Data engineering interview questions
How can Docker be used to scale streaming data applications?
How can Spark help in optimizing ingestion?
How can lifecycle management policies complement ADF for this task?
How did you handle data ingestion and processing for large datasets?
How do Delta Live Tables ensure data quality during transformations?
How do Delta Tables handle large-scale data updates efficiently?
How do Spark transformations differ from actions? Provide examples of each.
How do caching strategies impact memory management in Databricks?
How do you access Delta Logs?
How do you compare the time investment and value of a task?
How do you configure autoscaling for a Dataproc cluster?
How do you configure retention periods for Delta tables?
How do you connect to Blob Storage in Databricks?
How do you convert an array column to multiple columns in PySpark?
How do you decide the number of partitions for repartitioning data in Spark?
How do you ensure data quality and consistency across different stages of a data pipeline?
How do you ensure fault tolerance when processing large datasets in EMR?
How do you give permission to a notebook to other users in Databricks?
How do you handle bad data in Databricks?
How do you handle failures in Airflow tasks, and what retry strategies can you use?
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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.