Dimensional Modeling: Facts, Dimensions, and Grains
February 19, 2026Dimensional modeling is the most widely used approach for organizing analytics data. Developed by Ralph Kimball, it structures data into two types of tables:...
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Conceptual, Logical, and Physical Data Models Explained
February 19, 2026Most data teams jump straight from a stakeholder request to creating database tables. They skip the planning steps that prevent misalignment, redundancy, and...
Data Modeling for Analytics: Optimize for Queries, Not Transactions
February 19, 2026The data model that runs your production application is almost never the right model for analytics. Transactional systems are designed for fast writes : in...
Slowly Changing Dimensions: Types 1-3 with Examples
February 19, 2026Dimensions change. A customer moves cities. A product gets reclassified. An employee changes departments. How your data model handles these changes determine...
Data Modeling for the Lakehouse: What Changes
February 19, 2026Traditional data modeling assumed you controlled the database. You defined schemas up front, enforced foreign keys at write time, and optimized with indexes....
Data Modeling Best Practices: 7 Mistakes to Avoid
February 19, 2026A bad data model doesn't announce itself. It hides behind slow dashboards, conflicting numbers, confused analysts, and AI agents that generate wrong SQL. By ...