. Therefore, I am happy to share the news that Qlik Talend Cloud is now generally available (GA)!

Kafka is often chosen as a solution for realtime data streaming because it is highly scalable, fault-tolerant, and can operate with low latency even under high load. This has made it popular for companies in the fan engagement space, and where transactional data is used (e.g. betting) as low latency ensures that actions and responses happen quickly, maintaining the fluidity and immediacy of the experience.