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Learning Elasticsearch

Learning Elasticsearch

By : Andhavarapu
4.3 (4)
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Learning Elasticsearch

Learning Elasticsearch

4.3 (4)
By: Andhavarapu

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
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10
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Summary

In this chapter, we discussed the various bulk operations Elasticsearch supports. You also learned about Reindex and Shrink APIs, which can be used to the change the index configuration, such as the number of shards, mapping of an existing index and so on without re-indexing the data.

We covered how to organize your data in Elasticsearch using aliases and index templates. We discussed how to use ingest node to pre-process your data before indexing into Elasticsearch. You learned how to use ingest node to transform unstructured log data into JSON documents and automatically index them into a month-based index.

In the next chapter, we will discuss different ways of querying Elasticsearch.

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