Data engineering interview questions
Sqoop Incremental Import?
Sqoop command for importing multiple tables
Steps to link a Databricks notebook to an ADF pipeline
Steps to mount storage in Databricks.
Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?
Suppose you need to import 5 tables from an external RDBMS (like MySQL) into Hadoop HDFS. Write the Sqoop command
Task Dependencies in DAG
Trade-offs between batch processing (Spark) vs. real-time streams (Kafka)
Transformation vs. Action in PySpark?
Usage of UDFs?
Walk through how you would debug the data ingestion process to identify slow stages.
Walkthrough Spark's architecture, focusing on driver, executors, and DAGs
What Hadoop command would you use to merge multiple files into one?
What are Hadoop commands for Get and Merge?
What are Spark Submit properties?
What are Spark optimizations, and can you explain them?
What are the advantages of using Dataproc over a traditional Hadoop setup?
What are the advantages of using Delta Lake over Parquet?
What are the challenges of implementing real-time analytics using Spark Streaming?
What are the differences between %pip and %conda commands in Databricks?
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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.