Training Description
Description
The objective of the LP-SPL03 course is to provide participants with comprehensive knowledge in the field of construction, operation, principles of functioning, administration and creating data analysis using the Splunk system. All mentioned aspects of working with the Splunk system are discussed during lectures and supported by many practical exercises carried out in the AWS EC2 environment (Amazon Web Services Elastic Cloud 2).
lectures, labs
lectures 40% + labs 60%
We place emphasis on practical skills.
For who
For who
This course is designed for experienced administrators, analysts and IT architects.
what will you learn
what will you learn
During the training, you will learn the basics of Splunk system operation as well as data processing and system configuration management.
Requirements
Requirements
Experience in configuration and administration of operating systems (preferred Linux) and basic knowledge on statistic is required.
We provide
We provide
  • Office supplies (notebook and pen)
  • Catering
  • Clients receive personalized certificates signed by Linux Polska

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Outline for this course

01
Splunk Fundamentals
  • Splunk location in the enterprise architecture
  • Operating principles of Splunk
  • Splunk’s interface
  • Basic concepts (event, source of events, etc.)
  • Basics of the Splunk query language
  • Basics of data modeling (extractions, models)
  • Creating and configuration scheduled reports and alerts
  • Building your own applications based on Splunk
  • Security management in the Splunk system
01
Splunk Administration
  • Splunk system’s technical architecture
  • Data processing in Splunk
  • Indexing configuration (creation, operation, archiving and parameterization of indexes)
  • Distributed configuration management (use of UF and Deployment Server)
  • Typical administrative problems along with solutions
01
Splunk Administration
  • Introduction to statistics – statistical concepts in the Splunk environment
  • Application of Splunk statistical functions (measures, data grouping)
  • Data correlation
  • Trend forecasting and analyzing (“predictive analytics”)
  • Study of anomalies

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