Technical

Eight conformed dimensions, five facts, declared grains

Every table in the warehouse layer with its declared grain, its keys, its history policy and its privacy classification. A fact that cannot state its grain in one sentence is a fact nobody can safely aggregate.

Conformed dimensions: 8; Shared keys across seven domains
Facts: 5; Grain declared and constrained
Reporting views: 28; The published surface
Personal columns: 0; Not redacted. Never designed.

Deterministic synthetic data.Granite Auto Group is fictional.Real-engine validation pending.How this is governed

Warehouse built and testedDATA_DICTIONARY.mdv1.0 docs/source-to-target/lineage per entity docs/architecture-decisions/ADR-0006-scd-type-selection-phase-1.mdhistory policy

There is no personal data in this model, by construction

Names
Street addresses
Email addresses
Phone numbers
Full birth dates
Government identifiers
Bank and card information
Age: band only
Geography: county or market area

No record-level value appears anywhere on this page. The privacy field on each entity states its policy, and every row count is a table cardinality.

PRIVACY_AND_ETHICS.mdprohibited attributes

Filter the model

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History policy

Showing all 13 entities

Fact - square corners, left rule
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Choose an entity to see its declared grain, its keys, how it handles change over time, and how it is classified for privacy. The grain is the field worth reading first.

Every entity, with its declared grain

DIMENSION · 184 ROWS
Calendar
warehouse.dim_date
Built and tested
Declared grainOne row per calendar date
Primary key
date_key
Foreign keys
None
History policy
SCD Type 0 - immutable, insert-only
Privacy classification
Non-personal throughout. A calendar dimension cannot contain personal data.
Analytical use
Provides every date attribute used for period filtering, time intelligence, seasonality analysis, and selling-day normalization across every fact table.
Reporting view
reporting.vw_calendar
SalesGrossInventoryLead funnelMarketing
docs/source-to-target/STM-001-dim-date.mdsource-to-target mappingView docs/source-to-target/STM-001-dim-date.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 3 ROWS
Dealership
warehouse.dim_dealership
Built and tested
Declared grainOne row per dealership store version (SCD2)
Primary key
dealership_key
Foreign keys
None
History policy
SCD Type 2
Privacy classification
No personal data. dealership_id is a Synthetic identifier; every other column is Non-personal. Geography stops at city level; contact details are prohibited.
Analytical use
Conformed store dimension enabling every fact to be sliced by store, carrying the store attributes that drive nearly all comparative analysis.
Reporting view
reporting.vw_dealership
SalesGrossInventoryLead funnelMarketing
docs/source-to-target/STM-002-dim-dealership.mdsource-to-target mappingView docs/source-to-target/STM-002-dim-dealership.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 30 ROWS
Employee
warehouse.dim_employee
Built and tested
Declared grainOne row per employee role-assignment version (SCD2)
Primary key
employee_key
Foreign keys
dealership_key -> warehouse.dim_dealership
History policy
SCD Type 2
Privacy classification
employee_id is a Synthetic identifier; tenure_band is a Minimized personal attribute. Names, contact details, birth dates, and compensation are Prohibited.
Analytical use
Supplies the contextual attributes (tenure, department, store) required before any employee performance figure may be shown.
Reporting view
reporting.vw_employee
SalesGrossLead funnel
docs/source-to-target/STM-006-dim-employee.mdsource-to-target mappingView docs/source-to-target/STM-006-dim-employee.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 2,500 ROWS
Customer
warehouse.dim_customer
Built and tested
Declared grainOne row per synthetic customer
Primary key
customer_key
Foreign keys
None
History policy
SCD Type 1
Privacy classification
age_band, county, state_code are Minimized personal attribute; customer_id, household_key are Synthetic identifier; else Non-personal. All persons fabricated.
Analytical use
Represents the synthetic buying party on a sale, lead, or appointment, deliberately thin to support repeat-purchase and cohort analysis without profiling individuals.
Reporting view
reporting.vw_customer
SalesGrossLead funnel
docs/source-to-target/STM-007-dim-customer.mdsource-to-target mappingView docs/source-to-target/STM-007-dim-customer.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 900 ROWS
Vehicle
warehouse.dim_vehicle
Built and tested
Declared grainOne row per unique physical vehicle
Primary key
vehicle_key
Foreign keys
vehicle_model_key -> warehouse.dim_vehicle_model
History policy
SCD Type 1
Privacy classification
Non-personal throughout, with vehicle_id and synthetic_vin classed as Synthetic identifier. No VIN in ARPI is real or ever linked to owner history.
Analytical use
Describes an individual physical unit of inventory, distinguished from the model dimension; required by every inventory and sale measure.
Reporting view
reporting.vw_vehicle
SalesGrossInventoryLead funnel
docs/source-to-target/STM-005-dim-vehicle.mdsource-to-target mappingView docs/source-to-target/STM-005-dim-vehicle.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 120 ROWS
