SQLMastery Roadmap(2025 Edition)
Phase 1: Beginner Foundations (Core SQL)
Foundational Level
Learn syntax, querying basics, and understanding relational databases
Introduction to Databases
- 1. Database Fundamentals → What is a Database, DBMS vs RDBMS
- 2. Database Structure → Tables, rows, columns, schema
- 3. Key Concepts → Primary Key, Foreign Key, Constraints
Basic SQL Queries
- 1. Core Syntax → SELECT, FROM, WHERE clauses
- 2. Filtering Data → AND, OR, BETWEEN, IN, LIKE, IS NULL operators
- 3. Sorting Results → ORDER BY for ascending/descending sort
- 4. Limiting Results → LIMIT, TOP for result pagination
Aggregations & Grouping
- 1. Aggregate Functions → COUNT, SUM, AVG, MIN, MAX
- 2. Grouping Data → GROUP BY for categorical aggregation
- 3. Filter Groups → HAVING clause for filtered aggregations
Joins (Very Important)
- 1. INNER JOIN → Matching records from both tables
- 2. Outer Joins → LEFT JOIN, RIGHT JOIN for inclusive results
- 3. FULL OUTER JOIN → All records from both tables
- 4. CROSS JOIN → Cartesian product of tables
- 5. Self Joins → Joining a table to itself
Set Operations
- 1. UNION → Combine results removing duplicates
- 2. UNION ALL → Combine results keeping duplicates
- 3. INTERSECT → Common records between queries
- 4. EXCEPT → Records in first query but not second
Phase 1
Phase 2
Phase 2: Intermediate SQL (Data Handling)
Intermediate Level
Advanced querying, data manipulation, schema management
Data Manipulation
- 1. Insert Operations → INSERT for adding new records
- 2. Update Operations → UPDATE for modifying existing data
- 3. Delete Operations → DELETE for removing records
- 4. Transactions → BEGIN, COMMIT, ROLLBACK for data consistency
- 5. Bulk Operations → Bulk Insert for multiple records
Constraints & Data Integrity
- 1. Column Constraints → NOT NULL, DEFAULT, UNIQUE
- 2. Referential Integrity → Primary & Foreign Keys
- 3. CHECK Constraints → Custom validation rules
Views
- 1. Simple Views → Virtual tables from queries
- 2. Indexed Views → Views with physical storage
- 3. Materialized Views → Pre-computed results for performance
Subqueries & CTEs
- 1. Scalar Subqueries → Single value subqueries
- 2. Correlated Subqueries → Row-by-row dependent queries
- 3. Common Table Expressions → WITH clause for readable queries
- 4. Recursive CTEs → Self-referencing hierarchical queries
Window Functions (Must Learn)
- 1. Ranking Functions → ROW_NUMBER(), RANK(), DENSE_RANK()
- 2. Running Calculations → SUM() OVER for cumulative totals
- 3. Lead & Lag → LAG(), LEAD() for time-based comparisons
- 4. Partitioning → OVER (PARTITION BY ...) for grouped calculations
Phase 2
Phase 3
Phase 3: Database Design & Schema Mastery
Advanced Level
Industry-ready schema design and normalization
Normalization
- 1. Normal Forms → 1NF, 2NF, 3NF, BCNF, 4NF, 5NF progression
- 2. Design Goals → Why normalization matters, avoiding redundancy
- 3. Data Integrity → Ensuring consistency across tables
Denormalization
- 1. Performance Trade-offs → When to use denormalization
- 2. Design Decisions → Balancing normalization vs performance
- 3. Use Cases → Read-heavy systems, reporting databases
ER Diagrams
- 1. Entity Modeling → Entities, Attributes, Relationships
- 2. Cardinality → One-to-One, One-to-Many relationships
- 3. Complex Relations → Many-to-Many with junction tables
Indexes
- 1. Index Types → Clustered vs Non-Clustered indexes
- 2. Composite Indexes → Multi-column index strategies
- 3. Covering Indexes → Indexes that contain all query columns
- 4. Best Practices → When and how to index effectively
Phase 3
Phase 4
Phase 4: Advanced SQL & Optimization
Expert Level
Production-level query tuning and database performance
Query Optimization
- 1. Execution Plans → Understanding query execution paths
- 2. Cost Analysis → Cost-based optimization strategies
- 3. Query Refactoring → Reducing nested subqueries
- 4. Index Usage → Using indexes properly for performance
