I’ve built and maintained a pipeline that collects public business listings at scale.
Scraping itself was straightforward compared to everything that followed.
Once the data volume grows, most of the work shifts to:
-handling inconsistent categories
-deduplication across sources
-outdated or closed businesses
-missing or misleading fields
-deciding what “usable” actually means
Many datasets look large but fall apart when you try to use them for anything practical.
This post breaks down where most scraping projects fail once they move beyond small experiments, and what actually takes time when you want clean output.
Would be interested to hear how others here approached data validation and cleanup at scale.
RangeLead•2h ago
Scraping itself was straightforward compared to everything that followed.
Once the data volume grows, most of the work shifts to:
-handling inconsistent categories -deduplication across sources -outdated or closed businesses -missing or misleading fields -deciding what “usable” actually means
Many datasets look large but fall apart when you try to use them for anything practical.
This post breaks down where most scraping projects fail once they move beyond small experiments, and what actually takes time when you want clean output.
Would be interested to hear how others here approached data validation and cleanup at scale.