Lesson 1: Data Administration Architecture, Forwarders, Inputs, Source Types, and Onboarding Planning

Lesson Overview

This lesson develops practical capability in data administration architecture, forwarders, inputs, source types, and onboarding planning for the Splunk Enterprise Data Administration Training. Learners connect the topic to the wider course goal of onboarding, parsing, indexing, routing, validating, and governing Splunk data.

Topics Covered

Data Administration Architecture, Forwarders, Inputs, Source Types, and Onboarding Planning: purpose, terminology, architecture, roles, and expected business or operational outcomes
Core Splunk data, searches, configuration, objects, integrations, interfaces, and security controls relevant to the topic
Design decisions, dependencies, risks, permissions, performance considerations, and governance requirements
Guided practice focused on architecture, terminology, prerequisites, roles, data flow, and measurable outcomes
Testing, validation, diagnostics, troubleshooting, documentation, and operational support

Practical Activity

Create an architecture and requirements worksheet, identify dependencies, and map the learner scenario to Splunk components.
Capture assumptions, configuration choices, expected results, test evidence, and remediation notes in a reusable worksheet.
Review the completed exercise against security, data quality, performance, maintainability, and support-readiness criteria.

Key Takeaways

Explain the purpose and architecture of data administration architecture, forwarders, inputs, source types, and onboarding planning.
Apply the principal Splunk features and workflows required for the lesson scenario.
Validate results, identify common failure points, document decisions, and support a controlled operational outcome.

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Splunk Enterprise Data Administration Training 1 Section4 Lessons16 Hours

Curriculum