Normalizing 176K Recalls from 7 Government Feeds into One Queryable Corpus

A developer normalized 176,000 product recalls from seven government sources into a single corpus. The main challenge wasn't schema differences but reconciling conflicting definitions of what constitutes a recall. Identifiers like barcodes often hide in free-text fields, requiring regex and check-digit validation to avoid false negatives.