Book Image

Advanced Splunk

By : Ashish Kumar Tulsiram Yadav
Book Image

Advanced Splunk

By: Ashish Kumar Tulsiram Yadav

Overview of this book

Master the power of Splunk and learn the advanced strategies to get the most out of your machine data with this practical advanced guide. Make sense of the hidden data of your organization – the insight of your servers, devices, logs, traffic and clouds. Advanced Splunk shows you how. Dive deep into Splunk to find the most efficient solution to your data problems. Create the robust Splunk solutions you need to make informed decisions in big data machine analytics. From visualizations to enterprise integration, this well-organized high level guide has everything you need for Splunk mastery. Start with a complete overview of all the new features and advantages of the latest version of Splunk and the Splunk Environment. Go hands on with uploading data, search commands for basic and advanced analytics, advanced visualization techniques, and dashboard customizing. Discover how to tweak Splunk to your needs, and get a complete on Enterprise Integration of Splunk with various analytics and visualization tools. Finally, discover how to set up and use all the new features of the latest version of Splunk.
Table of Contents (20 chapters)
Advanced Splunk
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
Index

Machine learning


Splunk 6.4 has enhanced the Machine Learning Toolkit and Showcase app, which we already studied with an example in Chapter 5, Advanced Data Analytics. Splunk 6.4 comes with six new machine learning algorithms along with support to hundreds of algorithms of Python's data science library. Apart from this enhancement, the machine learning app has added the Guided ML feature that guides users step by step to build, test, and deploy machine learning models.

Splunk 6.4 has enhanced the predict command with features like these:

  • A new algorithm for bivariate time series has been introduced, taking covariance between the individual time series into account for better and efficient prediction

  • The predict command can be used to predict results for multiple time series at the same time and can also fill in missing data in the given time series