Why optimize: ADF costs scale with DIU (Data Integration Units) and runtime; unoptimized pipelines waste budget and miss SLAs. Architectural logic: (1) Mapping data flows run on Spark—use for complex transforms; they scale horizontally. (2) DIU slider—increase for heavy workloads; trade-off: higher DIU = higher cost per hour. (3) Partition sources/sinks—enables parallelism; small partition counts = underutilized clusters....
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 Deloitte. The answer also includes follow-up discussion points that interviewers commonly explore.
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