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Compare Spark RDDs, DataFrames, and SQL Performance Gains

ExperianData Scientist · Technical Screen
MediumData ScientistTechnical ScreenData Manipulation (SQL/Python)

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This question evaluates understanding of Spark abstractions (RDDs, DataFrames, Spark SQL), lazy evaluation, memory management, query optimization, and developer ergonomics within the Data Manipulation (SQL/Python) domain of distributed data processing.

SparkJobs

+---------+---------+---------------------+----------+ | job_id | user_id | submit_time | status | +---------+---------+---------------------+----------+ | 1001 | 17 | 2023-10-01 10:15:00 | running | | 1002 | 21 | 2023-10-01 10:20:00 | failed | | 1003 | 17 | 2023-10-01 10:25:00 | success | | 1004 | 42 | 2023-10-01 10:30:00 | running | +---------+---------+---------------------+----------+

Scenario

Discussion of distributed data processing tools used on big-data projects.

Question

Compare Spark RDDs, DataFrames, and Spark SQL. What performance gains come from Spark’s lazy evaluation model? When would you choose each abstraction?

Hints

Highlight memory management, query optimization, and developer ergonomics.