In real-world automation projects, exact string matching is rarely enough. Data extracted from invoices, emails, Excel files, PDFs, or web applications often contains: Typing mistakes Extra spaces ...
Pipeline de enriquecimento de dados municipais brasileiros. Recebe um CSV com nomes de municípios (com possíveis erros de digitação), cruza com a API oficial do IBGE usando fuzzy matching inteligente ...
Same 1 million customer record dataset. Three different matching approaches. Three very different outcomes. DIY (VLOOKUPs + Python fuzzy matching): 70% accuracy. 300,000 records wrong. Typical entity ...
Exact match after normalization (casefold, whitespace collapse) Alias table match (venue_aliases.alias_normalized == location_normalized) Slug match (if you ever store slugs in text) Known substring ...
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