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In dynamic workflows, the data structure is not fixed before processing begins. Run-time schema construction means the system builds or adapts the data schema on the fly as features flow through the workflow. It can detect new attributes, adjust feature types, and extend the schema during execution, allowing the pipeline to handle varying or evolving input without manual reconfiguration.

This approach is ideal when input data can change—fields may appear or disappear, or different records may have different structures. By constructing the schema at run time, the workflow remains flexible and robust against such variations.

In contrast, a predefined schema locks in all fields before processing, requiring prior knowledge of every possible attribute. A fixed engine count relates to parallel processing resources, not the data schema. No schema implies a lack of defined structure to guide processing, which is impractical for most data workflows.