Analyze Windows Data Ingestion with Splunk Projects

Overview

By the end of this course, learners will be able to configure Windows and agentless inputs, ingest data using multiple Splunk methods, parse and normalize events accurately, manage timestamps and time zones, and classify data effectively for reliable analysis. This hands-on, project-focused course is designed to help learners build strong, practical expertise in Windows data ingestion and processing using Splunk. You will explore Windows-specific inputs, agentless collection techniques, HTTP Event Collector (HEC), and PowerShell-based data ingestion, gaining a clear understanding of when and why to use each approach. The course also dives deep into parsing fundamentals, including event boundaries, multi-line events, timestamp extraction, and the use of props.conf for data transformations. Learners benefit by developing job-ready skills that are directly applicable to real-world Splunk environments, especially those managing complex Windows infrastructures. What makes this course unique is its end-to-end project orientation: rather than isolated concepts, you will see how inputs, parsing, metadata, and classification work together in a complete data onboarding workflow. This structured, practical approach ensures you can confidently design, troubleshoot, and optimize Splunk data ingestion pipelines in professional settings.

Syllabus

Foundations of Windows Data Collection in Splunk

This module introduces the fundamentals of collecting Windows data in Splunk. Learners explore Windows-specific input types, agent-based and agentless data collection approaches, and configuration best practices. The module emphasizes selecting appropriate inputs, understanding app context and configuration scope, and collecting critical operational and security data using Event Logs, performance monitoring, and PowerShell inputs. By the end of this module, learners will be prepared to design reliable and maintainable Windows data ingestion strategies in Splunk environments.

Advanced Input Methods and Configuration Controls

This module focuses on advanced data ingestion techniques and configuration controls in Splunk. Learners examine agentless inputs, HTTP Event Collector (HEC), metadata specification, and input fine-tuning for efficiency and accuracy. The module also introduces parsing fundamentals through monitor inputs and the data preview process, enabling learners to validate and optimize data onboarding before indexing. This module builds the skills required to ingest diverse data sources reliably in enterprise environments.

Event Parsing, Time Handling, and Data Classification

This module covers the critical aspects of parsing, timestamp management, and data classification in Splunk. Learners focus on defining event boundaries, handling single-line and multi-line events, configuring time zones, and extracting accurate timestamps. The module also emphasizes data validation and classification using metadata fields such as source, sourcetype, and host. By completing this module, learners gain the ability to ensure data accuracy and consistency for reliable searching, reporting, and analytics.