When is Spark MLlib generally preferable to single-node sklearn on…

Question

When is Spark MLlib generally preferable to single-node sklearn on Databricks?

Accepted Answer

When data/feature engineering requires distributed scale beyond single-node memory — Spark ML when data won't fit or featurization needs the cluster; sklearn is fine for small data.

Answer

Always, even for 1MB datasets

Answer

Never; Spark ML is deprecated for all cases

Answer

Only for plotting

When is Spark MLlib generally preferable to single-node sklearn on Databricks?

Short Explanation and Infographic

Full Explanation