Splunk Essentials - Second Edition: Operational Intelligence at your fingertips

A fast-paced and practical guide to demystifying big data and transforming it into operational intelligence

About This Book

• Want to get started with Splunk to analyze and visualize machine data? Open this book and step into the world of Splunk!
• Leverage the exceptional analysis and visualization capabilities to make informed decisions for your business
• This easy-to-follow, practical book can be used by anyone - even if you have never managed any data before

Who This Book Is For

This book is perfect for you if you are a software engineer or developer, system administrator or business analyst who wants to correlate machine data with business metrics and provide intuitive real-time and statistical visualizations. Some knowledge or experience of previous versions of Splunk will be helpful but not essential.

What You Will Learn

• Install and configure Splunk
• Gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields
• Be comfortable with the Search Processing Language and get to know best practices in writing search queries
• Create stunning and powerful dashboards
• Be proactive by implementing alerts and scheduled reports
• Use the Splunk SDK and integrate Splunk data into other applications
• Implement Splunk best practices

In Detail

Splunk is a search, analysis, and reporting platform for machine data, which has a high adoption on the market. More and more organizations want to adopt Splunk to use their data to make informed decisions.

This book is for anyone who wants to manage data with Splunk. You'll start with the very basics of Splunk - installing Splunk - and then move on to searching machine data with Splunk. You will gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields. After this, you will learn to create various reports, XML forms, and alerts. You will then continue using the Pivot Model to transform the data models into visualization. You will also explore visualization with D3 in Splunk. Finally you'll be provided with some real-world best practices in using Splunk.

Style and approach

This fast-paced, example-rich guide will help you analyze and visualize machine data with Splunk through simple, practical instructions.

Table of Contents

1. Splunk in Action
2. Bringing in Data
3. Search Processing Language
4. Data Models and Pivot
5. Data Optimization, Reports, Alerts & Accelerating Searches
6. Panes of Glass
7. Splunk SDK for JavaScript and D3.js
8. HTTP Event Collector
9. Best Practices and Advanced Queries

Betsy Page Sigman Betsy Page Sigman is a distinguished professor at the McDonough School of Business at Georgetown University in Washington, D.C. She has taught courses in statistics, project management, databases, and electronic commerce for the last 17 years and has been recognized with awards for teaching and service. Before arriving at Georgetown, she worked at George Mason University, the U.S. Bureau of the Census, Decision/Making/Information, the American Enterprise Institute, and the Social Science Data Center (now Roper Center) at the University of Connecticut. Her recent publications include a Harvard Business case study and a Harvard Business review article, articles in the Decision Sciences Journal of Innovative Education and Decision Line, and a case study in Educause Review Online. Additionally, she is a frequent media commentator on technological issues and big data.

Erickson Delgado Erickson Delgado is an enterprise architect who loves to mine and analyze data. He began using Splunk in version 4.0 and has pioneered the use of the application in his current work. In the earlier parts of his career, he worked with start-up companies in the Philippines to help build their open source infrastructure. He then worked in the cruise industry as a shipboard IT manager, and he loved it. From there, he was recruited to work at the company's headquarters as a software engineer. He has developed applications with Python and Node.js. He is interested in Go and is rediscovering programming with C/C++. He is crazy about visualization platforms and tools. In recent years, he has engaged himself with employing DevOps in his work. Since Erickson's routine evolves around technical practices, he blows off steam by saltwater fishing, mountain biking, crafting robots, and touring the country. He lives in Orlando.