Transactions & Concurrency
- 1. ACID Properties → Atomicity, Consistency, Isolation, Durability
- 2. Isolation Levels → READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE
- 3. Deadlock Management → Deadlocks and how to avoid them
- 4. Lock Types → Shared locks, exclusive locks, row-level locking
Stored Procedures & Functions
- 1. User-defined Functions → Creating custom UDFs
- 2. Stored Procedures → Parameterized stored procedures
- 3. Error Handling → Exception handling in stored procedures
- 4. Performance → When to use procedures vs inline queries
Triggers
- 1. Trigger Types → AFTER INSERT, AFTER UPDATE, AFTER DELETE
- 2. Use Cases → Audit trails, data validation, cascading updates
- 3. Risks → Performance impact, debugging challenges
Partitioning & Sharding
- 1. Partitioning Types → Horizontal vs Vertical partitioning
- 2. Table Partitioning → Range, list, hash partitioning strategies
- 3. Sharding Concepts → Database scaling across servers
- 4. Big Data → Handling large-scale datasets
Phase 4
Phase 5
Phase 5: Industry Ready Skills
Production Level
Work in production systems, integrate with applications
SQL in Different Databases
- 1. Database Variants → MySQL, PostgreSQL, SQL Server, Oracle differences
- 2. PostgreSQL Advanced → JSONB, CTE recursion, full-text search
- 3. Database Selection → Choosing the right database for use case
NoSQL & SQL Hybrid Knowledge
- 1. NoSQL Comparison → MongoDB vs SQL databases
- 2. JSON Support → PostgreSQL JSON queries and operations
- 3. Hybrid Approach → When to use SQL vs NoSQL
- 4. Polyglot Persistence → Using multiple database types
ETL & Data Engineering
- 1. ETL Concepts → Extract, Transform, Load with SQL
- 2. Data Pipelines → Using SQL in data pipelines
- 3. Python Integration → Pandas + SQLAlchemy for data workflows
- 4. Data Quality → Validation and cleansing in SQL
Analytics & BI Tools
- 1. BI Integration → SQL in Power BI, Tableau dashboards
- 2. Dashboard Optimization → Writing optimized queries for reporting
- 3. Time-series Analytics → Temporal queries and trends
- 4. KPI Calculations → Business metrics and aggregations
Big Data SQL
- 1. Distributed SQL → Spark SQL, HiveQL basics
- 2. Data Lakes → Working with partitioned tables
- 3. Columnar Formats → Parquet, ORC file formats
- 4. Query Federation → Querying across multiple sources
Phase 5
Phase 6
Phase 6: Expert / Industry Mastery
Mastery Level
Master complex real-world use cases
Advanced Performance Engineering
- 1. Index Tuning → Index tuning advisor, optimization tools
- 2. Caching Strategies → Database caching, query result caching
- 3. Query Optimization → Optimizing high-traffic queries
- 4. Monitoring → Performance monitoring and alerting
Security & Compliance
- 1. Access Control → Role-based access control (RBAC)
- 2. Row-level Security → Fine-grained data access (RLS)
- 3. Data Encryption → Encryption at rest and in transit
- 4. Compliance → GDPR, HIPAA, data privacy regulations
DevOps for SQL
- 1. Schema Migration → Database migrations with Liquibase, Flyway
- 2. CI/CD Pipelines → Automated SQL schema updates
- 3. Version Control → Git strategies for database changes
- 4. Backup & Recovery → Backup strategies, disaster recovery
Case Studies (Real World)
- 1. E-commerce Scale → Handling millions of records in transactions
- 2. Financial Systems → Optimizing financial transaction queries
- 3. Analytics Architecture → Scalable architecture for analytics
- 4. High Availability → Replication, failover strategies
Capstone Projects
- 1. Data Warehouse → Design & optimize Star Schema, Snowflake Schema
- 2. Reporting Dashboard → Build dashboard using SQL + BI tool
- 3. Large Dataset → Optimize queries for 10M+ row dataset
- 4. Production System → End-to-end database system design
🏆 Final Tips to Become Industry-Ready SQL Engineer
Congratulations! You've completed the SQL Engineer Mastery Roadmap and are ready to design scalable, robust systems.