Data Modeling

How data is structured: entities, schemas, normalization, and dimensional design for analytics.

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Fundamentals0/2 · 0%

Schema Design0/1 · 0%

Dimensional Modeling0/2 · 0%

Modeling for Scale0/1 · 0%

Fundamentals

Beginner6 min read

Introduction to Data Modeling

What data modeling is, why the same raw data can be organized many different ways, and why the choice matters for correctness, performance, and how easily people can answer questions with it.

Beginner6 min read

Conceptual, Logical, and Physical Models

The three layers of abstraction that separate 'what the business means by a customer' from 'what column type stores their ID' — and why keeping them distinct avoids costly rework.

Schema Design

Intermediate8 min read

Normalization vs Denormalization

The classic tradeoff between eliminating redundancy for data integrity and introducing redundancy for query speed — and why analytical systems deliberately favor the opposite of what transactional systems want.

Dimensional Modeling

Intermediate7 min read

Star Schema vs Snowflake Schema

The two most common ways to organize a dimensional model for analytics — one favors fewer joins and simplicity, the other favors less redundancy — and how to decide between them.

Intermediate8 min read

Facts and Dimensions Deep Dive

The building blocks of dimensional modeling: what makes something a fact vs. a dimension, the different grains and types of fact tables, and how to choose the right grain for a fact table.

Modeling for Scale

Advanced9 min read

Slowly Changing Dimensions (SCDs)

Dimension attributes change over time — a customer moves cities, a product's price changes. SCD types define how a dimensional model should handle that change, and why the choice affects historical reporting.