Advanced Elasticsearch 7.0: A practical guide to designing, indexing, and querying advanced distributed search engines

Book Description
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.

What you will learn
Pre-process documents before indexing in ingest pipelines
Learn how to model your data in the real world
Get to grips with using Elasticsearch for exploratory data analysis
Understand how to build analytics and RESTful services
Use Kibana, Logstash, and Beats for dashboard applications
Get up to speed with Spark and Elasticsearch for real-time analytics
Explore the basics of Spring Data Elasticsearch, and understand how to index, search, and query in a Spring application
Who this book is for
This book is for Elasticsearch developers and data engineers who want to take their basic knowledge of Elasticsearch to the next level and use it to build enterprise-grade distributed search applications. Prior experience of working with Elasticsearch will be useful to get the most out of this book.

Table of Contents
Overview of Elasticsearch 7
Index APIs
Document APIs
Mapping APIs
Anatomy of an Analyzer
Search APIs
Modeling Your Data in the Real World
Aggregations Frameworks
Preprocessing Documents in Ingest Pipelines
Using ElasticSearch for Exploratory Data Analysis
Elasticsearch from Java Programming
Elasticsearch from Python Programming
Using Kibana, Logstash and Beats
Working with Elasticsearch SQL
Working with Elasticsearch Analysis Plugins
Machine Learning with Elasticsearch
Spark and Elasticsearch for Real-Time Analytics
Building Analytics RESTful Services