Analyze Windows Data Ingestion with Splunk Projects

本课程是 Evaluate and Optimize Enterprise Log Analytics 专项课程的一部分

位教师：EDUCBA

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3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Configure Windows and agentless Splunk inputs to ingest data using multiple collection methods.
Parse, normalize, and classify Windows event data with accurate timestamps and metadata.
Design and troubleshoot end-to-end Splunk data onboarding workflows for reliable analysis.

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要了解的详细信息

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作业

12 项作业

授课语言：英语（English）

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本课程是 Evaluate and Optimize Enterprise Log Analytics 专项课程专项课程的一部分

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该课程共有3个模块

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.

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.

涵盖的内容

8个视频4个作业

8个视频总计80分钟

Windows Inputs and Agentless Inputs 13分钟
Windows Specific Inputs 13分钟
Types of Windows Specific Inputs 8分钟
Settings for App Context 11分钟
Local Performance Monitor 11分钟
Settings in Input Files 8分钟
Event Log Monitoring 10分钟
Powershell Inputs 8分钟

4个作业总计60分钟

Graded - Foundations of Windows Data Collection in Splunk 30分钟
Introduction to Windows and Agentless Inputs 10分钟
Configuring App Context and File-Based Inputs 10分钟
Monitoring Windows Events and PowerShell 10分钟

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.

涵盖的内容

8个视频4个作业

8个视频总计75分钟

Splunk Agentless Inputs 8分钟
HTTP event collector 8分钟
Specifying Metadata 11分钟
Fine Tuning Inputs 11分钟
Props Dot Conf 12分钟
Data Modifications in Props 8分钟
Creating Monitor Input 8分钟
Parsing Phase and Data Preview 9分钟

4个作业总计60分钟

Graded - Advanced Input Methods and Configuration Controls 30分钟
Agentless and HTTP-Based Data Ingestion 10分钟
Input Optimization and Props Configuration 10分钟
Monitor Inputs and Parsing Basics 10分钟

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.