Snowflake is the king of ease-of-use and SQL-based analytics. Databricks is the powerhouse for Spark-based data engineering and machine learning on a Lakehouse.
I've worked with both of these platforms in production for years, and the Snowflake-vs-Databricks debate keeps shifting under my feet. Five years ago this was an easy answer: Snowflake for the warehouse, Databricks for the notebooks. Today the lines are blurred enough that I've seen teams pick the wrong one and spend six months regretting it.
Snowflake was born as a pure cloud data warehouse. Its defining idea was separating storage from compute so you could scale each independently. What it got right from day one was the developer experience — SQL works, warehouses spin up in a couple of seconds, and there are almost no knobs to turn. The entire product philosophy is "just load your data and write SQL." In the last few years Snowflake pushed into Python and ML with Snowpark, Cortex, and container services, but the DNA is still SQL-first.
Databricks came from a completely different angle. The founders created Apache Spark, so the platform was always engineered around distributed compute for engineers, not analysts. Databricks pioneered the Lakehouse idea — store everything in open table formats (Delta Lake) on cheap object storage, and run whatever engine you want on top. They've added SQL warehouses, serverless, and BI-style features aggressively over the last three years.
When I help teams pick between these two, the decision almost never comes down to "performance." Both are fast enough. What matters is:
I helped a mid-sized fintech migrate from on-prem Hadoop to the cloud. Their analytics team picked Snowflake. Their ML team picked Databricks. Two years later, the ML team's Databricks bill was 3x the analytics team's Snowflake bill — and it was the right choice. They were training dozens of models a week on terabyte-scale features. Forcing that workload onto Snowflake (via Snowpark) at the time would have been slower and more expensive. On the flip side, I've also watched a BI-heavy team try to run their executive dashboards off a Databricks SQL warehouse, and the cold-start latency drove the CFO insane until they moved the last-mile serving to Snowflake.
There isn't one winner here, only a right answer for your specific situation. I've laid out the patterns I actually see work below.
Choose Snowflake if:
This is increasingly the answer for bigger orgs. Databricks handles raw ingestion and ML; refined, modeled marts land in Snowflake for BI and external sharing. With Iceberg external tables and Unity Catalog-to-Snowflake connectors, the cost of running both has dropped considerably. If you have the team to operate two platforms, you often get the best of both. If you don't, pick one and commit — splitting a small team across two platforms is usually worse than a suboptimal single-platform choice.
Databricks is meaningfully better for serious ML work. You get managed MLflow, GPU clusters, distributed training via Spark or Ray, and first-class notebooks. Snowflake's Snowpark ML and Cortex have closed the gap for lightweight in-database ML and LLM calls, but if you're doing anything past gradient-boosted trees on tabular data, Databricks wins.
Yes, and it's the architecture I increasingly see at mid-to-large companies. A common split: Databricks owns raw ingestion, streaming, and ML; Snowflake owns refined marts, BI, and external data sharing. With Iceberg-format external tables on both sides you can share data without physical copies — but your team size must justify running two platforms.
Both are strong but differently. Databricks Unity Catalog is the best cross-platform governance layer for open data if you want to cover lake tables and ML artifacts with one model. Snowflake's native governance (dynamic masking, row access policies, object tagging, access history) is more mature for SQL workloads and easier for a small team to operate day-to-day.
It depends entirely on workload shape. For steady BI and reporting on clean data, Snowflake is usually cheaper because warehouses auto-suspend in seconds and there's no idle cost. For heavy data engineering (raw to silver transformations on TBs per day) or ML training, Databricks is typically cheaper — Spark's engine is well-matched to that work and Photon plus spot nodes drops compute cost materially. The single biggest lever on either platform is right-sizing and aggressive auto-suspend/termination, not the platform choice itself.
For most workloads, yes. Photon plus Serverless SQL Warehouses produce sub-second latency on well-modeled tables. The two places Snowflake still feels better for BI are: (1) concurrency — Snowflake multi-cluster warehouses auto-add clusters under load with zero tuning, and (2) cold-start behavior — Snowflake warehouses resume faster than Databricks clusters historically. If your dashboards need to serve hundreds of simultaneous users with p99 latency guarantees, Snowflake is still my default pick.
You can, but it takes real work. Snowflake's native storage is proprietary (FDN format). You'd need to unload to Parquet or Iceberg in external stages, which is well-supported but adds operational overhead. The Iceberg external-table integration Snowflake shipped in the last two years materially changed the lock-in story — if portability is a must-have, store your base tables as Iceberg from day one and Snowflake will read and write them.
Snowpark is great if your team already lives in Snowflake and you want Python UDFs, stored procedures, and in-DB transformations without spinning up a separate Spark cluster. It won't match Databricks for workloads that need true distributed Spark — complex joins on massive fact tables, heavy shuffles, or iterative ML training. My rule of thumb: if you're replacing dbt SQL with imperative Python logic on <100 GB at a time, Snowpark is fine. If you're replacing a Hadoop or EMR job, stay in Databricks.