Enterprise System Design

An enterprise distributed systems design course and technical reference for building reliable, secure, and operable platforms on Azure, from first principles to production AI workloads.

┌─ architecture-console ──────────────────────────────────────────────────────┐
│                                                                             │
│  $ design --for production --with azure --assume partial-failure            │
│                                                                             │
│  INPUT   product intent, traffic, data, people, regulation                  │
│  OUTPUT  a system you can explain, operate, recover, and defend             │
│                                                                             │
│  [00] primitives  [01] systems  [02] retrieval  [03] control plane          │
│  [04] serving     [05] compute  [06] lifecycle  [07] safety                 │
│  [08] operations  [09] edge     [10] case studies                           │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

[!TIP] This is a source-grounded course for engineers who need to make architecture decisions under real constraints. Complete the engineering foundation first if you cannot yet trace, debug, disassemble, structure, and review production-shaped services.

System design turns product requirements into explicit contracts for data, compute, networking, identity, capacity, recovery, and operations. This course begins with those mechanics, then applies them to Azure platforms and AI workloads where token budgets, retrieval quality, model latency, and tool authority become part of the architecture.

Author's note

./boot

Architecture is the discipline of making system behavior explicit before traffic, failure, security review, or cost forces the issue. Each lesson follows a request through its boundaries: identity, network, API, data, queue, model, operator, and recovery path.

The Azure Well-Architected Framework provides the recurring review lens: reliability, security, cost optimization, operational excellence, and performance efficiency.

git clone https://github.com/DrHazemAli/enterprise-system-design.git
cd enterprise-system-design
open foundation/README.md

./principles --level principal-distinguished

$ principles --author "Hazem Ali" --scope consequential-systems

  HAZEM'S PRINCIPLES
  Informed by enterprise engineering work and published systems research.
  Covers engineering, AI, cybersecurity, networking, mission-critical systems, and operations.

  Begin: principles/README.md

Open Hazem's Principles.

./choose-route

$ route --list

  00  BUILD ENGINEERING JUDGMENT
      Execution, debugging, disassembly, SOLID, Clean Architecture, and workload review.
      Begin: foundation/README.md

  01  BUILD THE MENTAL MODEL
      Cloud, networks, APIs, data, and LLM mechanics.
      Begin: reference/00-ground-zero/00-cloud-computing-and-azure-resource-manager.md

  02  DESIGN A RELIABLE SERVICE
      Scale, consistency, estimation, caches, queues, and backpressure.
      Begin: reference/01-system-design-foundations/06-scale-from-one-to-one-million.md

  03  DESIGN AN ENTERPRISE AI SYSTEM
      Retrieval, permission-aware data, rate limits, routing, agents, and serving.
      Begin: reference/02-data-and-retrieval/12-embeddings-and-vector-similarity.md

  04  STUDY HAZEM'S PRINCIPLES
      Contracts, invariants, authority boundaries, verification, containment, and recovery.
      Begin: principles/README.md

Hazem's Principles are organized into six disciplines. Most disciplines contain chapters 01 through 05; cybersecurity extends through 07 for auditable agent authority and inference-memory integrity:

./atlas --visual

Evidence before generation
Trace retrieval, authorization, grounding, and response construction.

Every token has a path
Inspect admission, execution, memory, and response delivery.

Route for failure
Choose a model, absorb faults, and retain a governed fallback.

Serve without wasting memory
Reason about context state, scheduling, and throughput.

./map --dependency-flow

flowchart LR
  Z[Foundation  
Engineering judgment] --> A
    subgraph Foundations
        A[00  
Ground zero] --> B[01  
System design]
    end
    subgraph AI systems
        B --> C[02  
Data and retrieval]
        C --> D[03  
AI control plane]
        D --> E[04  
Inference and serving]
    end
    subgraph Production reality
        E --> F[05  
Execution and HPC]
        F --> G[06  
Model lifecycle]
        G --> H[07  
Security and safety]
        H --> I[08  
Operations]
        I --> J[09  
Edge and hybrid]
        J --> K[10  
Case studies]
    end

./curriculum --modules

./principles --domains

./lesson --guarantees

Every chapter leaves a design artifact behind:

  requirements + assumptions        architecture + trust boundaries
  request and recovery flows        interfaces + data ownership
  capacity and cost reasoning       failure modes + observability
  security controls + alternatives  review prompt + decision record

$ cd /github/DrHazemAli/enterprise-system-design