Databricks & Snowflake · Chapter 10 of 10
Choosing, migrating, and the interview
How to answer the question you will be asked, and what to actually learn.
You will be asked which you would choose. The strongest answer establishes that the comparison is largely stale, then answers on the dimension that still separates them.
What still genuinely differs
| If your primary user is… | Lean | Because |
|---|---|---|
| Engineers writing code and models | Databricks | Native Python/Scala, ML lifecycle, heavy unstructured transformation |
| A large analyst population on SQL | Snowflake | Concurrency, near-zero operations, predictable behaviour |
| Streaming as a first-class need | Databricks, usually | Structured Streaming and DLT are more mature for it |
| Sharing data with other organisations | Snowflake, usually | Secure sharing without copying is genuinely differentiated |
| A small team with no platform engineer | Snowflake | Far less to operate; Databricks rewards tuning you may not have time for |
Migration, honestly
Most consolidation programmes stall at roughly 70%, because the value is front-loaded and the long tail is unfunded — and then you carry both plus a half-finished migration, which is worse than either. If you are asked about migrating, the credible answer funds the finish before starting, publishes a decommission date, and does the long-tail work centrally rather than delegating it to teams for whom it is priority eleven.
What to actually learn, in order
- Pick one and go deep. Claiming both shallowly is transparent within two questions. Say which one you are deep in.
- Learn the cost model of that one — it is where enterprise conversations go and where most candidates are weakest.
- Learn one table format properly, Delta or Iceberg. Chapter 3 is the mechanism; then actually create a table, evolve its schema, delete a row and time-travel it.
- Read one query profile end to end on your chosen platform and be able to say where the time went.
- Then learn enough of the other to discuss the trade-off honestly. Two hours of reading is sufficient for that and it is the difference between sounding partisan and sounding experienced.
A free way to actually practise
Both offer free tiers — Databricks Community Edition and a Snowflake trial. Load a public dataset of a few hundred million rows, write a badly-performing query, look at the profile, fix it, and record the before and after.
That single exercise gives you a concrete performance story, which is what every technical round in this area is really asking for.