Everyone's rushing their Snowflake to Databricks migration, and they're setting themselves up for failure.
After leading multiple enterprise migrations to Databricks last quarter, here's what shocked me: The technical lift isn't the hard part. It's the hidden organizational resistance.
Last month, our 'perfectly planned' migration imploded when we discovered 60% of our engineers were using undocumented Snowflake stored procedures. Classic shadow IT coming back to haunt us!
The landmines nobody warns you about:
The Hidden Complexity Tax -
- Found 100+ hardcoded Snowflake references in production jobs
- 40% of our downstream data pipelines broke due to schema reference changes
- Shadow IT workflows we never knew existed suddenly surfaced
After doubling the project timelines, here's what actually works:
"Lift and Shift Everything" Tried this on our first migration. Result? Two weeks of production chaos when we discovered nested views weren't resolving correctly in Databricks.
The Big Bang Approach "Let's do it all at once!" Spoiler: Don't. We had much better success with 90-day progressive migrations, starting with least critical datasets.
We now spend 50% of migration time on discovery and impact analysis. Controversial? Yes. But our last migration had zero critical incidents.
Unity Catalog's stricter governance model will expose years of technical debt in your Snowflake environment. But that's actually the best part - it forces modernization.
The approach that saved us:
- Automated dependency mapping (found 40% more than manual audit)
- Two-week parallel runs for critical workloads
- Zero-trust verification of all stored procedures
- Progressive migration by data criticality
Currently seeing:
- 65% faster query performance
- 30% better resource utilization
- 90% reduction in access-related incidents
What migration nightmares have you faced moving to Databricks? Do share your stories
