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Database Engineer Roadmap(2025 Edition)

Phase 0: Foundations

Beginner Level (0-3 months)

Core database concepts and SQL fundamentals

Computer Science & Database Basics

  1. 1. Database fundamentals → What is a database, why use one
  2. 2. Database types → Relational vs Non-Relational systems
  3. 3. Core concepts → Tables, rows, columns, relationships
  4. 4. Keys → Primary keys, foreign keys, unique constraints
  5. 5. Schema design → Normalization principles, data organization
  6. 6. ACID properties → Atomicity, Consistency, Isolation, Durability
  7. 7. Data lifecycle → Create, Read, Update, Delete operations

SQL Mastery

  1. 1. Basic queries → SELECT, WHERE, ORDER BY, LIMIT
  2. 2. Aggregation → COUNT, SUM, AVG, MIN, MAX functions
  3. 3. Grouping → GROUP BY, HAVING clauses
  4. 4. Joins → INNER, LEFT, RIGHT, FULL OUTER joins
  5. 5. Subqueries → Nested queries, correlated subqueries
  6. 6. Constraints → UNIQUE, CHECK, NOT NULL, DEFAULT
  7. 7. Views & Aliases → Virtual tables, query simplification
  8. 8. Practice → SQLZoo, LeetCode SQL, Mode SQL Tutorial

Database Installation & Practice

  1. 1. PostgreSQL → Installation, configuration, best all-rounder
  2. 2. MySQL/MariaDB → Common web stack database
  3. 3. GUI tools → DBeaver, pgAdmin, TablePlus for management
  4. 4. Practice project → Student Management System with CRUD
  5. 5. Practice project → Movie Rating database with relationships
Phase 0
Phase 1
Phase 1: Data Modeling & Design

Intermediate Level (3-6 months)

Schema design, optimization, and database internals

Data Modeling & Normalization

  1. 1. ER diagrams → Entity-Relationship modeling
  2. 2. Normal forms → 1NF, 2NF, 3NF, BCNF principles
  3. 3. Relationships → One-to-one, one-to-many, many-to-many
  4. 4. Denormalization → Strategic denormalization for performance
  5. 5. Design tools → Draw.io, dbdiagram.io, Lucidchart
  6. 6. Schema evolution → Managing database changes over time

Transactions & Concurrency

  1. 1. ACID compliance → Transaction guarantees and consistency
  2. 2. Isolation levels → Read uncommitted to serializable
  3. 3. Locks & deadlocks → Understanding and preventing deadlocks
  4. 4. Transaction management → BEGIN, COMMIT, ROLLBACK
  5. 5. Concurrency control → Multi-version concurrency control (MVCC)
  6. 6. Performance trade-offs → Consistency vs availability

Indexing & Query Optimization

  1. 1. Index types → B-Tree, Hash, Bitmap, GiST indexes
  2. 2. Index strategies → When to index, composite indexes
  3. 3. Query execution plans → EXPLAIN and EXPLAIN ANALYZE
  4. 4. Query optimization → Identifying bottlenecks and improvements
  5. 5. Statistics → Table statistics and query planning
  6. 6. Performance tuning → Index maintenance, query rewriting

Advanced SQL Features

  1. 1. Stored procedures → Encapsulating business logic
  2. 2. Functions → User-defined functions in SQL
  3. 3. Triggers → Event-driven database actions
  4. 4. Views → Materialized and regular views
  5. 5. CTEs → Common Table Expressions for complex queries
  6. 6. Window functions → Analytical queries and ranking

Hands-on Projects

  1. 1. Inventory system → Stock management with triggers
  2. 2. E-commerce database → Orders, products, customers schema
  3. 3. Analytics dashboard → Optimized reporting queries
  4. 4. Performance benchmarking → Before/after optimization metrics
Phase 1
Phase 2
Phase 2: NoSQL & Modern Databases

Advanced Level (6-9 months)

Non-relational databases and specialized systems

NoSQL Database Types

  1. 1. Document databases → MongoDB, CouchDB for dynamic schemas
  2. 2. Key-value stores → Redis, DynamoDB for caching and sessions
  3. 3. Column-family → Cassandra, HBase for high-volume distributed systems
  4. 4. Graph databases → Neo4j, ArangoDB for relationship-heavy data
  5. 5. Time-series → InfluxDB, TimescaleDB for temporal data

NoSQL Theory & Patterns

  1. 1. CAP theorem → Consistency, Availability, Partition tolerance
  2. 2. BASE model → Basically Available, Soft state, Eventual consistency
  3. 3. Data replication → Master-slave, peer-to-peer replication
  4. 4. Sharding strategies → Horizontal partitioning, consistent hashing
  5. 5. Denormalization patterns → Data duplication for performance
  6. 6. Query patterns → Designing schemas for access patterns

Performance & Scalability

  1. 1. Query profiling → Identifying slow queries and bottlenecks
  2. 2. Indexing strategies → Database-specific index types
  3. 3. Caching layers → Redis, Memcached integration
  4. 4. Connection pooling → PgBouncer, connection management
  5. 5. Partitioning → Table partitioning for large datasets
  6. 6. Clustering → Multi-node database clusters

