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

Building Data Pipelines with Logstash

In the previous chapter, we understood the importance of Logstash in the log analysis process. We also covered its usage and its high-level architecture, and went through some commonly used plugins. One of the important processes of Logstash is converting unstructured log data into structured data, which helps us search for relevant information easily and also assists in analysis. Apart from parsing the log data to make it structured, it would also be helpful if we could enrich the log data during this process so that we can gain further insights into our logs. Logstash comes in handy for enriching our log data, too. In the previous chapter, we have also seen that Logstash can read from a wide range of inputs and that Logstash is a heavy process. Installing Logstash on the edge nodes of shipping logs might not always be feasible. Is there...