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You're reading from Advanced Elasticsearch 7.0 A practical guide to designing, indexing, and querying advanced distributed search engines

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781789957754

Length 560 pages

Edition 1st Edition

Languages

Java

Tools

Elasticsearch

Concepts

Enterprise Search

Author (1):

Wai Tak Wong

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Table of Contents (25) Chapters

Preface

1. Section 1: Fundamentals and Core APIs FREE CHAPTER

2. Overview of Elasticsearch 7

3. Index APIs

4. Document APIs

5. Mapping APIs

6. Anatomy of an Analyzer

7. Search APIs

8. Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics

9. Modeling Your Data in the Real World

10. Aggregation Frameworks

11. Preprocessing Documents in Ingest Pipelines

12. Using Elasticsearch for Exploratory Data Analysis

13. Section 3: Programming with the Elasticsearch Client

14. Elasticsearch from Java Programming

15. Elasticsearch from Python Programming

16. Section 4: Elastic Stack

17. Using Kibana, Logstash, and Beats

18. Working with Elasticsearch SQL

19. Working with Elasticsearch Analysis Plugins

20. Section 5: Advanced Features

21. Machine Learning with Elasticsearch

22. Spark and Elasticsearch for Real-Time Analytics

23. Building Analytics RESTful Services

24. Other Books You May Enjoy

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Elasticsearch architectural overview

The story of how the ELK Stack becomes Elasticsearch, Logstash, and Kibana, is a pretty long story (https://www.elastic.co/about/history-of-elasticsearch). At Elastic{ON} 2015 in San Francisco, Elasticsearch Inc. was renamed Elastic and announced the next evolution of Elastic Stack. Elasticsearch will still play an important role, no matter what happens.

Elastic Stack architecture

Elastic Stack is an end-to-end software stack for search and analysis solutions. It is designed to help users get data from any type of source in any format to allow for searching, analyzing, and visualizing data in real time. The full stack consists of the following:

Beats master: A lightweight data conveyor...

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Authors (1)

Wong

Dr. Timothy Wong is a 30 years IT veteran. He holds a PhD in networking from University of Manchester Institute of Science and Technology, UK. He has deep experience in wireless and wireline networking, digital a/v, software development, sales and consulting. He frequently works with telcos, banks, governments and enterprises. He is also a professor on Big Data, Wireless and IoT at Humber College, Toronto, Canada. As an entrepreneur, he co-founded a number of companies in the past 20 years. He infuses business and technical knowledge to pursue successes.

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