Spark/Big Data·13 min read·
Fragma Data Systems Data Engineer Interview Questions & Answers (2026)
Practice the 65 most asked data engineering questions at Fragma Data Systems. Covers Spark/Big Data, Behavioral, Python/Coding and more.
Why Fragma Data Systems Tests These Questions
Fragma Data Systems is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 65 questions in our vault, the most common category is Spark/Big Data (29 questions).
Difficulty breakdown: 19 easy, 15 medium, 31 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Fragma Data Systems
- **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**: What are your salary expectations for this role?
- **Q4**: Describe the difference between Spark RDDs, DataFrames, and Datasets.
- **Q5**: Explain the difference between Spark's map() and flatMap() transformations.
Category Breakdown for Fragma Data Systems Interviews
- **Spark/Big Data**: 29 questions
- **SQL**: 11 questions
- **System Design/Architecture**: 7 questions
- **Python/Coding**: 6 questions
- **General/Other**: 6 questions
- **Behavioral**: 5 questions
- **Cloud/Tools**: 1 questions
How to Prepare
Focus on Spark/Big Data questions first, as they dominate Fragma Data Systems'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.→easyWhat are your salary expectations for this role?→hardDescribe the difference between Spark RDDs, DataFrames, and Datasets.→mediumExplain the difference between Spark's map() and flatMap() transformations.→
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