Lesson 1: Observability Data Model, Metrics, Dimensions, Tokens, Authentication, API Tooling, and Security

Lesson Overview

This lesson develops practical capability in observability data model, metrics, dimensions, tokens, authentication, api tooling, and security for the Automation Using the REST and SignalFlow APIs Training. Learners connect the topic to the wider course goal of automating observability data, detectors, dashboards, and analytics with REST and SignalFlow APIs.

Topics Covered

Observability Data Model, Metrics, Dimensions, Tokens, Authentication, API Tooling, and Security: 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 observability data model, metrics, dimensions, tokens, authentication, api tooling, and security.
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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Automation Using the REST and SignalFlow APIs Training 1 Section4 Lessons16 Hours

Curriculum