Given a scenario where you need to scale your data processing pipeline from a small dataset to a large distributed dataset, explain how Pandas API on Spark can be a solution without requiring significant refactoring. Provide a detailed example.

Simulated

Pandas API on Spark allows for direct scaling of Pandas code to Spark clusters without any changes.

21.9%

Pandas API on Spark requires rewriting the entire codebase to leverage Spark's distributed capabilities.

6.3%

Pandas API on Spark can be used to incrementally refactor Pandas code to Spark, starting with small datasets and gradually moving to larger ones.

67.2%

Pandas API on Spark is not suitable for scaling data pipelines and requires a complete rewrite using native Spark APIs.

4.7%

Databricks Certified Machine Learning - Associate