SQL JOINs and pandas merges fail on color variants, promotional naming, translated descriptions, and spec formatting differences. AI embeddings and LLMs understand that 'Midnight' means black and 'Violet' means purple. Here's why traditional tools hit a ceiling and how hybrid pipelines break through it.
How to handle the N x M explosion in record matching — blocking strategies, pre-filter cascades, batch processing, and fault tolerance for large datasets.
Matching slows down fast at scale. Learn how blocking keys reduce comparisons by orders of magnitude, how to choose effective keys, and how multi-pass blocking recovers missed pairs.
From pandas merge to fuzzywuzzy to recordlinkage — a practical guide to CSV matching in Python, plus a framework for deciding when custom code stops making sense.
A comparison of rule-based and AI embedding approaches to record matching — strengths, weaknesses, costs, and why the best systems use both.
A practical decision guide for selecting matching algorithms based on data type, quality, and scale — from simple name matching to multi-field entity resolution.
Addresses are the hardest field to match. Abbreviations, unit numbers, directionals, and international formats make exact matching useless. Here's how to handle them.
A practical breakdown of six fuzzy matching algorithms — how they work, where they excel, and when to combine them for record matching across messy datasets.
Deterministic matching compares exact values. Probabilistic matching uses statistics, embeddings, and LLMs to find likely matches. Here's how each works, where each fails, and how combining them produces faster, cheaper, more accurate results.