The chapter we just finished was all about data analysis in Elasticsearch: the aggregations engine. We learned what the aggregations are and how they work. We used metrics, buckets, and newly introduced pipeline aggregations, and learned what we can do with them.
In the next chapter, we'll go beyond full text searching. We will use suggesters to build efficient autocomplete functionality and correct the users' spelling mistakes. We will see what percolation is and how to use it in our application. We will use the geospatial abilities of Elasticsearch and we'll learn how to efficiently fetch large amount of data from Elasticsearch.