Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**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....
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Nagarro. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
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. (3) Broadcast Switch—upgrade sort-merge to broadcast when runtime stats show small side.
Example: AQE coalesces 200 shuffle partitions to 12 based on 64MB advisory size. Predicate pushdown: filter on region before join reduces shuffle by 80%.
Scalability Trade-offs: Pushdown depends on format (Parquet/ORC yes; CSV no). AQE adds planning overhead; benefit usually large.
Cost Implications: Pushdown = 50–90% I/O reduction. AQE = 20–40% runtime reduction. Enable both.
Pro-Move: 'EXPLAIN shows PushedFilters; we validate pushdown in CI.' Red Flag: Filtering after join—pushdown lost, full shuffle.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.