Book Image

Learning Elastic Stack 7.0 - Second Edition

By : Pranav Shukla, Sharath Kumar M N
Book Image

Learning Elastic Stack 7.0 - Second Edition

By: Pranav Shukla, Sharath Kumar M N

Overview of this book

The Elastic Stack is a powerful combination of tools that help in performing distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and guide you in using it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed search and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well-versed with fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.
Table of Contents (17 chapters)
Free Chapter
Section 1: Introduction to Elastic Stack and Elasticsearch
Section 2: Analytics and Visualizing Data
Section 3: Elastic Stack Extensions
Section 4: Production and Server Infrastructure

Bucket aggregations

Bucket aggregations are useful to analyze how the whole relates to its parts, so that we can gain better insight on the data. They help in segmenting the data into smaller parts. Each type of bucket aggregation slices the data into different segments, or buckets. Bucket aggregations are the most common type of aggregation used in any analysis process.

In this section, we will cover the following topics, keeping the network traffic data example at the center:

  • Bucketing on string data
  • Bucketing on numerical data
  • Aggregating filtered data
  • Nesting aggregations
  • Bucketing on custom conditions
  • Bucketing on date/time data
  • Bucketing on geospatial data

Bucketing on string data

Sometimes, we may need to bucket the...