In this chapter, we discussed the MapReduce programs by going through the WordCount program. We checked how to develop the MapReduce jobs with the new and old map-reduce APIs. We delved into the details of a real-world network logs monitoring problem. You learned how to solve the problem in a better way by using the aggregation capabilities of Elasticsearch.

Further, you learned how to write and build the Hadoop MapReduce job that leverages ES-Hadoop to get the network logs monitoring data to Elasticsearch. Finally, we explored how to get the data out from Elasticsearch in the MapReduce job for the Twitter dataset. Overall, we got a complete understanding of how to get the data in and out between Elasticsearch and Hadoop.

In the next chapter, we will be dive deeper into Elasticsearch to understand Elasticsearch mappings, how the indexing process works, and how to query the Elasticsearch data in order to perform full-text search and aggregations.