Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Consider a scenario where you have a large dataset in Apache Spark and you need to apply a pre-trained machine learning model to each row in parallel. You are advised to use Pandas UDFs for this task. Why is Apache Arrow considered crucial in this context?
Consider a scenario where you have a large dataset in Apache Spark and you need to apply a pre-trained machine learning model to each row in parallel. You are advised to use Pandas UDFs for this task. Why is Apache Arrow considered crucial in this context?
Simulated
Explanation:
Apache Arrow plays a key role in the efficiency of Pandas UDFs by providing a columnar memory format that allows for zero-copy reads, which significantly speeds up the data transfer between Spark and Pandas. This is crucial for large datasets where performance bottlenecks can occur during data conversion and processing.