#partition

Explain wide vs. narrow transformations and how they drive shuffle cost, failure domains, and pipeline design. When would you intentionally add a wide transformation, and how do you minimize its impact?

Design a Delta table layout for mixed workload: point lookups by user_id, range scans by date, and full partition scans. Compare partitioning vs. Z-ordering—when to use each, and the rewrite cost trade-off.

Architecturally, how do Job–Stage–Task boundaries in Spark's execution model impact cluster sizing, shuffle cost, and when would you deliberately collapse or split stages?

Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.

Explain the Medallion Architecture (Bronze, Silver, Gold layers).

Explain the benefits of using DataFrames over RDDs.

Explain the difference between batch and streaming data processing in Data Fusion.

Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?