Data engineering interview questions
Accumulators - use as shared variable for write-only operations
Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.
After cleaning, how would you store the transformed data into Delta Lake?
Alternatives to the Medallion Architecture
Apache Spark Architecture - RDD, DAG, cluster manager, driver node, worker node
Apache Spark Fundamentals - discuss
Apache Spark Fundamentals - failures, job optimization, resource utilization
Approaches to handling multiple tasks within a sprint?
Basic Spark commands – Create RDD, Load data, Filter
Bloom Filters in Spark projects - explain use case
Broadcast Joins and Shuffle Merge Joins?
Broadcast join - how it optimizes joins
Cache vs. Persistent storage in Spark?
Cache() vs Persist(): Explain the difference and use cases for caching and persisting data in Spark with memory levels.
Calculating Databricks costs - explain DBU
Can Presto work with Near Real-Time Data (Streaming Data Source)?
Can you explain dynamic resource allocation in Spark? How does it help optimize job performance?
Can you explain how streams and tasks handle data freshness in near real-time?
Can you explain the concept of incremental loading in Sqoop and how to use it for job processing?
Can you explain the concept of mappers in Spark, and how are they used in data transformations?
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.