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Home/Questions/Spark/Big Data/Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.

Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.

Spark/Big Datamedium0.8 min readPremium
Frequency
Low
Asked at 2 companies
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
DatameticaS&P Global
Interview Pro Tip

Red Flag: Converting all SQL to DataFrame without reason—SQL is often fine for ad-hoc and stable queries. Pro-Move: Use SQL for BI/adhoc; DataFrame API for pipelines with tests and partitioning control.

Key Concepts Tested
partitionpythonsparksqlwindow
Expert AnswerPremium
150 wordsIncludes code examplesInterview-ready
Two approaches: spark.sql() for direct translation and DataFrame API for programmatic logic. SQL approach: createOrReplaceTempView, run ANSI-like SQL—fast parity, but string-based, harder to unit test, and execution plan less explicit. DataFrame API: composable, testable (pass mock DataFrames), explicit transformations....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Datametica, S&P Global. The answer also includes follow-up discussion points that interviewers commonly explore.

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