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Granularity in dimensional modeling defines what each row in a fact table represents, forming the foundation for data warehouse design. Atomic grain captures data at the most detailed level, enabling flexible querying, while summary grains optimize performance through aggregation. The concept relates to normalization approaches, where denormalized tables (like One Big Table) contrast with traditional relational designs. Modern columnar OLAP systems leverage denormalization and nested data types to achieve appropriate granularity levels. Establishing grain before selecting dimensions or facts ensures consistency and alignment across the entire dimensional model.

Granularity

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