**Predicate Pushdown**: Filter pushed to data source; only matching rows/row-groups read. Example: `df.filter("date = '2024-01-01'")` — Parquet reader skips row groups that don't contain that date. Partition pruning = filter on partition columns; entire directories skipped.
**AQE (Adaptive Query Execution)**: Runtime optimizations. (1) **Coalesce**—after shuffle, merge small partitions (e.g., 200 → 10). (2) **Skew Join**—split hot partitions....
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 Nagarro. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.