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Splunk Operational Intelligence Cookbook

Splunk Operational Intelligence Cookbook

By : Josh Diakun, Raheja, Paul R. Johnson, Derek Mock
4.7 (3)
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Splunk Operational Intelligence Cookbook

Splunk Operational Intelligence Cookbook

4.7 (3)
By: Josh Diakun, Raheja, Paul R. Johnson, Derek Mock

Overview of this book

Splunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 80 recipes that demonstrate all of Splunk’s features, not only will you find quick solutions to common problems, but you’ll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You’ll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You’ll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organization. Throughout the book, you’ll dive deeper into Splunk, explore data models and pivots to extend your intelligence capabilities, and perform advanced searching with machine learning to explore your data in even more sophisticated ways. Splunk is changing the business landscape, so make sure you’re taking advantage of it.
Table of Contents (12 chapters)
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Predicting website traffic volumes

In any environment, the capability to predict events provides immense value. In many cases, predictive analytics involves looking back over past events to predict what might occur in the future with a certain degree of confidence. When applied to the Operational Intelligence space and used correctly, predictive analytics can become a key asset that is more heavily relied on by teams rather than any other part of an Operational Intelligence program. For example, imagine having the ability to know the appropriate thresholds to set to alert key staff of impending issues, the capability to understand that a problem is beginning to occur even before it does, or simply being able to predict what consumers will purchase and ensuring the items are in stock. These examples just scratch the surface on use cases for predictive analytics.

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