**Why String Ops in Data Eng:** Data cleaning (replace, normalize), validation (regex), feature extraction. Vectorized Pandas ops beat Python loops 10–100x.
**Operations:** str.replace, re.sub for patterns; ''.join(c for c in s if c not in 'aeiou') for vowel removal; len(s.split()) or Counter for word count; re.match/re.search for pattern check.
**Performance:** For 1M rows: df['col'].str.replace(r'\s+', ' ', regex=True) is vectorized. Precompile regex: pat = re.compile(r'...') for reuse....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Incedo. The answer also includes follow-up discussion points that interviewers commonly explore.
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