In this chapter, we have learnt how to use Elasticsearch to build powerful analytics applications. We have covered how to slice and dice the data to get powerful insight. We started with metric aggregation to deal with numeric datatypes. We then covered bucket aggregation to find out how to slice the data into buckets or segments in order to drill down into specific segments.
We also understood how pipeline aggregations work. We did all of this while dealing with a real-world-like dataset of network traffic data. We have seen how flexible Elasticsearch is as an analytics engine. Without much additional data modelling and extra effort, we can analyze any field, even when the data is on a big data scale. This is a rare capability 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 learnt about in this chapter.
This concludes the chapters on Elasticsearch, the core of Elastic Stack. We have a very...
