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Splunk Operational Intelligence Cookbook

You're reading from   Splunk Operational Intelligence Cookbook Over 80 recipes for transforming your data into business-critical insights using Splunk

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Product type Paperback
Published in May 2018
Publisher
ISBN-13 9781788835237
Length 541 pages
Edition 3rd Edition
Languages
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Authors (4):
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Yogesh Raheja Yogesh Raheja
Author Profile Icon Yogesh Raheja
Yogesh Raheja
Josh Diakun Josh Diakun
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Josh Diakun
Derek Mock Derek Mock
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Derek Mock
Paul R. Johnson Paul R. Johnson
Author Profile Icon Paul R. Johnson
Paul R. Johnson
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Toc

Table of Contents (12) Chapters Close

Preface 1. Play Time – Getting Data In 2. Diving into Data – Search and Report FREE CHAPTER 3. Dashboards and Visualizations - Make Data Shine 4. Building an Operational Intelligence Application 5. Extending Intelligence – Datasets, Modeling and Pivoting 6. Diving Deeper – Advanced Searching, Machine Learning and Predictive Analytics 7. Enriching Data – Lookups and Workflows 8. Being Proactive – Creating Alerts 9. Speeding Up Intelligence – Data Summarization 10. Above and Beyond – Customization, Web Framework, HTTP Event Collector, REST API, and SDKs 11. Other Books You May Enjoy

Introduction

The machine data that facilitates operational intelligence comes in many different forms and from many different sources. Splunk can collect and index data from several sources, including log files written by web servers or business applications, syslog data streaming in from network devices, or the output of custom developed scripts. Even data that looks complex at first can be easily collected, indexed, transformed, and presented back to you in real time.

This chapter will walk you through the basic recipes that will act as the building blocks to get the data you want into Splunk. The chapter will further serve as an introduction to the sample data sets that we will use to build our own operational intelligence Splunk app. The datasets will be coming from a hypothetical three-tier e-commerce web application and will contain web server logs, application logs, and database logs.

Splunk Enterprise can index any type of data; however, it works best with time-series data (data with timestamps). When Splunk Enterprise indexes data, it breaks it into events, based on timestamps and/or event size, and puts them into indexes. Indexes are data stores that Splunk has engineered to be very fast, searchable, and scalable across a distributed server environment.

All data indexed into Splunk is assigned a source type. The source type helps identify the data format type of the event and where it has come from. Splunk has several preconfigured source types, but you can also specify your own. The example source types include access_combined, cisco_syslog, and linux_secure. The source type is added to the data when the indexer indexes it into Splunk. It is a key field that is used when performing field extractions and when conducting many searches to filter the data being searched.

The Splunk community plays a big part in making it easy to get data into Splunk. The ability to extend Splunk has provided the opportunity for the development of inputs, commands, and applications that can be easily shared. If there is a particular system or application you are looking to index data from, there is most likely someone who has developed and published relevant configurations and tools that can be easily leveraged by your own Splunk Enterprise deployment.

Splunk Enterprise is designed to make the collection of data very easy, and it will not take long before you are being asked or you yourself try to get as much data into Splunk as possible—at least as much as your license will allow for!

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