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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

Aggregation Frameworks

The two key features of Elasticsearch are search and data analytics. In the previous two chapters, we learned about the search API and how to design search data modeling. We also used real-world examples from the Investor Exchange (IEX) Cloud ETF system to practice using the search feature. In this chapter, we will discuss data analytics using the aggregation framework. Aggregation can be thought of as a unit of work for building analytic information on a set of documents. The framework consists of many building blocks that can be composed to build a complex summary of the data selected by a search query. The framework is straightforward, simple, extensible, quick to access, and awesome. It can be very helpful for our business.

By the end of this chapter, we will have covered the following topics and used IEX historical ETF data to work on supported aggregation...

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