Architecture Decision Matrix

Module 7 silent write — print one handout per trainee

Use in: Module 7 silent reflection (2 min) → Round 1 share
Purpose: Force trainees to commit a structured tool recommendation before open debate, so Round 1 shares are concrete and Round 2 challenges have real positions to stress-test.

Related docs:

open-discussion-guide.qmd — full Module 7 facilitation
reflection-prompts.qmd § Module 7
TRAINING_MATERIAL_MIGRATION_PLAN.md §1 refinement #1 — dbt is not a third warehouse

When to use it

Time in Module 7	Activity	Matrix role
After animation	Silent write (2 min)	Trainees fill My pick column individually
Round 1 — Share	3–4 volunteers defend their row	Speakers point to their matrix
Round 2 — Challenge	Facilitator plays constraint cards	Ask: “Does row X still hold?”
Round 3 — Synthesis	Whiteboard table from group	Compare to individual matrices
Round 4 — Story revisit	Story sketch ↔︎ matrix	What changed?

If time is tight (single round only): keep the silent fill + Round 1 + close.

The matrix

Trainees fill one copy each — pen on paper, printed handout, or fillable PDF.

#	Dimension	Databricks	Snowflake	dbt
1	Skillset fit (Marcus’s SQL-heavy team)
2	Time-to-first-KPI (Priya’s Overview page)
3	Cost (compute + storage + licensing)
4	Governance / lineage (audit in Q3)
5	Streaming readiness (Module 8 — live dispatch)
6	ML readiness (Module 9 — tip prediction)
7	5-year maintenance risk (after MHP leaves)
	Trainee’s recommended stack	—	—	—
	One-sentence justification	—	—	—

Scoring guidance (announce verbally, do not print on the matrix):

Cells are trainee opinions — not graded. Short notes / scores / icons all fine.
The My pick column may combine tools (e.g. Snowflake + dbt) — that is the point of rows 4, 5, 7.
The justification must reference at least two row numbers.

Facilitator instructions

Before the session

Print one A4 / Letter handout per trainee (or share a fillable PDF).
Have Story design worksheets accessible — trainees compare in Round 4.

During silent fill (2 min)

Project the matrix on screen with My pick column blank.
No talking. No laptop research. Pens only.
Start a visible 2-minute timer.

During Round 1 — Share

Ask 3–4 volunteers, 2 min each:

“Read me your bottom row — recommended stack and one sentence why. Reference at least two row numbers.”

Capture on whiteboard:

Speaker	Recommended stack	Strongest two rows cited
1
2
3

During Round 2 — Challenge

When playing a constraint card from open-discussion-guide.qmd, ask:

“Which row does this card change?”
“If row 3 (cost) drops to one license — does your pick from row 1 still win?”
“Card C (real-time in 6 months) — re-score row 5 in your head. Does your stack change?”

During Round 3 — Synthesis

Use the group’s average answers to fill the whiteboard table — not the matrix template. The matrix is the input; the synthesis table is the output.

During Round 4 — Story revisit

“Compare your matrix to your Story design sketch. What does the matrix make obvious that the sketch missed?”

Pre-filled reference matrix (TRAINER ONLY — do not project)

Use only if discussion stalls or for trainer self-prep. Do not hand to trainees.

Dimension	Databricks	Snowflake	dbt
Skillset fit (SQL team)	Lower — notebooks, PySpark	Higher — worksheets, SQL-first	High — SQL + YAML; analytics-engineer role
Time-to-first-KPI	Medium — cluster warmup + notebooks	Fast — warehouse resumes in 2–5 s	Adds project setup; fastest after Bronze exists
Cost	DBU per VM-hour; idle clusters bill	Credit-per-second; auto-suspend default 10 min	No compute cost; rides on backend
Governance / lineage	Unity Catalog (3-level namespace, lineage, tags)	Horizon (RBAC, masking, tags)	Built-in docs + column lineage + tests
Streaming readiness	Structured Streaming, Auto Loader, LSDP	Snowpipe Streaming + Dynamic Tables (insert-only)	`dynamic_table` materialization (Snowflake)
ML readiness	sklearn, XGBoost, MLflow, AutoML — full OSS	Cortex ML (SQL), Snowpark ML (Python)	Feature table + tests; no model training
5-year maintenance risk (SQL team)	Higher — Spark / PySpark skills required	Lower — SQL maintainable; vendor managed	Low — version-controlled SQL; CI/CD friendly

Trainer talking points (deploy if room is silent ≥30 s):

“Snowflake + dbt + Power BI” is a common consultant default for SQL-heavy clients. It is not the only valid answer.
Databricks wins outright when Module 9 ML or Module 8 sub-second streaming is on the roadmap.
Combining all three is rare in production — extra integration cost. Discuss when it would be worth it.

Printable handout (one A4 / Letter page)

Suggested layout — the matrix above plus the following footer:

Three decisions, three layers:
[ PLATFORM ]  →  [ TRANSFORM ]  →  [ CONSUMPTION ]

Three constraints to weigh (revisit in Module 7 close):
1. COST          — affordable to run and license long-term?
2. PERFORMANCE   — fast enough today and for Module 8 streaming?
3. COMPLIANCE    — audit-ready lineage and tests by Q3?

Closing line — Elena:
"Technology is a decision. Architecture is responsibility."

Success signals

The matrix worked if Round 1 speakers:

Reference at least two row numbers in their justification
Distinguish platform vs transform layer explicitly
Mention at least one of cost, performance, compliance as a deciding constraint
Change at least one row when a Round 2 constraint card is played

Document history

Date	Change
2026-05-24	Initial — Module 7 structured decision matrix workshop exercise