What this book covers

Chapter 1, Setting Up Environment, serves as a step-by-step guide to set up your environment, including Java, Hadoop, Elasticsearch, and useful plugins. Test the environment setup by running a WordCount job to import the results to Elasticsearch.

Chapter 2, Getting Started With ES-Hadoop, walks you through how the WordCount job was developed. We will take a look at the real-world problem to solve it better by introducing Elasticsearch to the Hadoop ecosystem.

Chapter 3, Understanding Elasticsearch, provides you detailed understanding on how to use Elasticsearch for full text search and analytics. With practical examples, you will learn indexing, search, and aggregation APIs.

Chapter 4, Visualizing Big Data Using Kibana, introduces Kibana with real-world examples to show how you can give different shapes and colors to your big data. It demonstrates how to discover your data and visualize it in dynamic dashboards.

Chapter 5, Real-Time Analytics, shows how Elasticsearch and Apache Storm can solve your real-time analytics requirements by classifying and streaming tweets data. We will see how to harness Elasticsearch to solve the data mining problem of anomaly detection.

Chapter 6, ES-Hadoop In Production, explains how Elasticsearch and the ES-Hadoop library works in distributed deployments and how to make the best out of it for your needs. It provides you practical skills to tweak configurations for your specific deployment needs. We will have a configuration checklist for production and skills to administer the cluster.

Chapter 7, Integrating with the Hadoop Ecosystem, takes practical examples to show how you can integrate Elasticsearch with Hadoop ecosystem technologies, such as Pig, Hive, Cascading, and Apache Spark.

Appendix, Configurations, contains a list of configuration options for any of the Hadoop ecosystem integrations.