Data Modeling and Schema Design for Analytics
Chapter 1: Foundations of Analytical Architecture
Operational vs Analytical Workloads
Row-Oriented vs Columnar Storage
The Role of Denormalization
Hands-on Practical: Analyzing Query Plans
Chapter 2: Dimensional Modeling Core
The Grain of Data
Fact Tables Fundamentals
Dimension Tables Structure
Star Schema Architecture
Snowflake Schema Variations
Hands-on Practical: Designing a Star Schema
Chapter 3: Advanced Dimension Handling
Surrogate Keys vs Natural Keys
Slowly Changing Dimensions Type 1
Slowly Changing Dimensions Type 2
Date and Time Dimensions
Role-Playing Dimensions
Hands-on Practical: Implementing SCD Type 2
Chapter 4: Complex Fact Table Patterns
Transactional Fact Tables
Periodic Snapshot Fact Tables
Accumulating Snapshot Fact Tables
Factless Fact Tables
Hands-on Practical: Building an Accumulating Snapshot
Chapter 5: Physical Design and Optimization
Partitioning Strategies
Clustering and Sorting Keys
Nested Data Structures
One Big Table OBT Methodology
Hands-on Practical: Optimization Tuning
Fact Tables Fundamentals
Was this section helpful?
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Ralph Kimball and Margy Ross, 2013 (Wiley) - This book is the authoritative source for dimensional modeling, providing comprehensive details on fact table design, measures, grain, additivity, and different fact table types.
- Fundamentals of Data Engineering: Planning and Building Analytics Systems, Joe Reis, Matt Housley, 2022 (O'Reilly Media) - Offers practical guidance on modern data engineering practices, including essential principles of data modeling and dimensional design for analytical systems.
- Fundamentals of Database Systems, Ramez Elmasri and Shamkant B. Navathe, 2016 (Pearson) - A widely recognized textbook covering database systems, with sections dedicated to data warehousing and online analytical processing (OLAP), which includes dimensional modeling concepts.
© 2025 ApX Machine LearningEngineered with