Data engineering interview questions
How to Upsert Your Data Daily Using Spark
How to check Spark version?
How to fill null values in PySpark?
How to handle null value in a single column in PySpark?
How to optimize mappers using properties in MapReduce?
How to remove duplicates in PySpark?
How would you debug a failing Spark job running on Dataproc?
How would you debug a slow-running PySpark job? What factors would you investigate?
How would you design a Kafka-based pipeline for processing streaming data in real-time?
How would you design a scalable and fault-tolerant data processing pipeline for handling large volumes of streaming data?
How would you enforce encryption at rest for all objects in a bucket?
How would you ensure exactly-once processing for Kafka consumers in your Spark job?
How would you ensure the pipeline is scalable for larger datasets?
How would you fetch data from an API and load it into a DataFrame?
How would you handle a large-scale data shuffle in a Dataflow pipeline?
How would you handle memory management in Spark?
How would you handle unstructured data in Hive?
How would you identify and resolve a shuffle spill in Spark UI?
How would you manage the streaming data schema and handle schema evolution in Delta Lake?
How would you manage transitions to Glacier Instant Retrieval and Deep Archive?
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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.