Mastering Splunk Data Management Techniques

Course Duration

3 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

Completion of Splunk Enterprise Data Administration (SP-SEDA) or equivalent. Solid understanding of Splunk data inputs, props/transforms, and index management.

Course Description

This 3-day course goes beyond standard data administration to cover advanced data management strategies for Splunk. Students learn to design and optimize the data ingestion pipeline, implement Data Stream Processor concepts, use edge processing with the Splunk Universal Forwarder, apply advanced data routing and filtering, and implement data management best practices for large-scale Splunk deployments.

Learning Objectives

• Review and optimize the Splunk data ingestion pipeline
• Implement advanced data routing, filtering, and transformation techniques
• Apply edge processing strategies to reduce indexing volume
• Use Heavy Forwarder capabilities for complex data transformation
• Implement data normalization and CIM compliance
• Design data management architectures for large-scale deployments
• Troubleshoot complex data quality and ingestion issues

Course Outline

Data Processing Fundamentals

• Splunk GDI architecture review
• Ingestion pipeline stages
• Data volume management strategies

Advanced Data Routing

• Props and transforms advanced patterns
• Data routing and cloning
• Filtering and nullQueue
• Load balancing across indexers

Edge Processing

• Heavy forwarder capabilities
• Edge processing patterns
• Reducing data volume at source

Data Normalization

• Common Information Model (CIM)
• Implementing CIM compliance
• Data normalization best practices

Data Management at Scale

• SmartStore considerations
• Data tiering strategies
• Troubleshooting data quality issues

Frequently Asked Questions