Change data feed. When switched on for a Delta table it records which rows changed from version to version, exposing extra columns that give the kind of change and the commit version. You can query it with table_changes(), stream from it, or feed it to AUTO CDC.
Also called change data feed.
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Terms in this definition
- Event
Table in Log Analytics where entries from Windows event logs are kept.
- Azure Artifacts feed
Holds packages (Cargo, Universal, Python, Maven, npm, NuGet) in Artifacts; it belongs to a project or the organisation and supports views, feed roles and upstreams.
- AUTO CDC
The Lakeflow pipelines API for change data capture: it merges incoming changes into a streaming table, keeping either just the latest values (SCD 1) or full history (SCD 2), and uses an ordering column to cope with records that arrive late. A Python-only snapshot variant also exists.
Related terms
- AUTO CDC FROM SNAPSHOT
A Lakeflow pipelines API, available only in Python, for sources without a change data feed: it compares one snapshot with the next and maintains an SCD type 1 or type 2 target from the differences.
- Optimal refresh
Turned on by default for materialized lake views, this setting chooses on every run between skipping, an incremental refresh and a full one. Source tables need Delta change data feed for incremental refresh to happen, and disabling the setting makes each run a full rebuild.
- Temporal table
Lets you ask what the data looked like on a past date by keeping its change history. Azure Databricks options are SCD type 2, change data feed, or Delta history with time travel; retention and purpose vary.