Data engineering interview questions · hard
Schema evolution - techniques for handling schema changes in PySpark
Setting Dependencies for Tasks in DAG
Share your experience in working with big data technologies such as Hadoop, Spark, or AWS EMR. How have you leveraged these tools in your previous projects?
Solve 7-8 data processing questions using PySpark on F1 Racing Data
Solve the dataset transformation using PySpark.
Solve the grade assignment problem using a UDF in PySpark.
Spark Architecture - Components include Driver, Executors, Cluster Manager, and Tasks
Spark Configurations for Large-Scale Jobs
Spark Execution Flow - describe
Spark Executor Management: 10 workers, 100GB RAM, 25 cores - number of executors, size, OOM in Driver
Spark Optimization - broadcast joins, caching, coalescing, predicate pushdown, AQE
Spark Optimizations: skewed joins, broadcast joins, Catalyst Optimizer, repartition vs coalesce
Spark Session Command - how to create
Spark Streaming - streaming data handling and file mounting techniques
Spark Submit - command syntax
Spark Tungsten & Catalyst Optimizer
Steps to link a Databricks notebook to an ADF pipeline
Trade-offs between batch processing (Spark) vs. real-time streams (Kafka)
Usage of UDFs?
Walk through how you would debug the data ingestion process to identify slow stages.
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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.