# What is semantic layer? Definition and Examples | Silicon

URL: https://meetsilicon.com/glossary/semantic-layer/

> A semantic layer is a shared set of business definitions, such as metrics, dimensions and joins, that sits between a data warehouse and the tools that query it.

Instead of every dashboard or analyst writing their own SQL for "revenue", the definition is written once and every tool asks the semantic layer for it. The result is one answer to the same question, wherever it is asked.
Semantic layers matter even more with AI. A language model that queries raw tables has to guess what columns mean. One that queries a semantic layer works from definitions your data team has already approved.
## Example
net_revenue is defined once as the sum of net_amount in fct_revenue. Finance dashboards, sales reports and AI answers all use that definition.
## What goes into a semantic layer
A semantic layer usually holds four things: metrics (how a number is calculated), dimensions (the ways you can slice it, such as region or plan), entities and joins (how tables relate to each other), and access rules (who can see which rows and columns).
In a dbt project, much of this already exists as model descriptions, column types and tests. Tools like Silicon read that project and add metric definitions in the same YAML, so the semantic layer lives in version control next to the models it describes.
## Where it goes wrong
The most common failure is a semantic layer that only one tool can read. Definitions end up copied into a second BI tool, a spreadsheet and a notebook, and they drift apart within months. The second is a metric with no clear owner, defined once by an analyst who has since left, so no one knows which filters it applies.
## How Silicon handles it
Silicon uses your dbt project as the semantic layer. Metrics defined in column meta become governed metrics that dashboards, scheduled reports, the metrics catalog and the AI analyst all query, and each metric shows its definition and owner.
FAQ
## Semantic layer questions
### Do I need a semantic layer if I only have one BI tool? +
It still helps. Even with one tool, definitions written inside individual charts drift apart. A semantic layer keeps one definition per metric and makes it reusable when you add a second tool or an AI assistant.
### Is a semantic layer the same as a data warehouse? +
No. The warehouse stores the data. The semantic layer sits on top and describes what the data means and how to calculate metrics from it.
### Which semantic layers work with dbt? +
The dbt Semantic Layer (MetricFlow), Cube, Lightdash and Silicon all read dbt projects. They differ in where metrics are defined and which tools can query them.
## Related
- Metrics layer ->
- dbt ->
- Text-to-SQL ->
- Silicon semantic layer ->
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