Can you explain the difference between generic and dynamic approaches when building a pipeline?

A generic pipeline is built with reusable, static components that are configured once and used across multiple data sources or use cases. It relies on predefined schemas, fixed transformations, and hard-coded logic, making it easy to maintain but less flexible to changes. A dynamic pipeline, on the other hand, adapts at runtime: it discovers schemas, selects transformations based on metadata, and can handle varying source structures without redeploying code. Dynamic pipelines often use metadata-driven orchestration, parameterized templates, or AI-driven decisions to adjust processing logic. The trade‑off is that generic pipelines are simpler and faster to implement, while dynamic pipelines offer greater scalability and adaptability at the cost of added complexity and potential performance overhead.