I beleive snowflake has its own distributed query engine, similar to say, big query.

It's a bit of a tricky comparison because snowflake, and a lot of other tools that get referred to as "modern data stack" are very vendor based. If you're using snowflake, you're probaby using it on snowflake provided architecture with a whole load of proprietary stuff. You can't "swap in" snowflake on the same hardware like you can with spark, daft, duckdb, polars etc.

That said, iirc benchmarks normally place it very similar to spark. It's distributed, so I'd be very surprised if it wasn't in the spark/daft ballpark rather than polars/duckdb.

Snowflake has their own sql engine and is more of a serverless option. Databricks started off with spark but now also has a sql engine(optional serverless) as well, they are using spark in the article.

The delta format is Databricks lakehouse file format, snowflake uses iceberg I believe.

Both Snowflake and Databricks also provide a ton of other features like ML, Orchestration and governance. Motherduck would be the direct competitor here.

Saying that there are now extensions to query snowflake or databricks data from duckdb for simple ad hoc querying.

Duckdb is fantastic and has saved me so many times strongly recommended.

I am curious as well about this, we use Snowflake, but as a software engineer I want to understand how Spark/Databricks is different, what are we missing out?

How we work with data is simple, if SQL+dashboard solves the problem then we do it in Snowflake, if we need something more advanced, then code + bunch of SQL.

Pretty sure ML engineers work in different ways, but I don't know that side well