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databricks Platform · Updated Aug 7, 2026

Change data feed on materialized views (Beta)

You can now read a change data feed from a materialized view created in a Lakeflow pipeline or in Databricks SQL, using Databricks Runtime 18 LTS or above.

What it does

You can now read a change data feed from a materialized view created in a Lakeflow pipeline or in Databricks SQL, using Databricks Runtime 18 LTS or above. Use this to replicate materialized view changes to destinations outside Databricks, or to keep a history of materialized view changes for auditing and reporting. Materialized views use automatic change data feed, so the change data feed is available for any materialized view with row tracking enabled, without configuring each materialized view.

Why it matters

For an introduction to the AUTO CDC APIs, see The AUTO CDC APIs: Simplify change data capture with pipelines.

Key capabilities
For a materialized view created
For a materialized view created in a pipeline, set pipelines.externalMetadata.enabled in the pipeline configuration:
For a standalone materialized view
For a standalone materialized view, run the following command once on each materialized view. This command requires the external…
Architecture
Databricks Platform → Platform
In short

You can now read a change data feed from a materialized view created in a Lakeflow pipeline or in Databricks SQL, using Databricks Runtime 18 LTS or above. Use this to replicate materialized view changes to destinations outside Databricks, or to keep a…

Read the full documentation ↗
Key takeaway
You can now read a change data feed from a materialized view created in a Lakeflow pipeline or in Databricks SQL, using Databricks Runtime 18 LTS or above.
2026-08-07-change-data-feed-on-materialized-views-beta Generated by the Databricks Updates Agent · browse all updates