Moving from batch → event-driven / CDC on Databricks usually means adopting streaming + incremental processing across the Bronze → Silver → Gold (Medallion) layers.

Key design factors to capture upfront

• Event source: Kafka / Event Hubs / Kinesis / Debezium / app events

• CDC strategy: source-side CDC vs Delta Change Data Feed (CDF)

• Exactly-once & ordering: idempotent writes, keys, watermarking

• Schema evolution: schema enforcement vs evolution at Bronze

• Data quality: quarantine bad records early (expectations)

• Scalability & recovery: checkpoints, replay, backfills

• Latency vs cost: micro-batch vs continuous triggers

• Governance: Unity Catalog, lineage, access controls

Typical Databricks pattern

• Bronze: Event ingestion using Auto Loader / streaming, raw append

• Silver: Apply CDC using Delta Live Tables (apply_changes) or Delta CDF

• Gold: Incremental aggregates / serving tables (streaming or triggered)

Recommended Databricks documentation

• Medallion architecture:
  https://docs.databricks.com/lakehouse/medallion.html

• Auto Loader & streaming pipelines (Lakeflow / DLT):
  https://docs.databricks.com/ldp/tutorial-pipelines.html

• Delta Lake Change Data Feed (CDF):
  https://docs.databricks.com/delta/delta-change-data-feed.html

• CDC with Delta Live Tables (apply_changes):
  https://www.databricks.com/blog/2022/04/25/simplifying-change-data-capture-with-databricks-delta-liv...

• Lakeflow / DLT CDC APIs:
  https://docs.databricks.com/ldp/cdc.html

• Data quality & expectations:
  https://docs.databricks.com/ldp/expectations.html

This combination gives you event-driven ingestion, scalable CDC, built-in data quality, and recoverability while staying fully aligned with Databricks best practices.