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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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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

Overview

The support feature for SQL was delivered with version 6.3. This was due to the popularity of SQL, while many Elasticsearch users were familiar with SQL syntax and the ability to perform SQL-like queries on indexed data had been a longstanding desire. Elasticsearch SQL is an X-Pack component, where X-Pack is an Elastic Stack extension that provides many features, including monitoring, security, and machine learning. In the default installation, Elasticsearch comes with X-Pack. You will recall from the Mapping concepts across SQL and Elasticsearch section of Chapter 1, Overview of Elasticsearch 7, that we constructed a table that describes the terms between SQL and Elasticsearch, comparing column and field, row and document, table and index, and database and cluster instance. You can think of Elasticsearch SQL as a translator from SQL to Elasticsearch. Nonetheless, all...

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