The Three Types of Observability Your System Needs
This article is written in partnership with Kyle Kirwan, Co-founder and CEO at data observability platform Bigeye
What Is Observability?
In 1969, humans first stepped on the moon thanks to a lot of clever engineering and 150,000 lines of code. Among other things, this code enabled engineers at mission control to have the full view of the mission and make near-real-time decisions. The amount of code was so small that engineers were able to thoroughly debug the software, and its performance was nearly flawless. Today’s search engines, on the other hand, execute billions of lines of code, making it much more likely that engineers will make mistakes or extraordinary circumstances will crop up somewhere in the system. On top of that, both data and machine learning models (code that computers essentially write for themselves) come with their own challenges. In short, it is nearly impossible to guarantee that a modern software system will always perform as you expect. That’s why observability is crucial on all production systems.
Luckily, we can automate observability of complex software systems so you can have timely and actionable information about your software, just like NASA engineers had about the Apollo missions. Observability is the practice of quickly finding out what the problem is and getting to the root cause. Most modern software systems include infrastructure, data, and machine learning models. All three need observability, but each has different requirements, workflows, and personas. Let’s take a closer look!