A Data Pipeline Gets Expensive When Every Source Becomes Its Own Little System

A multi-source data project can look surprisingly simple at the beginning.

Connect one publisher. Add another. Write some source-specific logic. Keep going.

Then the system reaches ten or more sources, and the work starts changing. You are no longer maintaining a scraper. You are operating a collection of external dependencies that all happen to feed the same product.

That is where the cost starts growing.

Not because source number 13 is magically more expensive than source number 3, but because every source brings its own structure, failure patterns, access constraints, and maintenance work.