Book Image

Learning Elastic Stack 6.0

By : Pranav Shukla, Sharath Kumar M N
Book Image

Learning Elastic Stack 6.0

By: Pranav Shukla, Sharath Kumar M N

Overview of this book

The Elastic Stack is a powerful combination of tools for distributed search, analytics, logging, and visualization of data from medium to massive data sets. The newly released Elastic Stack 6.0 brings new features and capabilities that empower users to find unique, actionable insights through these techniques. This book will give you a fundamental understanding of what the stack is all about, and how to use it efficiently to build powerful real-time data processing applications. After a quick overview of the newly introduced features in Elastic Stack 6.0, you’ll learn how to set up the stack by installing the tools, and see their basic configurations. Then it shows you how to use Elasticsearch for distributed searching and analytics, along with Logstash for logging, and Kibana for data visualization. It also demonstrates the creation of custom plugins using Kibana and Beats. You’ll find out about Elastic X-Pack, a useful extension for effective security and monitoring. We also provide useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments. On completing this book, you’ll have a solid foundational knowledge of the basic Elastic Stack functionalities. You’ll also have a good understanding of the role of each component in the stack to solve different data processing problems.
Table of Contents (19 chapters)
Title Page
Credits
Disclaimer
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

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...