Databricks Machine Learning Associate Question 357
Single answerYou are working with a large dataset in Databricks and need to convert a Pandas DataFrame to a Spark DataFrame for distributed processing. You want to ensure the conversion is optimized for performance. Which technology enables efficient data exchange between Pandas and Spark during this conversion?
- A
Apache Arrow
- B
Apache Kafka
- C
Delta Lake
- D
MLlib
Show answer and explanation
Correct answer: A
Explanation
Apache Arrow is the key technology that enables high-performance data interchange between Pandas and Spark. By leveraging its in-memory, columnar format, it minimizes serialization overhead and significantly speeds up conversions. This makes it an essential tool when working in Databricks environments that require switching between Pandas and Spark DataFrames.
- A. Correct.
Correct. Apache Arrow is an in-memory columnar data format that supports efficient data exchange between Pandas and Spark, enabling faster conversions.
- B. Incorrect.
Incorrect. Apache Kafka is used for real-time streaming and messaging, and is not related to Pandas and Spark conversions.
- C. Incorrect.
Incorrect. Delta Lake is a storage layer that provides ACID transactions and scalable data pipelines but is not involved in Pandas-to-Spark conversions.
- D. Incorrect.
Incorrect. MLlib is a machine learning library in Spark and is not relevant to data conversion between Pandas and Spark.