Snowflake or Databricks? An Honest Comparison (From 200+ Migrations)

It's the question in every data platform decision, and it generates more heat than insight: Snowflake or Databricks?

Here's an honest answer from having migrated a lot of both. Short version: they're both excellent, the gap is narrower than either vendor implies, and the decision matters far less than what you do next. But there are real differences, and your workload should pick.

Where each genuinely leads

Snowflake is stronger when your centre of gravity is SQL analytics. Reporting, regulatory extracts, BI over structured data. It's operationally calmer — less to tune, fewer knobs, and your existing SQL people are productive on day one. Separation of storage and compute means finance's month-end doesn't fight the analysts' model run. If your data is mostly relational and your consumers are mostly analysts, it's the shorter path.

Databricks is stronger when your centre of gravity is ML and streaming. Real-time scoring, telematics ingestion, heavy feature engineering, deep learning on documents and images, and one platform for data plus AI. If your roadmap is genuinely agentic and real-time, gravity pulls this way. It expects more engineering maturity and rewards it.

Both have converged aggressively. Snowflake does Python and ML now; Databricks does SQL warehousing well. Anyone claiming a chasm is selling something.

Domain-specific considerations (insurance, in my case)

Your document problem. Insurance drowns in unstructured documents — policies, medical records, police reports, estimates. If extracting them at scale is central, Databricks' ML-native shape has an edge.

Your streaming needs. FNOL, fraud, telematics. Databricks handles this natively; Snowflake can, but it's less its home ground.

Your existing analysts. They live in SQL and have decades of it. Snowflake meets them where they are, and that adoption curve is worth real money.

Your regulator. Both can satisfy lineage and audit requirements — but neither gives it to you free. That's architecture and discipline, not a checkbox on either platform.

Your legacy core. Whichever you pick, you're doing CDC off a mainframe or Oracle stack. Both handle it. This won't decide it.

The thing that actually decides success

Now the honest part, which is uncomfortable for both vendors.

Across the migrations we've done, the platform choice has almost never been the reason a project succeeded or failed. What decides it, every time:

• Did you resolve identity? Is there a stable customer key across products? Without it, both platforms give you the same fragmented mess at higher cost.
• Did you remodel, or lift-and-shift? Copying a 1990s schema into either platform gets you a 1990s schema with a cloud bill.
• Did you measure data quality? Neither platform tells you your roof-age field is 30% blank.
• Did you wire in lineage and decision logs from the start? Both support it. Neither does it for you. Retrofit is not possible.
• Did you split real-time from batch by decay rate? Both let you build the wrong thing at scale.

Teams that get these right succeed on either platform. Teams that don't fail on either — then blame the platform and migrate to the other one, where they fail again for exactly the same reasons.

A decision rule, if you want one

Answer this honestly: in two years, will most of your value come from analysing data or from acting on it in real time?

Analysing — reports, regulatory extracts, BI: Snowflake. You'll ship faster and sleep better.

Acting — real-time fraud, telematics, agentic workflows, document ML at scale: Databricks. The extra engineering cost buys headroom you'll need.

Genuinely both, at scale, with a real platform team: either works. Pick on your team's existing skills, because that's the variable that compounds.

The advice nobody sells

If you're spending months on this decision, that's the actual problem. The evaluation cost is exceeding the difference in outcome. Pick the one your team can operate, and put the saved months into the customer key, data quality, and lineage — the things that will still determine whether this works in three years.

Nobody's AI initiative failed because they picked Snowflake over Databricks. Plenty have failed because the customer key never got built while everyone argued about the platform.

200+ migrations across Snowflake, Databricks, BigQuery, Redshift and Synapse — and we'll tell you honestly which fits. More at IntelliBooks.