Data Warehouse Fundamentals provides a clear and practical introduction to the core concepts that power modern data storage, analysis, and decision-making systems. Designed for beginners, analysts, and aspiring data professionals, this course explains how data warehouses are structured, why organizations rely on them, and how they support large-scale reporting and business intelligence.
 
You’ll explore the foundational components of data warehouse architecture, including fact and dimension tables, star and snowflake schemas, and the role of staging environments. The course also guides you through essential ETL processes—extracting, transforming, and loading data—along with best practices for data integration, cleansing, and workflow automation.
 
With a focus on performance and optimization, you’ll learn how storage structures, indexing, partitioning, and query strategies impact overall speed and efficiency. By the end, you’ll have a strong understanding of how data warehouses operate and how they help organizations turn raw data into reliable insights.
 
What You’ll Learn
Foundations of data warehouse design and architecture
Star, snowflake, and other schema models
ETL principles and data integration workflows
Storage optimization techniques for large datasets
Query performance considerations and best practices
How data warehouses support BI, reporting, and analytics