In this chapter, you looked at the basic concepts and components of an Elasticsearch cluster.
After this, we discussed how Elasticsearch indexes a document using inverted index. We also discussed mapping and analysis techniques. We learned how we can denormalize an event before ingesting into Elasticsearch. We discussed how Elasticsearch uses horizontal scalability and throughput. After learning about Elasticstack components such as Beats, Logstash, and Kibana, we handled a live use case, where we demonstrated how access log events can be ingested into Kafka using Filebeat. We developed a code to pull messages from Kafka and ingest into Elasticsearch using Logstash. At the end, we learned data visualization using Kibana.
In the next chapter, we will see how to build analytics to design data visualization solutions that drive business decisions.
