REPLACE, TRIM, and Case-Insensitive Matching

Concepts covered: sqlRegexMatch

The question that recurs in interviews involving customer data: 'find pairs of customer records that are likely the same person but have small differences in name or address.' This is fuzzy matching; the answer involves similarity scoring. The candidate who reaches for Levenshtein or Jaro-Winkler (depending on the data) and a threshold tuning conversation is the candidate who has built entity-resolution pipelines. The candidate who tries to match on LOWER equality is the one who hasn't yet seen the data. Three patterns this lesson covers First: fuzzy matching. Levenshtein distance counts character edits; Jaro-Winkler scores similarity giving extra weight to prefix matches; SOUNDEX matches phonetically. Each fits a different kind of similarity. Second: unicode normalization. The same charac

About This Interactive Section

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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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.