Master the intricacies of Elasticsearch 7.0 and use it to create flexible and scalable search solutions

Key Features

• Master the latest distributed search and analytics capabilities of Elasticsearch 7.0
• Perform searching, indexing, and aggregation of your data at scale
• Discover tips and techniques for speeding up your search query performance

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

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. Aggregations 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

About the Author

Wai Tak Wong is a faculty member in the Department of Computer Science at Kean University, NJ, USA. He has more than 15 years professional experience in cloud software design and development. His PhD in computer science was obtained at NJIT, NJ, USA. Wai Tak has served as an associate professor in the Information Management Department of Chung Hua University, Taiwan. A co-founder of Shanghai Shellshellfish Information Technology, Wai Tak acted as the Chief Scientist of the R&D team, and he has published more than a dozen algorithms in prestigious journals and conferences. Wai Tak began his search and analytics technology career with Elasticsearch in the real estate market and later applied this to data management and FinTech data services.