# What is schema drift? Definition and Examples | Silicon

URL: https://meetsilicon.com/glossary/schema-drift/

> Schema drift is any unplanned change to the structure of data, such as a renamed column, a dropped field or a changed data type, that can break the queries and dashboards built on it.

Drift often comes from upstream: an application team renames a field, or a vendor changes an export. In analytics, the damage shows up downstream as broken charts, empty reports or silently wrong numbers.
The most reliable defence is to check changes before they merge, against everything that depends on the changed model.
## Example
customer_id is renamed to account_id in fct_orders. Three dashboards that group by customer_id stop loading.
## Types of schema drift
Renamed columns and tables, dropped columns, changed data types (a string that becomes an integer), changed grain (one row per order becomes one row per order line), and new values in a field that downstream logic does not expect. The last two are the hardest to catch because nothing errors and the numbers simply change.
## How teams catch it
Tests in dbt catch some drift inside the warehouse. Observability tools catch drift after it has landed by watching tables. Checking pull requests catches it earliest, by comparing the proposed change with every dashboard, metric and saved query that depends on the affected columns before the change merges.
## How Silicon handles it
Silicon's schema protection comments on each pull request with the dashboards and metrics a change would break, and can draft a fix pull request that updates the affected content.
FAQ
## Schema drift questions
### Is schema drift the same as schema evolution? +
Schema evolution is a planned change that is handled on purpose. Schema drift is a change that reaches downstream users without that planning.
### Can dbt tests catch schema drift? +
Partly. Tests catch drift inside the dbt project, such as a column that becomes null. They do not see the dashboards and saved queries outside dbt that depend on a column.
## Related
- Data lineage ->
- Dashboard reliability ->
- Data observability ->
- What is schema drift in analytics? ->
- Schema protection ->
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