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Not a true fork: It’s not maintained independently from Apache Spark but is a close superset with proprietary improvements. Codebases largely track, but Databricks sometimes “forks” select modules or patches, then later merges when upstream releases catch up.
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Result: Features may appear early, or behave differently, on Databricks than on the open source PySpark.
