How do you handle incremental loads in Azure Data Factory when different sources have different change patterns?

When building an Azure data platform, scheduled loads are fairly straightforward. Incremental loads can be more complicated when each source handles changes differently.

For example, some systems may have a reliable timestamp or change indicator, while others may require a different approach to identify new or modified records.

A few things I'm curious about:

1. Would you maintain separate incremental-load logic for each source, or use a metadata-driven approach?

2. How do you handle the initial full load without duplicating records when the pipeline switches to incremental processing?

3. What do you use as the watermark when the source doesn't provide a reliable last-modified field?

4. How do you handle late-arriving or updated records without repeatedly processing the entire dataset?

Those who are working with Azure Data Factory, what approach are you using for incremental ingestion across sources with different change-tracking capabilities?