Data engineering interview questions
What file format does Delta Lake use, and why is it beneficial?
What happens if the checkpoint location is accidentally deleted?
What happens if the vacuum command is not run periodically?
What happens when an executor fails during a task execution?
What insights can you gather from the DAG visualization in Spark UI?
What is Avro file format & what is its significance in delta tables?
What is Broadcast Join and Why is It Required?
What is Databricks Auto Loader, and how does it handle new files?
What is Predicate Pushdown and AQE with Example
What is Shuffle and How to Handle It in Spark
What is YARN, and how does it manage resources in a Hadoop ecosystem?
What is YARN?
What is a DAG in Apache Airflow, and how is it used for scheduling workflows?
What is a serializer in Spark?
What is data shuffling in Spark, and how do you minimize its impact on job performance?
What is offset management in Kafka?
What is one disadvantage of using Scala for data engineering tasks?
What is the advantage of caching in PySpark? When and why would you use it?
What is the command to import data from HDFS to Hive?
What is the difference between Lazy Evaluation and Eager Execution in PySpark?
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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.