Data engineering interview questions · medium
What work is done by the executor memory in Spark?
When and how do you use Broadcast Join?
Write a Python script to find the count of each word in a text file using Spark.
Write the PySpark code to find the second highest salary in each department.
Accumulators - use as shared variable for write-only operations
Broadcast Joins and Shuffle Merge Joins?
Broadcast join - how it optimizes joins
Can you explain the concept of mappers in Spark, and how are they used in data transformations?
Code a simple PySpark job to read a JSON file, filter records, and write output in Parquet format.
Compare Spark's lineage recovery with Hadoop's block replication mechanism.
Daily tasks of a Data Engineer?
Data-Related Issues Encountered - handling skewed data
Describe how you would use PySpark to aggregate and summarize large transaction datasets.
Discuss performance tuning concepts such as shuffle, skew, and caching.
Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.
How do you handle out-of-memory errors in Spark jobs?
How do you handle very large datasets in Spark to ensure scalability and efficiency?
Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.
Split a DataFrame such that even numbers appear in one column and odd numbers in another
Steps to mount storage in Databricks.
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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.