SQL + Analytical Thinking with Dhruv Sharma

What we cover:

1. Business problem → translate to SQL
2. Metrics definition (DAU, retention, fraud rate, etc.)
3. Ambiguous datasets (like those real companies give)
4. Follow-up questions (this is where most candidates fail)

Example scenarios:

1. “Find most valuable users” → vague → you define logic
2. Fraud pattern detection
3. Funnel/retention analysis