Databases Advanced

Master databases at production scale

Go beyond SQL basics into the world of distributed systems, specialized databases, and production operations. Learn how companies like Netflix, Uber, and Amazon operate databases at massive scale with high availability, performance, and reliability.

What You'll Master

🌐
Distributed Systems

Aurora, Spanner, partitioning, sharding, and replication

🔧
Specialized Databases

Time-series, graph, full-text search, and vector databases for AI

🚀
Production Operations

Monitoring, security, backup/recovery, and disaster planning

⚡
Performance at Scale

Caching strategies, connection pooling, and query optimization

🏗️
Enterprise Patterns

Data modeling, ORMs, migrations, and high availability architectures

Why Advanced Database Skills Matter

Production databases at scale face challenges that never appear in tutorials: distributed transactions, replication lag, cache invalidation, disaster recovery, and monitoring billions of queries. These advanced skills separate senior engineers who can architect systems handling millions of users from those stuck debugging basic queries. Master the techniques that power Netflix's global streaming, Uber's real-time matching, and Stripe's payment infrastructure.

Start Learning: Relational Databases at Scale
Begin with distributed SQL systems: Aurora, Spanner, and beyond!

Course Index

  1. Relational Databases at Scale
    Aurora, Spanner, and cloud-native distributed SQL
  2. Partitioning & Sharding Strategies
    Scaling databases horizontally by dividing data across servers
  3. Stored Procedures, Triggers & Functions
    Moving logic into the database: when, why, and how
  4. Time-Series Databases
    Optimized storage and queries for timestamped data at scale
  5. Graph Databases
    When relationships matter more than the data itself
  6. Full-Text Search
    Finding needles in textual haystacks
  7. Vector Databases & AI Integration
    Storing embeddings for machine learning at scale
  8. Database Security
    Protecting data from threats and compliance violations
  9. Backup, Recovery & Disaster Planning
    Protecting data from hardware failures and disasters
  10. Database Migrations & Schema Evolution
    Managing schema changes safely across teams
  11. Data Modeling Patterns & Best Practices
    Temporal data, audit tables, soft deletes, and denormalization
  12. ORMs & Database Access Patterns
    SQLAlchemy, N+1 problem, lazy vs eager loading, performance
  13. Replication & High Availability
    Master-slave, read replicas, failover, connection pooling
  14. Database Caching Strategies
    Redis, cache-aside, write-through, invalidation patterns
  15. Database Monitoring & Observability
    Query performance, slow logs, metrics, alerts, Prometheus/Grafana

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