In this chapter, you learned how to use Elasticsearch to build powerful analytics applications. We covered how to slice and dice the data to get powerful insight. We started with metric aggregation and dealt with numerical datatypes. We then covered bucket aggregation in order to find out how to slice the data into buckets or segments, in order to drill down into specific segments.

We also went over how pipeline aggregations work. We did all of this while dealing with a real-world-like dataset of network traffic data. We illustrated how flexible Elasticsearch is as an analytics engine. Without much additional data modeling and extra effort, we can analyze any field, even when the data is on a big data scale. This is a rare capability that's not offered by many data stores. As you will see in Chapter 7, Visualizing Data with Kibana, Kibana leverages many of the aggregations that we learned about in this chapter.

This concludes the chapters on Elasticsearch, the core of Elastic Stack, in this book. You now have a very strong foundation to learn about the rest of the ecosystem of Elastic Stack. Starting with the next chapter, we will shift our focus to learning about Logstash, which primarily deals with getting data into Elasticsearch from a variety of sources.