Making data warehouses less terrible with modular thinking
Original: Modular Dimensional Data Modeling
Summary
Modular Dimensional Data Modeling offers a revolutionary approach to data engineering, empowering analytics professionals to build scalable, maintainable data models. By leveraging modular design principles and dimensional modeling techniques, this methodology enables streamlined data transformation, effortless integration of new data sources, and enhanced data accessibility for business intelligence and advanced analytics. For data-driven organizations seeking to unlock the full potential of their data, Modular Dimensional Data Modeling provides a strategic edge in navigating the complexities of modern data ecosystems.
Who This Is For
Analytics Engineers
Data Engineers
Business Analysts
Key Takeaways
- Apply software engineering's Single Responsibility Principle to dimensional modeling for more maintainable data models
- Transform traditional Kimball star schemas into modular, activity-based fact tables that can be composed together
- Build separate fact tables for different business events that can be independently maintained and easily combined
- Overcome the rigidity of traditional dimensional models while keeping their familiar structure and BI tool compatibility
Tools & Technologies
Kimball dimensional modeling Star schemas One Big Table (OBT) Tableau PowerBI Looker Columnar databases Activity Schema (SOMA)
Topics Covered
data-modeling dimensional-modeling data-warehousing semantic-layer
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