This is the second in a series of blog posts exploring the role that intelligent observability plays in the day-to-day life of smart teams. In this post, meet our clever ITOps engineer, James, as he reduces noise and distraction using intelligent observability.
I drive a 2005 Ford diesel pickup truck. Most of the time my truck runs great. But occasionally an orange light on the dashboard will flicker on to alert me that something is wrong. Unfortunately, there’s no information about what is wrong and why. My truck has a monitoring solution, but not an observability solution. In many cases, IT has the same problem as my truck.
Much has been said and written recently about observability. Sometimes the term is used interchangeably (and incorrectly) with visibility. Many software vendors are using the term observability, but there is little consensus on the definition. Let’s review exactly what observability means. Observability is a term from control theory that has been borrowed by vendors selling software for IT Ops.
One of the more delicate debates in the DevOps world is what observability has to do with monitoring. Is observability just a trendy buzzword that means the same thing as monitoring? Is observability an improved version of monitoring? Are monitoring and observability different types of processes that solve different problems? The answer to those questions depends in part on your perspective.
This article provides an introduction to API Observability and how it fits within the overall APIOps Cycles. Then, we will walk through an example of how to successfully deploy and leverage Tyk Gateway and Moesif API Observability on Amazon EC2.
All the work presented in this blog post is open source and available as part of our Splunk Connect for Ethereum repository examples, including the instrumentation of Besu as a Docker container, the configuration of Splunk, and two applications showing how to monitor Besu syncing to the chain.