Book Image

Elasticsearch 8.x Cookbook - Fifth Edition

By : Alberto Paro
Book Image

Elasticsearch 8.x Cookbook - Fifth Edition

By: Alberto Paro

Overview of this book

Elasticsearch is a Lucene-based distributed search engine at the heart of the Elastic Stack that allows you to index and search unstructured content with petabytes of data. With this updated fifth edition, you'll cover comprehensive recipes relating to what's new in Elasticsearch 8.x and see how to create and run complex queries and analytics. The recipes will guide you through performing index mapping, aggregation, working with queries, and scripting using Elasticsearch. You'll focus on numerous solutions and quick techniques for performing both common and uncommon tasks such as deploying Elasticsearch nodes, using the ingest module, working with X-Pack, and creating different visualizations. As you advance, you'll learn how to manage various clusters, restore data, and install Kibana to monitor a cluster and extend it using a variety of plugins. Furthermore, you'll understand how to integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this Elasticsearch cookbook, you'll have gained in-depth knowledge of implementing the Elasticsearch architecture and be able to manage, search, and store data efficiently and effectively using Elasticsearch.
Table of Contents (20 chapters)

Chapter 17: Big Data Integration

Elasticsearch has become a common component in big data architectures because it provides several of the following features:

  • It allows you to search for massive amounts of data quickly.
  • For common aggregation operations, it provides real-time analytics on big data.
  • It's easier to use an Elasticsearch aggregation than a Spark one.
  • If you need to move on to a fast data solution, starting from a subset of documents after a query is faster than doing a full rescan of all your data.

The most common big data software that's used for processing data is now Apache Spark (, which is considered the evolution of the obsolete Hadoop MapReduce for moving the processing from disk to memory.

In this chapter, we will see how to integrate Elasticsearch in Spark, both for write and read data. At the end, we will see how to use Apache Pig to write data in Elasticsearch in a simple way.

In this chapter...