Catalyst: Rule-based + cost-based optimizer for Spark SQL/DataFrame. Stages: 1) Analysis—resolve tables, columns, types via Catalog. 2) Logical optimization—predicate pushdown, projection pruning, constant folding, join reorder. 3) Physical planning—generate plans, cost model...
Red Flag: Listing stages without explaining predicate pushdown or cost implications. Pro-Move: 'We switched from Python UDF to Spark SQL; Catalyst pushed filters to Parquet and reduced scan by 70%'—shows practical impact.
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Dunnhumby, Fragma Data Systems, HashedIn. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization, spark) 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.
Catalyst: Rule-based + cost-based optimizer for Spark SQL/DataFrame. Stages: 1) Analysis—resolve tables, columns, types via Catalog. 2) Logical optimization—predicate pushdown, projection pruning, constant folding, join reorder. 3) Physical planning—generate plans, cost model picks best (e.g., broadcast vs sort-merge). 4) Code generation—Tungsten generates Java bytecode for tight loops. Why it matters: Decouples declarative query from execution; same SQL runs optimally on different cluster sizes. Predicate pushdown avoids reading irrelevant data (huge at scale). Cost: Bad plans (no pushdown, wrong join) can be 10–100x slower. Best practice: Use DataFrame/Spark SQL to get Catalyst; avoid UDFs that block pushdown.
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Practice the 48 most asked data engineering questions at Dunnhumby. Covers Spark/Big Data, Python/Coding, General/Other and more.
9 min read →Senior Spark interviews at Amazon, Databricks, and Meta focus on performance tuning, not API syntax. Master these 15 questions to prove you've run Spark at scale.
20 min read →According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.