Data engineering interview questions
Logical Plan workflow when submitting Spark queries?
Memory Management in Spark - executor, storage, shuffle memory
Memory Tuning in Spark
Methods to avoid duplicates in PySpark/Scala?
Monitoring and Orchestrating Spark Jobs
Onboarding Delta Lake Catalog to Presto
Partition and Save as Parquet in PySpark
Perform EDA on a dataset and summarize your findings in a business context
Performance Tuning Techniques for Spark
Persistence Storage Levels: When to use MEMORY_ONLY, MEMORY_AND_DISK, etc.
Process a large log file (in GBs) to identify the top 10 users by event frequency. Optimize for memory efficiency and handle streaming input.
Production Experience - deploying and monitoring Spark jobs
Provide Pivot in PySpark example code and explain its purpose.
Provide example code for Drop Duplicates in PySpark.
Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.
Provide strategies for handling data deduplication and cleaning in Spark jobs.
Push and Pull in Tasks
PySpark Code for Broadcast Join and Conditional Aggregation by Location
PySpark Coding Challenge - dataset with 4-5 columns, solve data processing problem on paper
PySpark Coding Challenge: Transform input dataset with columns id, dob, name to add age, firstname, lastname
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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.