Systems | Development | Analytics | API | Testing

ETL (Extract, Transform, Load) frameworks have evolved significantly over the past two decades. In 2025, as data pipelines expand across cloud platforms, real-time systems, and regulatory constraints, the architecture and flexibility of ETL frameworks are more critical than ever. This post explores the key principles, features, and operational concerns that modern data professionals need to understand to build effective, scalable ETL frameworks for data engineering use cases.

Testing software is a lot like checking your homework before turning it in. You want to make sure everything works, and you didn’t make any mistakes. In software, there are many kinds of tests, but two of the most common are unit testing and functional testing. Whether you’re a business owner, a developer, a student, or part of a web agency or DevOps team, knowing the difference can help you build better apps, websites, or tools. Is Your Infrastructure Ready for Global Traffic Spikes?

Test automation is an essential component when it comes to delivering high-quality software. However a lot of QA teams face challenges when it comes to deciding which test cases they need to prioritize for automation. The selection process can be hard, especially if you consider the limited resources that these teams sometimes have and complexity of modern applications.

Between the relentless pressure to ship new features and updates, and the increasing adoption rates of DevOps, AI’s influence in the future of the testing landscape is palpable, and the focus in the coming years will be applying that to QA teams everywhere.

When evaluating automation testing companies, you’ll notice most promise the same benefits: faster release cycles, fewer bugs in production, and less testing work for your development team. But in our experience, many fall short. They bottleneck releases, miss bugs users immediately notice, and still rely on your developers to write and maintain tests.

Everyone is taking part in the AI scramble, some more successfully than others, as we all know. But many enterprise companies are still just getting their feet wet. Here at Qlik we saw an opportunity to try to capture what it is that these successful companies did to get value from AI for their organizations. So, we partnered with IDC to get those answers and discovered that preparation is a major difference maker for success with AI.