High Availability & Recovery

  1. 1. Backup strategies → Full, incremental, differential backups
  2. 2. Point-in-time recovery → PITR for PostgreSQL and MySQL
  3. 3. Replication → Master-slave, master-master configurations
  4. 4. Hot standby → Read replicas and failover systems
  5. 5. WAL → Write-Ahead Logging for durability
  6. 6. Disaster recovery → RTO and RPO planning

NoSQL Projects

  1. 1. User analytics → MongoDB tracking system with aggregations
  2. 2. Real-time leaderboard → Redis sorted sets implementation
  3. 3. Social network → Neo4j graph database with relationship queries
  4. 4. IoT data pipeline → Cassandra time-series data storage
  5. 5. Caching layer → Redis integration with relational database
Phase 2
Phase 3
Phase 3: Cloud & Distributed Systems

Industry Ready (9-12 months)

Cloud databases and distributed architectures

Amazon Web Services

  1. 1. Amazon RDS → Managed PostgreSQL, MySQL, MariaDB, Oracle
  2. 2. Amazon Aurora → High-performance MySQL and PostgreSQL compatible
  3. 3. DynamoDB → Serverless NoSQL key-value and document database
  4. 4. Amazon Redshift → Data warehouse for analytics
  5. 5. ElastiCache → Managed Redis and Memcached
  6. 6. Database Migration Service → Schema and data migration

Google Cloud Platform

  1. 1. Cloud SQL → Managed PostgreSQL, MySQL, SQL Server
  2. 2. Cloud Spanner → Globally distributed relational database
  3. 3. Firestore → NoSQL document database
  4. 4. Bigtable → Wide-column NoSQL for analytics
  5. 5. BigQuery → Serverless data warehouse
  6. 6. Memorystore → Managed Redis and Memcached

Microsoft Azure

  1. 1. Azure SQL Database → Managed SQL Server instances
  2. 2. Cosmos DB → Multi-model globally distributed database
  3. 3. Azure Database for PostgreSQL → Managed PostgreSQL
  4. 4. Azure Database for MySQL → Managed MySQL
  5. 5. Azure Cache for Redis → In-memory caching
  6. 6. Azure Synapse Analytics → Enterprise data warehousing

Cloud Infrastructure

  1. 1. Infrastructure as Code → Terraform, CloudFormation for provisioning
  2. 2. Backup automation → Scheduled backups, retention policies
  3. 3. Monitoring → CloudWatch, Prometheus, Grafana dashboards
  4. 4. Cost optimization → Right-sizing, reserved instances
  5. 5. Security → VPC, encryption, IAM roles and policies
  6. 6. Multi-region → Geographic distribution and latency optimization

Cloud Projects

  1. 1. Migrated application → On-premise to cloud database migration
  2. 2. Multi-region deployment → Global application with replication
  3. 3. Cost optimization → Demonstrate 30%+ cost reduction
  4. 4. Monitoring dashboard → Complete observability solution
  5. 5. IaC deployment → Terraform-managed database infrastructure
Phase 3
Phase 4
Phase 4: Distributed & Big Data

Expert Level (12-18 months)

Large-scale distributed systems and data platforms

Distributed Database Architecture

  1. 1. Horizontal scaling → Sharding, partitioning strategies
  2. 2. Consistent hashing → Data distribution across nodes
  3. 3. Distributed transactions → Two-phase commit protocol
  4. 4. Consensus algorithms → Raft, Paxos for distributed agreement
  5. 5. Vector clocks → Conflict resolution in distributed systems
  6. 6. Quorum reads/writes → Tunable consistency levels

NewSQL Databases

  1. 1. CockroachDB → Distributed SQL with ACID guarantees
  2. 2. TiDB → MySQL-compatible distributed database
  3. 3. YugabyteDB → PostgreSQL-compatible distributed SQL
  4. 4. VoltDB → In-memory transactional database
  5. 5. NuoDB → Elastic SQL database for cloud
  6. 6. Spanner-inspired → Global consistency at scale

Big Data Ecosystem

  1. 1. Apache Hadoop → Distributed storage and processing
  2. 2. Apache Spark → Fast distributed data processing
  3. 3. Apache Hive → SQL on Hadoop data warehouse
  4. 4. Apache HBase → Column-family NoSQL on Hadoop
  5. 5. Apache Kafka → Distributed event streaming platform
  6. 6. Presto/Trino → Distributed SQL query engine

Data Lakes & Warehouses

  1. 1. Data lake architecture → Raw data storage and processing
  2. 2. ETL pipelines → Extract, Transform, Load workflows
  3. 3. Delta Lake → ACID transactions for data lakes
  4. 4. Apache Iceberg → Table format for large datasets
  5. 5. Snowflake → Cloud data warehouse platform
  6. 6. Databricks → Unified analytics platform

