Data engineering interview questions · easy
Why does Hive use Derby by default, and what alternatives are used in production?
Worked with UDFs - share examples
Write PySpark code to filter and count records.
Write PySpark code to filter records based on specific conditions and add a calculated column.
Write a PySpark code snippet to filter rows with a specific condition.
Write the Spark command to rename an existing column in a DataFrame.
Writing Excel sheets to Delta tables in Databricks
You are given 10 worker machines with 100 GB RAM and 25 CPU cores. How would you determine the number of executors and the size of each executor?
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.
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.