Summary

In this section, we have dipped our toes into the world of Data Frame Analytics, a whole new branch of machine learning and data transformation tools that unlock powerful ways to use the data you have stored in Elasticsearch to solve problems. In addition to giving an overview of the new unsupervised and supervised machine learning techniques that we will cover in future chapters, we have studied three important topics: transforms, using the Painless scripting language, and the integration between Python and Elasticsearch. These topics will form the foundation of our future work in the following chapters.

In our exposition on transforms, we studied the two components – the pivot and aggregations – that make up a transform, as well as the two possible modes in which to run a transform: batch and continuous. A batch transform runs only once and generates a transformation on a snapshot of the source index at a particular point in time. This works perfectly for...