Lesson 1: Analytics Use Cases, Data Understanding, SPL Preparation, Sampling, Quality, and Feature Design

Lesson Overview

This lesson develops practical capability in analytics use cases, data understanding, spl preparation, sampling, quality, and feature design for the Splunk 8.0 for Analytics and Data Science Training. Learners connect the topic to the wider course goal of preparing data, performing statistical analysis, building predictive workflows, and operationalizing insights.

Topics Covered

Analytics Use Cases, Data Understanding, SPL Preparation, Sampling, Quality, and Feature Design: 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 analytics use cases, data understanding, spl preparation, sampling, quality, and feature design.
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 8.0 for Analytics and Data Science Training 1 Section4 Lessons24 Hours

Curriculum