In this chapter, we started with the general problems of structuring data in Elasticsearch and the different approaches to data modeling. We have shown you how relational data can be managed using nested and parent-child data types. We further discussed the aggregation module of Elasticsearch for data analytics purposes, including the concept of instant aggregation introduced in Elasticsearch 5.0, along with all four categories of aggregations, that is, metric, bucket, pipeline, and the latest matrix aggregation available in Elasticsearch.
Our next chapter will focus on topics for improving the user search experience using suggesters, which allows you to correct user query spelling mistakes and build efficient autocomplete mechanisms. In addition to that, you'll see how to improve query relevance by using different queries and the Elasticsearch functionality. Finally, the chapter will cover how to use synonyms with Elasticsearch.