Git-like Repository for Data Lake Management and Quality Control

Overview

Discover how to manage data lakes efficiently using git-like operations, ensuring data quality and simplifying experimentation while preventing corruption in complex distributed systems.

Syllabus

• Introduction to Data Lakes
Overview of Data Lakes and Their Importance
Common Challenges in Data Lake Management
• Introduction to Version Control Concepts
Basics of Version Control Systems
Introduction to Git and Git-like Operations
• Data Lake Management with Git-like Tools
Setting Up a Git-like Repository for Data Lakes
Key Operations: Commit, Branch, Merge, and Revert
• Ensuring Data Quality in a Data Lake
Data Validation Techniques
Implementing Monitoring and Alerting Systems
• Experimentation in Data Lakes
Strategies for Safe Experimentation
Tracking Experiments and Changes over Time
• Preventing Data Corruption in Distributed Systems
Challenges of Distributed Data Management
Techniques for Ensuring Data Integrity and Consistency
• Case Studies and Real-World Applications
Industry Examples of Git-like Data Lake Management
Lessons Learned from Successful Implementations
• Hands-On Lab: Setting Up a Git-like Data Management System
Exercise: Initializing a Repository
Exercise: Committing, Branching, and Merging Data Changes
• Future Trends and Technologies in Data Lake Management
Emerging Tools and Practices
The Role of AI and Machine Learning in Data Quality Control
• Course Summary and Best Practices
Recap of Key Concepts and Techniques
Developing a Personal Action Plan for Data Lake Management
• Q&A and Course Feedback Session

Subjects

Business