Data engineering interview questions
Prioritize Spark optimizations by impact and effort. Discuss partitioning strategy, caching policy, join selection, shuffle reduction, and when each becomes a scalability or cost bottleneck.
Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)?
Walk through the three AQE features in Spark 3.x (coalesce, join switch, skew join)—how they operate at shuffle boundaries, which configs enable them, and what happens when AQE cannot help.
Explain wide vs. narrow transformations and how they drive shuffle cost, failure domains, and pipeline design. When would you intentionally add a wide transformation, and how do you minimize its impact?
Design a Delta table layout for mixed workload: point lookups by user_id, range scans by date, and full partition scans. Compare partitioning vs. Z-ordering—when to use each, and the rewrite cost trade-off.
Architecturally, how do Job–Stage–Task boundaries in Spark's execution model impact cluster sizing, shuffle cost, and when would you deliberately collapse or split stages?
Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.
Architect incremental load in ADF + Databricks with idempotency, late-arrival handling, and cost/scalability implications of watermark vs. change data capture.
Explain strategies for managing schema changes in PySpark over time.
Explain the Medallion Architecture (Bronze, Silver, Gold layers).
Explain the benefits of using DataFrames over RDDs.
Explain the concept of checkpointing in Spark and why it is important.
Explain the difference between batch and streaming data processing in Data Fusion.
Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
How do you drop columns with null values in PySpark?
How do you handle data skewness in Spark?
How do you optimize Spark jobs for performance?
How would you implement a sliding window aggregation in Spark Structured Streaming?
How would you read data from a web API using PySpark?
Implement a Spark job to find the top 10 most frequent words in a large text file.
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The most common Spark interview topics are: the difference between RDDs and DataFrames, transformations vs actions, data skew and how to handle it, partition strategies, shuffle optimization, and the catalyst optimizer. Delta Lake and Structured Streaming are increasingly tested.
If you're targeting mid-to-senior roles at companies processing large datasets, yes. Spark/Big Data questions appear in most data engineering interviews at scale-up and enterprise companies. Even companies using other tools test Spark as a proxy for distributed systems knowledge.
Use Databricks Community Edition (free), Google Colab with PySpark, or local Docker setups. Focus on understanding concepts like partitioning, broadcast joins, and lazy evaluation. Most interview questions test conceptual understanding, not syntax.
Data skew handling and performance tuning are the most challenging areas. Interviewers ask how to diagnose skew in a Spark job, strategies to fix it (salting, repartitioning, broadcast joins), and how to read Spark UI for performance bottlenecks.