Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Architectural logic: Pipelines = orchestration (activities, flow); Data Flows = Spark-based transformation activity. Pipelines orchestrate; Data Flows transform. A pipeline can contain Copy, Lookup, Data Flow, etc. When to use: Copy for simple move; Data Flow for filter, join,...
Architectural logic: Pipelines = orchestration (activities, flow); Data Flows = Spark-based transformation activity. Pipelines orchestrate; Data Flows transform. A pipeline can contain Copy, Lookup, Data Flow, etc. When to use: Copy for simple move; Data Flow for filter, join, aggregate. Scalability: Data Flow runs on Spark; tune partitions. Cost: Data Flow consumes more than Copy. Best practice: Pipelines for orchestration; Data Flow for complex logic; Databricks for very heavy transforms.
Red Flag: Using Data Flow for simple copy. Pro-Move: 'We use Copy for raw→staging; Data Flow for joins and cleansing—Copy is 10x cheaper for bulk.'
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