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Learning Elastic Stack 7.0

You're reading from   Learning Elastic Stack 7.0 Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789954395
Length 474 pages
Edition 2nd Edition
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Authors (2):
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Sharath Kumar Sharath Kumar
Author Profile Icon Sharath Kumar
Sharath Kumar
Pranav Shukla Pranav Shukla
Author Profile Icon Pranav Shukla
Pranav Shukla
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction to Elastic Stack and Elasticsearch FREE CHAPTER
2. Introducing Elastic Stack 3. Getting Started with Elasticsearch 4. Section 2: Analytics and Visualizing Data
5. Searching - What is Relevant 6. Analytics with Elasticsearch 7. Analyzing Log Data 8. Building Data Pipelines with Logstash 9. Visualizing Data with Kibana 10. Section 3: Elastic Stack Extensions
11. Elastic X-Pack 12. Section 4: Production and Server Infrastructure
13. Running Elastic Stack in Production 14. Building a Sensor Data Analytics Application 15. Monitoring Server Infrastructure 16. Other Books You May Enjoy

The basics of text analysis

The analysis of text data is different from other types of data analysis, such as numbers, dates, and times. The analysis of numeric and date/time datatypes can be done in a very definitive way. For example, if you are looking for all records with a price greater than, or equal to, 50, the result is a simple yes or no for each record. Either the record in question qualifies or doesn't qualify for inclusion in the query's result. Similarly, when querying something by date or time, the criteria for searching through records is very clearly defined – a record either falls into the date/time range or it doesn't.

However, the analysis of text/string data can be different. Text data can be of a different nature, and it can be used for structured or unstructured analysis.

Some examples of structured types of string fields are as follows...

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