Advanced Projects

  1. 1. Distributed database → Multi-region CockroachDB deployment
  2. 2. Big data pipeline → Kafka to Spark to data warehouse
  3. 3. Sharded application → Custom sharding implementation
  4. 4. Real-time analytics → Streaming data processing pipeline
  5. 5. Data platform → End-to-end data architecture design
Phase 4
Phase 5
Phase 5: Database DevOps & Mastery

Advanced Expert (18+ months)

Database operations, security, and system design

Database DevOps

  1. 1. Schema migrations → Flyway, Liquibase for version control
  2. 2. CI/CD pipelines → Automated testing and deployment
  3. 3. Version control → Git for SQL scripts and schemas
  4. 4. Blue-green deployments → Zero-downtime migrations
  5. 5. Rollback strategies → Safe schema change management
  6. 6. Automated testing → Unit and integration tests for databases

Database Security

  1. 1. Encryption → At-rest and in-transit data protection
  2. 2. Access control → Role-based access control (RBAC)
  3. 3. Authentication → SSO, MFA, service accounts
  4. 4. Audit logging → Compliance tracking and monitoring
  5. 5. Data masking → PII protection and anonymization
  6. 6. SQL injection → Prevention and security best practices

Monitoring & Observability

  1. 1. Metrics collection → Database performance metrics
  2. 2. Query monitoring → Slow query logs and analysis
  3. 3. Alerting → PagerDuty, Slack integration for incidents
  4. 4. Log aggregation → Centralized logging with ELK stack
  5. 5. Tracing → Distributed tracing for database calls
  6. 6. SLA management → Service level objectives and monitoring

Compliance & Governance

  1. 1. GDPR compliance → Data protection and privacy regulations
  2. 2. HIPAA → Healthcare data security requirements
  3. 3. SOX compliance → Financial data audit requirements
  4. 4. Data retention → Policies and automated enforcement
  5. 5. Data lineage → Tracking data flow and transformations
  6. 6. Privacy frameworks → CCPA, regional data regulations

System Design & Architecture

  1. 1. Database selection → Choosing the right database for use case
  2. 2. Capacity planning → Growth forecasting and scaling
  3. 3. Performance tuning → Advanced optimization techniques
  4. 4. Multi-tenancy → Shared vs isolated database architectures
  5. 5. Microservices data → Database per service pattern
  6. 6. Event sourcing → CQRS and event-driven architectures

Master Projects

  1. 1. Complete platform → Multi-database architecture with monitoring
  2. 2. Zero-downtime migration → Large-scale database migration project
  3. 3. Performance optimization → 10x improvement case study
  4. 4. Compliance framework → End-to-end governance implementation
  5. 5. Multi-tenant SaaS → Scalable database architecture design
Phase 5
Phase 6
Phase 6: Real-World Projects & Interview Prep

Industry-Level Experience

Production-ready projects and interview preparation

Industry-Level Projects

  1. 1. E-commerce database → Users, orders, inventory, payments schema
  2. 2. Analytics data warehouse → ETL pipelines with BI dashboard integration
  3. 3. Real-time event stream → Kafka + Cassandra for streaming data
  4. 4. Multi-region replication → Global database deployment with failover
  5. 5. Microservices architecture → Database per service pattern implementation

Interview Preparation

  1. 1. SQL challenges → LeetCode, HackerRank, StrataScratch problems
  2. 2. Database design → System design case studies and architecture
  3. 3. Performance tuning → Query optimization and scaling scenarios
  4. 4. Mock interviews → Database modeling and technical discussions
  5. 5. Behavioral questions → Project experience and problem-solving

Essential Tools Mastery

  1. 1. SQL engines → PostgreSQL, MySQL, MariaDB, SQLite proficiency
  2. 2. NoSQL platforms → MongoDB, Redis, Cassandra, DynamoDB
  3. 3. GUI clients → DBeaver, DataGrip, pgAdmin expertise
  4. 4. Monitoring → Prometheus, Grafana, CloudWatch dashboards
  5. 5. Migrations → Flyway, Liquibase version control
  6. 6. Cloud platforms → AWS RDS, GCP Cloud SQL, Azure SQL
  7. 7. ETL/BI tools → Airflow, dbt, Power BI, Tableau

Advanced Specializations

  1. 1. Vector databases → Pinecone, Weaviate for AI applications
  2. 2. Time-series databases → TimescaleDB, InfluxDB for metrics
  3. 3. OLAP systems → ClickHouse, DuckDB, Snowflake analytics
  4. 4. Graph databases → Advanced Neo4j, relationship modeling
  5. 5. Search engines → Elasticsearch, OpenSearch full-text search
  6. 6. Data governance → Encryption, RBAC, audit logging frameworks

Career Development

  1. 1. Portfolio → GitHub with documented database projects
  2. 2. Blog posts → Technical writing on database topics
  3. 3. Certifications → AWS Database Specialty, GCP Professional
  4. 4. Open source → Contributions to database projects
  5. 5. Networking → Database community engagement and conferences