Full disclosure up front: we're a Databricks shop — our engineers are certified on it and it's where we build. That's exactly why this comparison is worth writing honestly: we've watched companies choose each platform for good and bad reasons, and the bad reasons are always the same.
The convergence is real, the centers of gravity aren't
On paper the platforms now do each other's jobs: Snowflake runs Python and ML workloads, Databricks ships a SQL warehouse with BI-grade performance. But each platform still has a center of gravity, and you will live near it.
Snowflake's center is SQL analytics. If your world is structured data, dashboards and analysts who live in SQL, Snowflake is genuinely excellent: near-zero administration, predictable behavior, warehouses your analysts cannot break.
Databricks' center is engineering on data. Streaming pipelines, unstructured data, machine learning, and transformations complex enough to deserve version control and tests. If your roadmap includes "then we build ML/AI features on this," you'll end up doing engineering work — and Databricks is where that work is native rather than bolted on.
The questions that actually decide it
Who works on the data? Count heads. Mostly SQL analysts → Snowflake will make them productive on day one. Data engineers writing Python → Databricks gives them a real development environment instead of a SQL console with extensions.
What's the ugliest data you'll touch in two years? If the answer is "CSV exports and a CRM," either platform is fine. If it's event streams, documents, images or model training data, be honest about that now — migrating platforms later costs more than choosing right.
How does the bill grow? Both are consumption-billed, and both will punish you for ungoverned usage — Snowflake through warehouses someone left running on autopilot, Databricks through clusters someone sized by vibes. The difference we see in practice: Databricks gives engineers more knobs, which means more savings if someone owns cost engineering, and more waste if nobody does. Budget for governance either way — on Databricks that means Unity Catalog, which is no longer optional.
How much lock-in can you tolerate? Databricks stores data in open formats (Delta Lake, now interoperable with Iceberg) on your own cloud storage — your data remains yours to point other engines at. Snowflake has moved toward open formats too, but its gravity is still data living inside Snowflake. For a company that expects to renegotiate in five years, open storage is a real bargaining chip.
The bad reasons we keep seeing
Choosing Databricks because "we might do AI someday" with no engineer to build it — you'll pay a complexity tax for an option you never exercise. Choosing Snowflake because the finance team liked the demo dashboard — and then hiring data engineers who spend their lives working around it. Choose for the team you'll actually have.
A structure that works either way
Whichever engine you pick, the architecture that keeps you sane is the same: raw data preserved untouched, cleaned and conformed layers on top, business-ready tables at the end. We wrote a practical guide to the medallion architecture that covers how we structure it.
If you're weighing this decision with real workloads on the table, we're happy to look at them with you — including the cases where the honest answer is Snowflake.
Originally published on the Zephico blog.
