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Explain the role of DAGs (Directed Acyclic Graphs) in Spark.

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Freecharge
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
94 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. DAGs in Spark: directed acyclic graph of RDD/DataFrame lineage. Role: (1) Optimization—pipeline narrow transformations, prune; (2) Fault tolerance—recompute lost partitions; (3) Scheduling—define stages and tasks. No cycles; each node is deterministic. Example: read, filter, map, groupBy....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Freecharge. The answer also includes follow-up discussion points that interviewers commonly explore.

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