Data engineering interview questions
Explain Delta Time Travel and the purpose of the vacuum command.
Explain Hive, its purpose, and its default metadata storage.
Explain MapReduce Architecture.
Explain PySpark's Catalyst Optimizer.
Explain SCD1 and SCD2 in Databricks PySpark with examples.
Explain Spark Architecture – Driver, Executors, and Tasks.
Explain Spark transformations (lazy evaluation, wide vs narrow).
Explain Spark's execution process – Job/Stage/Task creation.
Explain Spark's narrow vs. wide transformations and when to use each
Explain a scenario-based question on Spark optimization and how you would troubleshoot performance issues.
Explain aggregation functions in PySpark with examples and use cases.
Explain caching techniques in Databricks.
Explain data encryption in Databricks, both at rest and in transit.
Explain database drivers/connectors and their use cases.
Explain how Glue's Spark-based architecture handles data parallelism.
Explain how HDFS (Hadoop Distributed File System) stores data across nodes.
Explain how I handle performance optimizations, scheduling tasks, and monitoring DAGs in Airflow.
Explain how Kafka handles real-time data streaming and guarantees message delivery.
Explain how Spark groups transformations into stages. What causes a stage boundary?
Explain how Spark handles data partitioning and the role of shuffles in performance tuning.
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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.