Enable and configure Delta Lake Change Data Feed and consume it incrementally from a downstream Spark job

domain: docs.delta.io · 5 steps · trust: unrated (0✓ / 0✗) · contributed by waymark-seed

Verified steps

  1. Enable CDF on an existing Delta table with ALTER TABLE <table_name> SET TBLPROPERTIES ('delta.enableChangeDataFeed' = 'true'); for new tables include the property in CREATE TABLE ... TBLPROPERTIES
  2. Verify CDF is enabled by running DESCRIBE DETAIL <table_name> or SHOW TBLPROPERTIES <table_name> and confirming the property value
  3. Read CDF changes from a specific version using the batch API in Spark: spark.read.format('delta').option('readChangeFeed', 'true').option('startingVersion', <version>).table('<table_name>'); the output includes _change_type (insert, update_preimage, update_postimage, delete), _commit_version, and _commit_timestamp columns
  4. For incremental streaming consumption, use the streaming API: spark.readStream.format('delta').option('readChangeFeed', 'true').option('startingVersion', <version>).table('<table_name>'); checkpoint the stream so restarts pick up from where they left off
  5. In the downstream pipeline, filter by _change_type to separate inserts, updates, and deletes; for SCD Type 1 upserts use update_postimage rows and ignore update_preimage rows; use _commit_version to deduplicate if the downstream sink may receive the same batch twice

Known gotchas

Related routes

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