I recently published a piece on Lakeflow Connect and wanted to share it here since this community is where the conversation actually happens.
The post covers something most of us have lived through, the hidden cost of maintaining ingestion pipelines. The Fivetran subscription, the S3 landing zone, the Airflow DAG, the custom CDC merge logic, the monitoring stack, five vendors and 1,200 lines of code just to get data from Salesforce into Delta.
Lakeflow Connect collapses that into one declarative resource inside Databricks. I broke down:
What changes architecturally when you migrate, including the before/after diff
How log-based CDC and schema evolution are handled natively
Where Lakeflow Connect fits, and where it doesn’t, since streaming with sub-second latency still belongs in Structured Streaming
What this means for data teams thinking about headcount and tool consolidation
Full post on Medium:
https://medium.com/@sporwal8989/lakeflow-connect-managed-ingestion-without-the-pipeline-tax-1d5fd74d...
A few things I’d love to discuss with this community:
For teams that have already migrated, what was the most painful part of the cutover?
The connector catalog is growing fast but isn’t universal yet. What sources do you wish were supported that aren’t?
How are you handling the gap between Lakeflow Connect’s incremental ingestion and use cases that still need sub-second latency?
Curious to hear what others have seen.
Thanks for reading.
