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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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Backfilling the number of purchases by city

In the previous recipe, you generated an hourly summary and then, after waiting for 24 hours, you were able to report on the summary data over a 24-hour period. However, what if you wanted to report over the past 30 days or even 3 months? You would have to wait a long time for your summary data to build up over time. A better way is to backfill the summary data over an earlier time period, assuming you have raw data for this time period in Splunk.

In this recipe, you will create a search that identifies the number of purchases by city on a given day, and write this search to a summary index. You will leverage the IP location database built into Splunk to obtain the city based on the IP address in the results. You will then execute a script that comes bundled with Splunk in order to backfill the summary for the previous 30 days. Following...

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