Data engineering interview questions · hard
Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
How do you optimize Spark jobs for performance?
How would you implement a sliding window aggregation in Spark Structured Streaming?
Implement a Spark job to find the top 10 most frequent words in a large text file.
What are the key components of the Spark execution model (Job, Stage, Task)?
What is Spark's Catalyst Optimizer? Explain its stages.
What is the difference between Spark RDDs, DataFrames, and Datasets?
What is the small-file problem in Spark, and how do you solve it?
Why is SparkSession used in Spark 2.0 and later versions?
A data pipeline processes files for different clients stored in separate directories. Explain how you would use dynamic DAG creation to handle client-specific workflows in Airflow.
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
Basic Spark commands – Create RDD, Load data, Filter
Bloom Filters in Spark projects - explain use case
Cache vs. Persistent storage in Spark?
Calculating Databricks costs - explain DBU
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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.