skill-based roadmap · Databases
SQL Roadmap
A structured path to mastering SQL for data querying, database design, and production-grade data engineering across relational database systems.
✓ Every resource link below is verified live.
1. Stage 1: Foundations of Relational Databases
What is a Relational Database?
Understand the core model before writing any SQL
Tables, Rows, Columns & Data Types
Data types define what you can store and query
Installing PostgreSQL & a SQL Client
Hands-on practice requires a local working environment
2. Stage 2: Core SQL Querying
SELECT, WHERE & Filtering
Retrieving and filtering data is the most common SQL task
ORDER BY, LIMIT & OFFSET
Sorting and paginating results is essential for real apps
Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
Aggregations power dashboards, reports and analytics
GROUP BY & HAVING
Group data to answer 'how many per category' questions
3. Stage 3: Multi-Table Queries & Relationships
Primary Keys & Foreign Keys
Keys enforce referential integrity across related tables
INNER, LEFT, RIGHT & FULL JOINs
JOINs are the most critical skill for real-world SQL work
Subqueries & Nested SELECTs
Subqueries solve complex filtering and lookup problems
UNION, INTERSECT & EXCEPT
Set operations combine result sets from multiple queries
4. Stage 4: Database Design & DDL
CREATE, ALTER & DROP Tables
DDL commands let you build and evolve your schema
Normalization: 1NF, 2NF, 3NF
Normalization eliminates redundancy and prevents data anomalies
Indexes & Query Performance
Indexes dramatically speed up queries on large datasets
Transactions, COMMIT & ROLLBACK
Transactions guarantee data consistency under concurrent access
5. Stage 5: Advanced SQL Techniques
Window Functions: RANK, ROW_NUMBER, LAG, LEAD
Window functions enable running totals and time-series analysis
Common Table Expressions (CTEs) & WITH
CTEs make complex multi-step queries readable and maintainable
JSON & Semi-Structured Data in SQL
Modern apps store JSON in relational databases frequently
Stored Procedures & Functions
Encapsulate reusable logic directly inside the database
6. Stage 6: SQL in Applications & Engineering
Using SQL with Python (psycopg2 & SQLAlchemy)
Python is the dominant language for data and backend work
SQL with Node.js (pg & Prisma)
Full-stack JS apps need typed, safe database access patterns
Database Migrations
Migrations version-control schema changes across environments
Query Optimization & EXPLAIN ANALYZE
Reading query plans is essential for production performance tuning
7. Stage 7: Job-Ready Practice & Real-World SQL
SQL for Data Analysis & Business Intelligence
Analysts and engineers are both expected to write complex SQL
Interview Practice: LeetCode SQL Problems
SQL interviews test window functions, joins and edge cases heavily
Cloud SQL: AWS RDS & Google Cloud SQL
Production databases run on managed cloud services, not localhost
Capstone: Build a Multi-Table Schema with Real Data
Building a project proves job-ready SQL design and query skills