Book Image

Implementing Splunk 7, Third Edition - Third Edition

Book Image

Implementing Splunk 7, Third Edition - Third Edition

Overview of this book

Splunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects
Table of Contents (19 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Defining the toolkit


Consider that Splunk offers a three-tier architecture for machine learning defined as:

  • Tier 1: Core platform searching features
  • Tier 2: Packaged solutions and apps offered on Splunkbase
  • Tier 3: Using the Splunk Machine Learning Toolkit

Since tier 1 and tier 2 should be self-explanatory to you at this point, let's have a closer look at tier 3.

To define the Machine Learning Toolkit, we will start with a typical machine learning project so as to understand what type of work will be carried out by most data scientists. These work efforts are:

  • Collect (data)
  • Clean and transform (data)
  • Explore and visualize (data)
  • Model (data)
  • Evaluate (the results of the model)
  • Deployment (once the predictions are made, how can they be put to use?)

Time well spent

Of the preceding listed tasks, popular opinion from the field (of data scientists) states that up to 60% of the time is spent performing the cleaning and transforming of data, while almost 20% (of the time) is spent on data collection.

The Splunk...