SQL
Query and model structured data — features, metrics, and app state all live here.
Maturity level: L2 — Can Build
Six perspectives on SQL
Roadmap
Learn alongside Python — most real AI systems touch a database.
Architecture
Persistence layer for features, users, eval results, and audit logs.
Company
Frequently listed for ML, data, and full-stack AI engineer roles.
Projects
Back every API and pipeline with a real database schema.
Interview
Query writing, optimization, and data modeling basics.
Career
Expected even for infra-heavy roles when debugging data issues.
What & Why
What: Structured query language for relational databases and warehouses.
Why: Training data, feature stores, user data, and business metrics are overwhelmingly SQL-backed.
Build this
Build a feature pipeline that reads from PostgreSQL and writes engineered features for ML.
Production reality
- ! Slow queries
- ! Missing indexes
- ! Lock contention
- ! Schema drift
- ! N+1 query patterns
Interview preparation
- Explain JOIN types with examples
- How would you optimize a slow analytics query?
- SQL vs NoSQL for ML features
Connected skills
Explore SQL in the interactive universe or train with live cohorts.