Chapter 1. Introduction to Data-Driven Architecture

If you are reading this book, it certainly means that you and I have something in common: we are both looking for a solution to effectively visualize and understand our data.

Data can be anything: business data, infrastructure data, accounting data, numbers, strings, structured, or unstructured. In any case, all organizations reach a point where trying to understand data and extract the value of it begins to be a real challenge, for different reasons:

The good news is that you won't need to learn it the hard way, as I'll try in this book to explain how I've tackled data analytics projects for different use cases and for different types of data based on my experience.

The other good news is that I'm part of the Solutions Architecture (SA) team at Elastic, and guess what? We'll use the Elastic stack. By being part of the SA team, I'm involved in a variety of use cases, from small to large scale, with different industries; the main goal is always to give to our users better management of and access to their data, and a better way to understand their data.

In this book, we'll dig into the use of Kibana, the data analytics layer of the Elastic stack. Kibana is the data visualization layer used in an overall data-driven architecture.

But what is data-driven architecture? This is the concept I will illustrate in this chapter by going through industry challenges, the usual technology used to answer this need, and then we'll go into the description of the Elastic stack.