Glossary

One big table

A denormalized modeling approach that joins facts and dimensions into a single wide table instead of many.

Also called: OBT, wide table

One big table (OBT) is a denormalization pattern that pre-joins a fact table with all of its relevant dimensions into a single, wide table, so a query needs no joins at all — every row already carries the customer's region, the product's category and the transaction amount together. It sits at the opposite end of the spectrum from a normalized snowflake schema.

OBT trades storage and update cost for query simplicity and speed: each fact row repeats every dimension attribute, inflating size, and a change to a dimension attribute, like a corrected product category, must be rewritten across every historical row that references it, rather than in one dimension row. This makes it a poor fit for slowly changing attributes unless combined with an explicit slowly changing dimension strategy.

The pattern has become more common as columnar storage formats compress repeated values cheaply and modern query engines scan wide tables efficiently, making join elimination worth more than the storage saved by normalizing. OBT suits BI tools and self-service users who query a fixed, well-understood set of metrics; it suits less well when the same base facts need to be joined to many different, evolving dimensions, where a star schema stays easier to maintain.

Last reviewed September 22, 2026

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Related terms

Denormalization Deliberately introducing redundancy into a data model to reduce joins and speed up read queries. Fact table The table in a dimensional model that stores measurable, numeric events, each row linked to the dimensions describing it. Dimension table A table of descriptive attributes, such as customer or product details, that a fact table references for context. Star schema A dimensional modeling pattern with a central fact table connected to descriptive dimension tables, shaped like a star. Columnar storage A file and database layout that stores each column's values together, instead of storing each row together. Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers.