--- title: Granularity | daily.dev description: Granularity in dimensional modeling defines what each row in a fact table represents, forming the foundation for data warehouse design. Atomic grain captures... canonical: https://daily.dev/posts/jrrxleowt twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Granularity | daily.dev og:description: Granularity in dimensional modeling defines what each row in a fact table represents, forming the foundation for data warehouse design. Atomic grain captures... og:url: https://daily.dev/posts/jrrxleowt og:image: https://api.daily.dev/og/posts/JRrXLEowt.png og:image:alt: Post cover image og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Granularity **[Data Engineering](https://daily.dev/sources/sspdata)** · [@sspaeti](https://daily.dev/sspaeti) · 0 upvotes · 0 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. 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