Having worked with a team using Snowpark, there are a couple things that bother me about it as a platform. For example, it only supported Python 3.8 until 3.9/10 recently entered preview mode. It feels a bit like a rushed project designed to compete with Databricks/Spark at the bullet point level, but not quite at the same quality level.

But that's fine! It has only existed for around a year in public preview, and appears to be improving quickly. My issue was with how aggressively Snowflake sales tried to push it as a production-ready ML platform. Whenever I asked questions about version control/CI, model versioning/ops, package managers, etc. the sales engineers and data scientists consistently oversold the product.

Disclaimer: Snowflake employee here. You can add any Python library you want - as long as its dependencies are also 100% Python. Takes about a minute: pip install the package, zip it up, upload it to an internal Snowflake stage, then reference it in the IMPORTS=() directive in your Python. I did this with pydicom just the other day - worked a treat. So yes, not the depth and breadth of the entire Python ecosystem, but 1500+ native packages/versions on the Anaconda repo, plus this technique? Hardly a "walled garden".