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Learning Elastic Stack 6.0

You're reading from   Learning Elastic Stack 6.0 A beginner's guide to distributed search, analytics, and visualization using Elasticsearch, Logstash and Kibana

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781787281868
Length 434 pages
Edition 1st 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 (12) Chapters Close

Preface 1. Introducing Elastic Stack FREE CHAPTER 2. Getting Started with Elasticsearch 3. Searching-What is Relevant 4. Analytics with Elasticsearch 5. Analyzing Log Data 6. Building Data Pipelines with Logstash 7. Visualizing data with Kibana 8. Elastic X-Pack 9. Running Elastic Stack in Production 10. Building a Sensor Data Analytics Application 11. Monitoring Server Infrastructure

Basics of text analysis


Analysis of text data is different to other types of data analysis such as numbers, dates, and time. 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 the 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 analysis or unstructured analysis.

Some examples of structured types of string fields are as follows: country codes, product codes, non-numeric serial numbers/identifiers, and so on. The datatype of these fields may be a string, but often...

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