Data engineering interview questions
Databricks - platform, use cases
Databricks Cluster Management - standalone vs YARN mode
Databricks Job Cluster and SQL Endpoint - discuss Photon
Databricks notebooks vs. Fabric notebooks - differences
Databricks vs. PySpark?
Define Airflow and explain it as a workflow orchestration tool.
Define what a User-Defined Function (UDF) is and how to register it in PySpark.
Defining Tasks in DAG
Delta Lake: ACID compliance, time travel, streaming support
Delta vs Parquet - explain
Deploying DAGs
Describe a custom EMR cluster configuration for Spark-based ETL with minimal cost.
Describe building custom JARs for Spark jobs
Describe how to pass data between tasks in Airflow using XComs.
Describe how you would monitor ETL job performance and handle long-running tasks.
Describe how you would optimize a join between two large tables where one is significantly smaller, using broadcast joins in PySpark.
Describe how you would optimize slow-running Spark jobs in a distributed environment.
Describe how you would use PySpark to aggregate and summarize large transaction datasets.
Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.
Describe the projects emphasizing Spark, Hadoop, or Azure for large-scale data processing
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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.