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Learning ELK Stack

You're reading from Learning ELK Stack Build mesmerizing visualizations, analytics, and logs from your data using Elasticsearch, Logstash, and Kibana

Product type Paperback

Published in Nov 2015

Publisher

ISBN-13 9781785887154

Length 206 pages

Edition 1st Edition

Tools

Elasticsearch

Concepts

Enterprise Search

Author (1):

Saurabh Chhajed

View More author details

Table of Contents (12) Chapters

Preface

1. Introduction to ELK Stack FREE CHAPTER

2. Building Your First Data Pipeline with ELK

3. Collect, Parse and Transform Data with Logstash

4. Creating Custom Logstash Plugins

5. Why Do We Need Elasticsearch in ELK?

6. Finding Insights with Kibana

7. Kibana – Visualization and Dashboard

8. Putting It All Together

9. ELK Stack in Production

10. Expanding Horizons with ELK

Index

Input dataset

The input dataset is a continuous stream of Tomcat access logs in the following format:

10.0.0.2 - - [08/Sep/2015:17:39:46 +0100] "GET /elk/demo/10 HTTP/1.1" 200 40
10.0.0.2 - - [08/Sep/2015:17:39:47 +0100] "GET /elk/demo/11 HTTP/1.1" 200 39
10.0.0.3 - - [08/Sep/2015:17:39:48 +0100] "GET /elk/demo/12 HTTP/1.1" 200 39
10.0.0.2 - - [08/Sep/2015:17:39:49 +0100] "GET /elk/demo/13 HTTP/1.1" 200 39
10.0.0.2 - - [08/Sep/2015:17:39:50 +0100] "GET /elk/demo/14 HTTP/1.1" 200 39
10.0.0.4 - - [08/Sep/2015:17:39:51 +0100] "GET /elk/demo/15 HTTP/1.1" 200 40
10.0.0.2 - - [08/Sep/2015:17:39:52 +0100] "GET /elk/demo/16 HTTP/1.1" 200 39
10.0.0.2 - - [08/Sep/2015:17:39:53 +0100] "GET /elk/demo/17 HTTP/1.1" 200 39
10.0.0.5 - - [08/Sep/2015:17:39:54 +0100] "GET /elk/demo/18 HTTP/1.1" 200 39
10.0.0.2 - - [08/Sep/2015:17:39:55 +0100] "GET /elk/demo/19 HTTP/1.1" 200 39
10.0.0.2 - - [08/Sep/2015:17:39:56 +0100] "GET /elk/demo/20 HTTP/1.1" 200 40
10.0.0.6 - - [08/Sep/2015:17:39:57 +0100] "GET /elk/demo...

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Authors (1)

Chhajed

Chhajed

Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at http://saurzcode.in. Saurabh has also reviewed books by Packt Publishing, Apache Camel Essentials and Java EE 7 Development with NetBeans 8, in the past.

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