Databricks Machine Learning Associate Question 339
Single answerA data engineering team is working on an existing Pandas-based data pipeline that processes large datasets. They are facing memory and performance issues as the data size increases. The team wants to scale the pipeline without completely rewriting their code. Which solution would best address their requirements?
- A
Migrate the pipeline to use PySpark DataFrame APIs entirely.
- B
Switch to Dask for distributed data processing.
- C
Use Pandas API on Spark to scale their existing code with minimal refactoring.
- D
Split the dataset into smaller chunks and process them sequentially using Pandas.
Show answer and explanation
Correct answer: C
Explanation
Pandas API on Spark is a feature provided by Databricks that enables users to scale their existing Pandas-based data pipelines with minimal code changes by leveraging Apache Spark's distributed computing capabilities. It is the best solution for teams looking to address memory and performance issues while retaining their existing Pandas code structure.
- A. Incorrect.
While PySpark DataFrame APIs are powerful for distributed data processing, migrating to PySpark requires significant refactoring of the existing Pandas-based code, which contradicts the goal of minimal code changes.
- B. Incorrect.
Dask is another framework for scaling data processing, but it would also require rewriting the pipeline and learning a new framework, which adds complexity.
- C. Correct.
Pandas API on Spark is designed to scale existing Pandas-based workflows with minimal code changes, meeting the requirement of scaling the pipeline without much refactoring.
- D. Incorrect.
Processing the data sequentially in smaller chunks using Pandas would not address the team's scaling issue effectively, as it still operates within the limitations of a single machine.