Databricks Machine Learning Associate Question 340
Single answerYou are working with a large dataset that exceeds the memory capacity of your local machine. You want to scale your existing Pandas-based data transformation pipeline to process this dataset without significantly refactoring your code. Which of the following approaches would be most appropriate in Databricks?
- A
Use Pandas API on Spark to scale your existing code with minimal changes.
- B
Rewrite the entire pipeline using Apache Spark DataFrames.
- C
Use Dask to distribute the workload across a cluster.
- D
Manually partition the dataset and process it in smaller chunks using Pandas.
Show answer and explanation
Correct answer: A
Explanation
The Pandas API on Spark is specifically designed to help users scale their existing Pandas workflows with minimal code changes by leveraging the distributed computing power of Apache Spark. This makes it an ideal solution for scaling data pipelines in Databricks without significant refactoring.
- A. Correct.
Correct: Pandas API on Spark provides a scalable solution that allows you to leverage Spark's distributed computation while keeping your Pandas-like syntax, requiring minimal code refactoring.
- B. Incorrect.
Incorrect: While Apache Spark DataFrames are powerful for distributed computation, rewriting the pipeline would require significant effort and doesn't align with the requirement to minimize refactoring.
- C. Incorrect.
Incorrect: Dask is another option for distributed computation, but it is not natively integrated with Databricks and would require additional setup and code changes.
- D. Incorrect.
Incorrect: Manually partitioning the dataset is inefficient and error-prone for large-scale processing, and it does not scale effectively compared to distributed computing frameworks like Spark.