UPCOMING WEBINAR
14 Hrs to 90 Sec: Eliminating SQL-to-Databricks Pipeline Lag [Live Webinar]
If your AI models on Databricks feel less accurate in production than in testing, the model isn’t the problem. The data is temporally wrong.
Relying on batch replication for SQL databases creates a structural freshness floor that you cannot bypass simply by running jobs more frequently.
Batch polling scans full tables, struggles with missing updated_at columns, and completely misses hard deletes—leaving canceled orders and removed products sitting in your Delta tables forever as “ghost records”.
Join us to see how replacing batch ETL with log-based Change Data Capture (CDC) closes your data gap from 14 hours down to under 2 minutes.
Key Takeaways
-
Understand why log-based CDC reads the transaction log instead of querying your production tables, neutralizing the impact on your source database.
-
See how CDC accurately captures hard deletes to keep your Databricks data matching reality.
-
Watch how DBSync connects an operational database to Databricks in under an hour—without Kafka, without Debezium, and without custom Spark Streaming code.
-
Discover the pipeline architecture that delivers a median latency of ~90 seconds from a database commit straight to a Delta table upsert.