Learn about the technical challenges involved in building an idempotent data pipeline.

When I joined the k6 team in late November, the foremost question on my mind was "How does this compare to JMeter?" You see, I come from a performance testing consulting background, and in the last few years, JMeter has been far and away my tool of choice. So what am I doing on the k6 site, talking about how much I love JMeter? Well, firstly, it turns out that the k6 team is pretty open and transparent, especially about potential improvements in k6.

Performance testing has become increasingly distributed, as teams do more testing at each stage of the software development life cycle. While the business benefits of performance testing are undeniable, like finding defects earlier when it’s easier and less expensive to fix them — it makes managing all your tests more challenging.

You’ve built an API to solve technical problems, but you know that’s just the beginning. In addition to helping developers use it, you need to understand how they use it. You want to measure its performance and popularity, and make adjustments based on what you discover.

What are the most important database performance metrics, and how do you monitor them? This is a question many IT professionals would like the answer to. We can collect and use a wide range of database metrics to analyze database and server resource consumption, not to mention overall usage. You are probably wondering why this is essential for business, so let’s explore this next.

As all developers know, when building software things don’t always go as planned. In fact, most of the time they don’t. With today’s modern distributed architectures it’s more important than ever to have the proper tools in your toolbelt. This allows us to automate as much of the software delivery lifecycle as possible and then be able to immediately triage issues when they arise.