LIKE and Wildcard Pattern Matching

Concepts covered: sqlSubstring

Fuzzy matching scores similarity between two strings. Identical strings have a perfect score; strings with small differences have high scores; very different strings have low scores. The choice of function depends on the kind of similarity that matters: edit distance for typos, phonetic similarity for misspellings, prefix-weighted for names. Levenshtein distance Levenshtein counts character-level edits (insertions, deletions, substitutions). 'Mariam' to 'Miriam' is 2 edits. The distance is the raw score; for similarity, divide by the longer string's length to get a normalized score (0 = identical, 1 = completely different). Use Levenshtein when typo similarity is what you want: catching 'Smith' vs 'Smtih' as similar. Jaro-Winkler similarity Jaro-Winkler weights early-character matches more

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This section is part of the String Manipulation: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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