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1. Overview of Elasticsearch 7
2. Index APIs
3. Document APIs
4. Mapping APIs
5. Anatomy of an Analyzer
6. Search APIs
7. Modeling Your Data in the Real World
8. Aggregation Frameworks
9. Preprocessing Documents in Ingest Pipelines
10. Using Elasticsearch for Exploratory Data Analysis
11. Elasticsearch from Java Programming
12. Elasticsearch from Python Programming
13. Using Kibana, Logstash, and Beats
14. Working with Elasticsearch SQL
15. Working with Elasticsearch Analysis Plugins
16. Machine Learning with Elasticsearch
17. Spark and Elasticsearch for Real-Time Analytics
18. Building Analytics RESTful Services
Authors

Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks.

You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch.

By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch.