Review of Basic Features and Architecture of Splunk
Developing a Splunk Application and a Technology Add-on
Connecting Data to Splunk
- Understanding various data input methods and sources
- Processing data
- Improving the input process
Conducting Advanced Data Analytics
- Manipulating and filtering data
- Combining searches and using subsearches
- Working with time and multivalue fields
- Creating advanced reports
- Using geography and location
- Using advanced transactions
- Dealing with anomalies
- Predicting and trending
- Understanding machine learning
Performing Advanced Visualization
- Sunburst Sequence
- Geospatial visualization
- Punchcard visualization
- Calendar heatmap visualization
- Sankey diagram
- Using Dashboard controls
- Managing multi-search
- Customizing tokens
- Customizing layout, look and feel
- Implementing the custom alert action
Integrating Splunk with Other Enterprise Systems
- Working with the Splunk SDK
- Splunk with Python and R for analytics
- Splunk with Tableau for visualization
Summary and Conclusion
- Experience with business intelligence and data visualization
- Knowledge of Splunk fundamentals
- Data analysts
- Data scientists
- Data engineers
The instructor knows splunk very well.
Incorporating the data science topics.
I enjoyed that it was hands on and practical and not "Death by PowerPoint".
I learned more about Splunk than I already knew.
Setting up the universal forwarder and heavy forwarder is something I know for a fact I will use on my team.
Madison Sample - Ultimate Knowledge
using BOTS as the search basis
Enjoyed the technical in depth dive into Splunk and the utilization of the clustering and ingest capabilities.