--- title: "Data Modeling and Warehousing" description: "Design dimensional models for analytics: star and snowflake schemas, fact and dimension tables, slowly changing dimensions, and warehouse architecture." scfAsset: managed: false category: "guide" maintainers: - user: "can.celik1" external: true tags: ["STACKIT University", "Learning", "Data Warehousing", "Dimensional Modeling"] source_url: "https://framework.stackit.cloud/data-and-ai/assetcontainer/stackit/data-modeling-warehousing/" source_file: "docs/data-and-ai/assetcontainer/stackit/data-modeling-warehousing.mdx" --- ## Course Overview Data modeling is what separates good data engineers from great ones: this course teaches the art and science of designing data structures optimized for analytics and reporting, transforming normalized transactional data into dimensional models that enable fast business intelligence. It covers both the "how" and the "why" behind dimensional modeling — when to denormalize for performance and how to handle scenarios like slowly changing dimensions. Across four modules it moves from dimensional modeling foundations (facts, dimensions, star schemas) to building fact and dimension tables, handling slowly changing dimensions with the standard SCD strategies, and warehouse architecture patterns (Kimball vs. Inmon) with performance optimization. ### What You'll Learn - Design and implement star and snowflake schemas for data warehouses - Create effective fact and dimension tables that support business analytics - Handle slowly changing dimensions (SCDs) using appropriate strategies - Understand the transition from normalized to denormalized models - Build data warehouse architectures that scale - Optimize query performance in analytical environments ### Modules - Foundations of Dimensional Modeling - Building Fact and Dimension Tables - Slowly Changing Dimensions - Data Warehouse Architecture and Optimization