GitHub user dosubot[bot] added a comment to the discussion: Failure on creation 
of Snowflake dataset due to staging S3 download errors (works in SQL Lab, 
regression with new connector)

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Hi @oliverbell-klv! I'm [Dosu](https://go.dosu.dev/dosubot) and I’m helping the 
superset team.

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This is a known challenge with Superset's Snowflake integration: metadata 
reflection and dataset creation use SQLAlchemy's inspector methods (like 
`get_columns`), which can trigger result sets that Snowflake stages in S3. If 
your infrastructure intermittently fails to access the staging S3 bucket, these 
operations will fail, even if SQL Lab queries work (since they often return 
small results directly and avoid S3 staging) 
[source](https://github.com/apache/superset/issues/33937).

Superset does not have built-in retry logic or self-healing for 
Snowflake-specific network failures. Mitigation relies on tuning pool and 
timeout settings in the connector's configuration or your infrastructure setup. 
You can adjust these via the database's "Extra" field (`engine_params` and 
`connect_args`). Also, enabling the `CLIENT_SESSION_KEEP_ALIVE` parameter in 
Snowflake may improve connection stability 
[source](https://github.com/apache/superset/issues/33937).

If the issue persists, review your AWS/container networking and resource 
limits, and enable detailed logging (`LOG_LEVEL = logging.DEBUG`) to capture 
more diagnostic info. There are no Superset-specific config flags or version 
pins that directly address S3 staging download errors in metadata reflection. 
This is primarily an infrastructure/networking issue surfaced by how Superset 
interacts with the Snowflake connector for metadata queries.


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GitHub link: 
https://github.com/apache/superset/discussions/34778#discussioncomment-14169369

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