Top 50 spark Interview Questions
The most frequently asked spark questions across all roles and companies, ranked by real interview frequency. Updated daily.
Optimize a Spark join where skewed keys create long-running tasks during a transaction to merchant metadata enrichment step.
Spokeo
Veeva Systems
Information Tech ConsultantsOptimize a PySpark join when one DataFrame is much smaller, focusing on join strategy, shuffle reduction, and practical Spark tuning.
Atos
Agile Defense
McKinsey &Explain when to use PySpark map versus flatMap, with examples that show one-to-one and one-to-many transformations.
McKinsey &
Swiss ReExplain how Spark partitions and distributes data across a cluster, and how transformations differ from actions.
Stackadapt
Quantexa
Apex SystemsDiagnose and remediate out-of-memory failures across Spark drivers, executors, and shuffle operations.
McKinsey &Explain how you have compared warehouse and lake architectures, modeled dimensions, and designed Spark pipelines in pre-sales or consulting work.
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Tests ownership and communication in a real big data environment, including how you handled technical tradeoffs and delivered measurable impact.
Experian
Berkeley Research Group
Amherst HoldingsExplain narrow vs wide transformations, how dependencies differ, and why wide transformations trigger network shuffle.
Ancestry
Jpmorgan Chase &
VisaDetect a heavily skewed join key and produce a deterministically salted join result for more balanced large-scale processing.
DatabricksBuild an efficient PostgreSQL report that aggregates partitioned project events and costs without join multiplication.
McKinsey &Optimize a PostgreSQL query with multiple joins and aggregations over large tables while preserving correct filtered totals.
Google