⌘K

Course Contents

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From Design to Production Client-Server to N-Tier The Monolith (and When It's Right)Microservices — and When to Split Serverless Event-Driven Architecture & P2P Choosing a Style: The Rubric Checkpoint: Architecture Styles

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Service Discovery API Gateway & BFF Sidecar & Service Mesh Externalized Configuration Strangler Fig & Anti-Corruption Layer Database per Service CQRS Event Sourcing Saga: Orchestration vs Choreography Checkpoint: Microservices Patterns

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VMs vs Containers Kubernetes: The 20% That Matters Autoscaling Policies EC2 vs EKS vs Lambda: The Decision Edge Computing Multi-Cloud: The Reality Check Codelab: Containerize & Deploy to K8s Checkpoint: Compute & Orchestration

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Batch vs Stream MapReduce & Its Descendants ETL vs ELT & Data Pipelines Lakes, Warehouses, Lakehouse Lambda vs Kappa Architecture Streaming Engines Windowing & Exactly-Once Counting at Scale & Top-K Checkpoint: Big Data & Streaming

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CI/CD for Systems Rolling, Blue-Green, Canary Feature Flags A/B Testing Infrastructure Schema Migrations: Expand-Contract Zero-Downtime Data Migrations Rollback Strategy Codelab: Canary Rollout Checkpoint: Deployment & Evolution

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The Three Pillars Structured Logging & Aggregation Correlation IDs Metrics & Cardinality Distributed Tracing Alerts & Runbooks in Practice Observability-Driven Debugging Codelab: Distributed Tracing Checkpoint: Observability

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TLS & mTLS in Architecture Encryption at Rest & Secrets Management RBAC & Zero Trust DDoS & Abuse Protection Capacity Planning Cloud Cost Engineering Cost-Aware Design Review The Production-Readiness Checklist Checkpoint: Security & Cost Capstone Pass 4: The Marketplace Goes to Production

Counting at Scale & Top-K

Count at scale when you cannot keep everything — three kinds of how-many, sums versus idempotent sets, when approximate counts are allowed, the top-k trap, mergeable summaries, and the counting pipeline.

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