I actually see them as variations on the same architecture. Databricks keeps their metadata in files, Snowflake keeps theirs in a database, but they both, ultimately, are querying data stored in a columnar format on blob store (and, to be fair, Snowflake have been doing that with ACID-compliant SQL for a lot longer than Databricks). So using SQL over blob at high performance has been around for a while.
Databricks say their solution is better because it's open (though keep the optimizations you need to run this at scale to themselves, i.e. is ultimately proprietary). Snowflake says theirs is better because it's a fully managed service, meaning no infrastructure to procure or manage, is fully HA across multiple data centers by default etc.
Databricks push 'open' but really still want you to use their proprietary tech for first transforming into something usable (Parquet/Delta) and then querying with Photon/SQL, though you can also use other tech. With Snowflake you can just ingest and query, but it has to be through their engine.
Customers should do their own valudation and see which one fits their needs best.
Databricks say their solution is better because it's open (though keep the optimizations you need to run this at scale to themselves, i.e. is ultimately proprietary). Snowflake says theirs is better because it's a fully managed service, meaning no infrastructure to procure or manage, is fully HA across multiple data centers by default etc.
Databricks push 'open' but really still want you to use their proprietary tech for first transforming into something usable (Parquet/Delta) and then querying with Photon/SQL, though you can also use other tech. With Snowflake you can just ingest and query, but it has to be through their engine.
Customers should do their own valudation and see which one fits their needs best.