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Snowflake and Databricks are different, sometimes complementary technologies. You can store data in Snowflake & query it with Databricks for example: https://github.com/snowflakedb/spark-snowflake
Snowflake predicate pushdown filtering seems quite promising: https://www.snowflake.com/blog/snowflake-spark-part-2-pushin...
Think both these companies can win.
I’ve had a lot of success with Dask lately. It’s comparable to spark in some ways [0]. Being written in python and built on top of pandas/numpy it allows much more flexibility. It also has great tools built on top of kubernetes making deployment quick and easy [1].
[0] https://docs.dask.org/en/latest/spark.html
[1] https://github.com/dask/dask-gateway
The last point was for teams that only rely on notebooks, sorry if I didn't make that clear.
You're right that all those issues can be sidestepped if you build projects in version controlled Git repos, test the code, and deploy JAR / Wheel files.
Speaking of testing, can you let me know if this PySpark testing fix worked for you ;) https://github.com/MrPowers/chispa/issues/6
> * AWS has a managed Spark offering called EMR
There is also my rinky-dink open source project, Flintrock [0], that will launch open source Spark clusters on AWS for you.
It's probably not the right tool for production use (and you would be right to wonder why Flintrock exists when we have EMR [1]), but I know of several companies that have used Flintrock at one point or other in production at large scale (like, 400+ node clusters).
[0]: https://github.com/nchammas/flintrock
[1]: https://github.com/nchammas/flintrock#why-build-flintrock-wh...
I’m sorry for delay, will fix ASAP...
My point is that you can do that even without jars/wheels - you can do VC and tests of notebooks. For example, https://github.com/alexott/databricks-nutter-projects-demo