In this chapter, we learned about one of the most powerful features of Elasticsearch, that is, aggregation frameworks. We went through the most important metric and bucket aggregations along with examples of doing analytics on our Twitter dataset with Python and Java API.

This chapter covered many fundamental as well complex examples of the different facets of analytics, which can be built using a combination of full-text searches, term-based searches, and multilevel aggregations. Elasticsearch is awesome for analytics but one should always keep in mind the memory implications, which we covered in the last section of this chapter, to avoid the over killing of nodes.

In the next chapter, we will learn to work with geo spatial data in Elasticsearch and we will also cover analytics with geo aggregations.