How can Azure Data Factory be used to implement incremental data movement between enterprise data sources?

Enterprise data pipelines often need to move only new or modified records instead of repeatedly processing entire datasets. Azure Data Factory supports incremental data movement using approaches such as watermark columns, change tracking, timestamps, and metadata-driven pipelines. The challenge is designing a process that remains accurate when records are updated, deleted, or arrive late. How can developers and data engineers design incremental pipelines that are efficient, restartable, scalable, and reliable for real-world enterprise workloads?