Denormalization is the process of combining data into a "wide" tables that are optimized for read workloads. Denormalized tables are best suited for [[Online Analytical Processing|OLAP]] systems where you need to analyze historical data, as updates are not required and data redundancy is not an issue. ## Denormalization Advantages - Faster reads of historical/analytical data because fewer joins needed ## Denormalization Disadvantages - Duplicate data %% wiki footer: Please don't edit anything below this line %% ## This note in GitHub <span class="git-footer">[Edit In GitHub](https://github.dev/data-engineering-community/data-engineering-wiki/blob/main/Concepts/Data%20Modeling/Denormalization.md "git-hub-edit-note") | [Copy this note](https://raw.githubusercontent.com/data-engineering-community/data-engineering-wiki/main/Concepts/Data%20Modeling/Denormalization.md "git-hub-copy-note")</span> <span class="git-footer">Was this page helpful? [👍](https://tally.so/r/mOaxjk?rating=Yes&url=https://dataengineering.wiki/Concepts/Data%20Modeling/Denormalization) or [👎](https://tally.so/r/mOaxjk?rating=No&url=https://dataengineering.wiki/Concepts/Data%20Modeling/Denormalization)</span>