Architecture & Data
Build the foundation of scalable AI systems. Master data architecture patterns that power production applications serving millions.
What You'll Master
Database Design
Design and implement database architectures optimized for AI workloads. Balance between structured and unstructured data storage.
- PostgreSQL for relational data
- MongoDB for document storage
- Schema design and normalization
- Indexing strategies for performance
- Migration and versioning strategies
Vector Databases
Implement specialized databases for AI embeddings. Build semantic search and recommendation systems at scale.
- Pinecone for managed vector search
- Weaviate for hybrid search
- pgvector for PostgreSQL embeddings
- Embedding generation and storage
- Similarity search optimization
Caching Systems
Implement multi-layer caching strategies to optimize performance and reduce costs in AI applications.
- Redis for in-memory caching
- CDN and edge caching
- API response caching
- Cache invalidation strategies
- LLM response caching
Queue Management
Build robust asynchronous processing systems for handling AI workloads and background tasks efficiently.
- RabbitMQ and Redis Queue
- Background job processing
- Priority queues and scheduling
- Rate limiting and throttling
- Dead letter queues and retry logic
Tools & Technologies
What You'll Build
Knowledge Management System
Build a comprehensive knowledge base with semantic search, automatic categorization, and intelligent retrieval.
- • Multi-format document storage
- • Vector-based semantic search
- • Automatic tagging and categorization
- • Version control and history
Real-time Analytics Platform
Create a high-performance analytics system with real-time data processing and intelligent caching.
- • Real-time data ingestion
- • Multi-level caching strategy
- • Aggregation pipelines
- • Predictive analytics
Task Processing System
Develop a scalable task processing system with queues, workers, and intelligent job scheduling.
- • Distributed task processing
- • Priority queue management
- • Retry logic and error handling
- • Job monitoring dashboard
Your Learning Journey
Database Fundamentals (Week 1-2)
Start with SQL basics. Design schemas. Implement CRUD operations. Learn indexing and query optimization. Build your first database-backed application.
Vector Storage (Week 3-4)
Implement vector databases. Store and query embeddings. Build semantic search. Create recommendation engines. Optimize similarity searches.
Caching Strategies (Week 5-6)
Master Redis and caching patterns. Implement cache layers. Optimize API responses. Reduce database load. Build cost-efficient systems.
Async Processing (Week 7-8)
Build queue systems. Implement background jobs. Handle long-running tasks. Create resilient processing pipelines. Scale to handle millions of jobs.
Ready to Build Scalable Systems?
Master the data architecture that powers modern AI applications.