[solidangle]

When was the last time you used Databricks? You should definitely try it again. Their product offering has improved a lot in the past few years.

> broad feature set

My experience is that the feature sets of Snowflake and Databricks are very similar. Both have time travel support. Snowflake has materialized views, but Databricks has Delta Live Tables. Databricks has a distributed Pandas API, but Snowflake recently introduced Snowpark. Databricks also has autoscaling and they recently launched a serverless offering that makes autoscaling super fast aswell.

[glogla]

Snowflake has much more advanced data security - table, column, and row level security and dynamic data masking policies. The zero-copy cloning is also pretty useful for CI/CD (pretty much the one practical way to do blue-green deployment for data application).

Databricks has some interesting features (we were originally interested in it as "nice UI" for our AWS data lake for citizen data scientists - using it for industrialized processing was price impractical compared to AWS Glue) but the security seems lacking - it goes just table level and only in SQL and Spark, with R you can't have security at all.

I really liked the Databricks UI and integrated visualizations, though, that's where they are better than Snowflake I think. Of course, they gained those by buying open source Redash.io and ending it.

The part that ended our PoC with them was when they gave us a price quote for expected number of users, the management was like "ok that sounds reasonable" until I told them that's just license and does not include EC2 costs - the real cost would be at least twice. That made everyone angry.