Deliverables

The curriculum covers what an architect should know. This covers what an architect actually hands over: 55 artifacts, each with a worked example, a note on when it is worth producing, who reads it, and the ways it usually goes wrong. Filter by the artifact's type, the kind of engagement that calls for it, the stack or industry it is drawn against, or the audience it is written for.

Identify the deliverable → 42 diagrams, shown one at a time with the answer withheld. Nothing to submit.

4 of 55 deliverables shown.

flowchart LR
  a["Idea accepted<br/><i>work 2d</i>"]
  w1(["wait 9d<br/>backlog"])
  b["Refinement<br/><i>work 1d</i>"]
  w2(["wait 4d<br/>awaiting design"])
  c["Build<br/><i>work 5d</i>"]
  w3(["wait 3d<br/>awaiting review"])
  d["Code review<br/><i>work 0.5d</i>"]
  w4(["wait 6d<br/>awaiting test env"])
  e["Test<br/><i>work 2d</i>"]
  w5(["wait 11d<br/>awaiting release window"])
  f["Release<br/><i>work 0.5d</i>"]

  a --> w1 --> b --> w2 --> c --> w3 --> d --> w4 --> e --> w5 --> f
  f --> tot["Work 11d &middot; Wait 33d<br/>Lead time 44d &middot; Flow efficiency 25%"]

Behavioural View

Value Stream Map

Every step from request to delivery with its work time and its wait time, exposing that most of the elapsed time is queueing.

Flow Metrics Discovery

flowchart LR
  subgraph e1["Ephemeral / PR"]
    direction TB
    a1["scaled to 1<br/>mocked externals<br/>synthetic data<br/><i>proves: it builds and unit-passes</i><br/>auto-destroy 24h"]
  end
  subgraph e2["Dev"]
    direction TB
    a2["shared<br/>real internal deps<br/>synthetic data<br/><i>proves: integration wiring</i><br/>anyone deploys"]
  end
  subgraph e3["Staging"]
    direction TB
    a3["prod-shaped topology<br/>real deps · sandbox externals<br/>masked prod-like data<br/><i>proves: contracts + performance</i><br/>pipeline only"]
  end
  subgraph e4["Production"]
    direction TB
    a4["full scale · multi-AZ<br/>real data<br/><i>proves: nothing — it serves</i><br/>pipeline + gate"]
  end
  e1 --> e2 --> e3 --> e4
  note["Same artifact promoted throughout.<br/>Config differs; the image digest does not."]

Deployment & Infrastructure View

Environment Topology

Which environments exist, what each one proves, what data it holds and who may deploy to it.

Environment Strategy Build

Analysis Artifact

Capacity & Sizing Model

Demand translated into resource, with the peak multiplier, the headroom and the failure-domain reserve stated as assumptions rather than buried.

Capacity Modelling Design

quadrantChart
  title Platform technologies
  x-axis "Retire" --> "Adopt"
  y-axis "Low usage" --> "High usage"
  quadrant-1 "Adopt — default choice"
  quadrant-2 "Hold — in use, do not extend"
  quadrant-3 "Retire — plan removal"
  quadrant-4 "Trial / Assess — bounded use"
  "Kubernetes (managed)": [0.92, 0.88]
  "PostgreSQL": [0.95, 0.93]
  "Kafka (managed)": [0.86, 0.72]
  "Terraform": [0.90, 0.80]
  "OpenTelemetry": [0.78, 0.55]
  "Service mesh": [0.55, 0.30]
  "Self-managed Elasticsearch": [0.18, 0.62]
  "Oracle DB": [0.12, 0.70]
  "Jenkins": [0.20, 0.45]
  "Nomad": [0.60, 0.08]
  "DuckDB (analytics)": [0.62, 0.12]

Analysis Artifact

Technology Radar

What the organisation has decided to adopt, trial, assess or retire — a decision record about technology choice, not a fashion report.

Architecture Principles Discovery