The semantic layer is a term used to describe the [[Data Modeling|data model]] that takes multiple enterprise data source models and combines them into one unified model for the business. It traditionally is built in the [[Data Warehouse|data warehouse]] and used by reporting tools. Companies have been using semantic layers to manage data since the early 1990s. ## Semantic Layer Advantages - Reduces complexity - Makes it easier to find information for business users - Facilitates self-serve reporting - Improve aggregated query performance ## Semantic Layer Disadvantages - Requires regular maintenance %% 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%20Management/Semantic%20Layer.md "git-hub-edit-note") | [Copy this note](https://raw.githubusercontent.com/data-engineering-community/data-engineering-wiki/main/Concepts/Data%20Management/Semantic%20Layer.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%20Management/Semantic%20Layer) or [👎](https://tally.so/r/mOaxjk?rating=No&url=https://dataengineering.wiki/Concepts/Data%20Management/Semantic%20Layer)</span>