Description

Introduction of Data Management Professional

The Data Management Professional Certification for Splunk is designed to equip professionals with the necessary skills to manage, analyze, and optimize data within the Splunk ecosystem. This certification focuses on the best practices for handling large volumes of machine data, improving data collection, and ensuring data quality and integrity across an organization’s Splunk infrastructure.

Participants will learn how to implement effective data management strategies, leverage Splunk’s indexing and search functionalities, and use Splunk’s data onboarding tools for seamless data integration. This training emphasizes data governance, performance optimization, and effective reporting, helping businesses make informed, data-driven decisions based on accurate insights.

Prerequisites

• Basic knowledge of Splunk fundamentals, including search processing language (SPL).
• Familiarity with data formats (e.g., JSON, XML, CSV) and common data ingestion methods.
• Understanding of network architectures and security protocols relevant to data management.
• Experience with SQL and basic data management principles would be helpful.

Table of Contents

1. Introduction to Data Management in Splunk

1.1 Overview of Data Management in Splunk
1.2 Key Components of Splunk’s Data Architecture
1.3 Understanding the Role of the Data Management Professional
1.4 Splunk’s Data Collection, Indexing, and Searching Capabilities

2. Data Ingestion and Onboarding

2.1 Data Collection Methods: Forwarders, HTTP Event Collectors (HEC)
2.2 Structured and Unstructured Data Ingestion
2.3 Handling Log Files and Streaming Data in Splunk
2.4 Integrating Data from External Sources into Splunk
2.5 Using Data Inputs and Monitoring Inputs in Splunk

3. Data Parsing and Indexing

3.1 The Parsing Pipeline and Data Extraction
3.2 Field Extraction and Data Enrichment
3.3 Creating and Managing Indexes in Splunk(Ref: Tensor Flow Developer Certificate-Splunk)
3.4 Data Models: Structuring Data for Easy Access and Analysis
3.5 Best Practices for Efficient Data Indexing

4. Data Governance and Quality Control

4.1 Data Integrity and Consistency in Splunk
4.2 Implementing Data Retention Policies and Archive Strategies
4.3 Monitoring Data Integrity and Resolving Issues
4.4 Handling Duplicate Data and Data Validation
4.5 Compliance with Data Governance Standards (GDPR, HIPAA)

5. Splunk Data Models and Data Preparation

5.1 Introduction to Splunk Data Models
5.2 Preparing Data for Analytics and Reporting
5.3 Creating Custom Data Models for Specific Use Cases
5.4 Optimizing Data Models for Performance and Scalability

6. Data Enrichment and Transformations

6.1 Leveraging Lookups and External Data Sources
6.2 Using Splunk for Data Transformation
6.3 Time-based Data Enrichment and Handling Event Data
6.4 Configuring Field Extractions and Lookups

7. Data Monitoring and Performance Optimization

7.1 Ensuring Data Availability and Reliability
7.2 Performance Tuning for Data Indexing and Searching
7.3 Monitoring Data Volume and Ingestion Rates
7.4 Best Practices for Query Optimization in Splunk

8. Reporting and Visualizations on Data Management Professional

8.1 Creating Dashboards and Reports in Splunk
8.2 Visualizing Data for Effective Decision Making
8.3 Integrating Data Management Insights into Splunk Reports
8.4 Automated Alerts and Reports Based on Data Insights

9. Splunk for Security and Compliance

9.1 Managing Security Data in Splunk
9.2 Leveraging Splunk for Compliance Reporting (SOX, HIPAA)
9.3 Implementing Security Best Practices for Data Management
9.4 Auditing and Monitoring Data Access and Changes

10. Hands-on Labs and Case Studies on Data Management Professional

10.1 Lab: Configuring Data Inputs and Onboarding Data into Splunk
10.2 Lab: Data Parsing, Indexing, and Field Extraction in Splunk
10.3 Lab: Creating Custom Data Models and Dashboards
10.4 Case Study: Real-World Applications of Data Management in Splunk

11. Certification Exam Preparation on Data Management Professional

11.1 Key Concepts and Exam Overview
11.2 Recommended Study Materials and Resources
11.3 Sample Questions and Mock Exam
11.4 Final Review and Exam Tips

Conclusion

Upon completing the Data Management Professional Certification in Splunk, learners will have acquired comprehensive skills to manage data effectively in a high-performance environment. They will be equipped to handle the full lifecycle of data collection, processing, and reporting while optimizing for performance, governance, and compliance. This certification prepares professionals to oversee large-scale data management projects within Splunk, ensuring they can provide actionable insights and drive informed decision-making.

Reference