Spark/Big Data·9 min read·
Dunnhumby Data Engineer Interview Questions & Answers (2026)
Practice the 48 most asked data engineering questions at Dunnhumby. Covers Spark/Big Data, Python/Coding, General/Other and more.
Why Dunnhumby Tests These Questions
Dunnhumby is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 48 questions in our vault, the most common category is Spark/Big Data (24 questions).
Difficulty breakdown: 14 easy, 17 medium, 17 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Dunnhumby
- **Q1**: What is the difference between repartition and coalesce in Apache Spark?
- **Q2**: What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.
- **Q3**: Explain the difference between Spark's map() and flatMap() transformations.
- **Q4**: How does Spark's Catalyst Optimizer work? Explain its stages.
- **Q5**: What is the difference between Managed and External tables in Hive/Spark?
Category Breakdown for Dunnhumby Interviews
- **Spark/Big Data**: 24 questions
- **SQL**: 14 questions
- **General/Other**: 7 questions
- **Python/Coding**: 3 questions
How to Prepare
Focus on Spark/Big Data questions first, as they dominate Dunnhumby's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.
Practice These Questions
mediumWhat is the difference between repartition and coalesce in Apache Spark?→mediumWhat is the difference between narrow and wide transformations in Apache Spark? Explain with examples.→mediumExplain the difference between Spark's map() and flatMap() transformations.→hardHow does Spark's Catalyst Optimizer work? Explain its stages.→easyWhat is the difference between Managed and External tables in Hive/Spark?→
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