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Advanced Elasticsearch 7.0

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

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789957754
Length 560 pages
Edition 1st Edition
Languages
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Author (1):
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Wai Tak Wong Wai Tak Wong
Author Profile Icon Wai Tak Wong
Wai Tak Wong
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Toc

Table of Contents (25) Chapters Close

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

Summary

Cheers! We have completed a basic study of Java programming with Elasticsearch. We should now be able to understand and access the different Elasticsearch clients in Java. We also learned how to incorporate Elasticsearch into Java applications, especially Spring Boot applications. We also outlined the basic programming concept of Spring Data Elasticsearch. We pointed out the issue that Spring Data needs to revise significantly, due to the fact that the transport client is deprecated in version 7.0 and will be removed fully in version 8.0.

In the following Chapter 12, Elasticsearch from Python Programming, we will introduce Python programming with Elasticsearch. The elasticsearch-py package is the official low-level client for Elasticsearch. The goal of the Python package is to provide commonality to all Elasticsearch-related codes in Python. We will also review Elasticsearch...

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