Vehicle model
warehouse.dim_vehicle_model
Built and tested
Declared grainOne row per model-year / make / model / trim combination
Primary key
vehicle_model_key
Foreign keys
None
History policy
SCD Type 1
Privacy classification
Non-personal throughout.
Analytical use
Configuration-level dimension enabling model, trim, and class analysis independent of physical unit counts, underpinning days-supply and model-mix reporting.
Reporting view
reporting.vw_vehicle_model
SalesGrossInventory
docs/source-to-target/STM-004-dim-vehicle-model.mdsource-to-target mappingView docs/source-to-target/STM-004-dim-vehicle-model.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 19 ROWS
Lead source
warehouse.dim_lead_source
Built and tested
Declared grainOne row per normalized lead source
Primary key
lead_source_key
Foreign keys
None
History policy
SCD Type 1
Privacy classification
Non-personal throughout.
Analytical use
Normalizes CRM lead origin into a governed set of sources so funnel and marketing measures are comparable across stores.
Reporting view
reporting.vw_lead_source
SalesLead funnelMarketing
docs/source-to-target/STM-010-dim-lead-source.mdsource-to-target mappingView docs/source-to-target/STM-010-dim-lead-source.md (source-to-target mapping) on GitHub (opens in a new tab)
DIMENSION · 24 ROWS
Marketing campaign
warehouse.dim_marketing_campaign
Built and tested
Declared grainOne row per campaign
Primary key
campaign_key
Foreign keys
lead_source_key -> warehouse.dim_lead_source
History policy
SCD Type 1 initially
Privacy classification
Non-personal throughout.
Analytical use
Describes marketing campaigns so that spend, leads, and resulting gross can be attributed to a named initiative rather than only to a channel.
Reporting view
reporting.vw_marketing_campaign
Lead funnelMarketing
docs/source-to-target/STM-013-dim-marketing-campaign.mdsource-to-target mappingView docs/source-to-target/STM-013-dim-marketing-campaign.md (source-to-target mapping) on GitHub (opens in a new tab)
FACT · 650 ROWS
Vehicle sale
warehouse.fact_vehicle_sale
Built and tested
Declared grainOne row per finalized vehicle transaction
Primary key
vehicle_sale_key
Foreign keys
sale_date_key -> warehouse.dim_date delivery_date_key -> warehouse.dim_date dealership_key -> warehouse.dim_dealership vehicle_key -> warehouse.dim_vehicle customer_key -> warehouse.dim_customer salesperson_key -> warehouse.dim_employee desk_manager_key -> warehouse.dim_employee finance_manager_key -> warehouse.dim_employee lead_source_key -> warehouse.dim_lead_source
History policy
Insert-only transaction fact
Privacy classification
Non-personal. customer_key and employee keys are synthetic surrogates.
Analytical use
The central profit fact, carrying the gross components that drive nearly every executive measure in the project.
Reporting view
reporting.vw_vehicle_sales
SalesGrossInventory
docs/source-to-target/STM-008-fact-vehicle-sale.mdsource-to-target mappingView docs/source-to-target/STM-008-fact-vehicle-sale.md (source-to-target mapping) on GitHub (opens in a new tab)
FACT · 45,754 ROWS
Inventory snapshot
warehouse.fact_vehicle_inventory_snapshot
Built and tested
Declared grainOne row per vehicle per dealership per daily snapshot date while active in inventory
Primary key
snapshot_date_key, dealership_key, vehicle_key
Foreign keys
snapshot_date_key -> warehouse.dim_date dealership_key -> warehouse.dim_dealership vehicle_key -> warehouse.dim_vehicle vehicle_model_key -> warehouse.dim_vehicle_model
History policy
Periodic snapshot, insert-only and immutable
Privacy classification
Non-personal throughout.
Analytical use
Daily photograph of every unit in stock, enabling inventory age, aged-inventory percentage, days supply, and inventory investment measures as of any date.
Reporting view
reporting.vw_inventory_snapshots
Inventory
docs/source-to-target/STM-009-fact-vehicle-inventory-snapshot.mdsource-to-target mappingView docs/source-to-target/STM-009-fact-vehicle-inventory-snapshot.md (source-to-target mapping) on GitHub (opens in a new tab)
FACT · 6,000 ROWS
Lead
warehouse.fact_lead
Built and tested
Declared grainOne row per unique CRM lead
Primary key
lead_key
Foreign keys
lead_created_date_key -> warehouse.dim_date dealership_key -> warehouse.dim_dealership customer_key -> warehouse.dim_customer vehicle_key -> warehouse.dim_vehicle vehicle_model_key -> warehouse.dim_vehicle_model lead_source_key -> warehouse.dim_lead_source campaign_key -> warehouse.dim_marketing_campaign assigned_salesperson_key -> warehouse.dim_employee assigned_bdc_employee_key -> warehouse.dim_employee
History policy
Accumulating snapshot
Privacy classification
Non-personal, with lead_id and original_lead_id classed as Synthetic identifier.
Analytical use
One row per CRM opportunity, carrying the funnel milestone flags the entire Lead Funnel page rests on.
Reporting view
reporting.vw_leads
Lead funnelMarketing
docs/source-to-target/STM-011-fact-lead.mdsource-to-target mappingView docs/source-to-target/STM-011-fact-lead.md (source-to-target mapping) on GitHub (opens in a new tab)
FACT · 2,111 ROWS
Appointment
warehouse.fact_appointment
Built and tested
Declared grainOne row per scheduled appointment
Primary key
appointment_key
Foreign keys
appointment_created_date_key -> warehouse.dim_date scheduled_date_key -> warehouse.dim_date show_date_key -> warehouse.dim_date dealership_key -> warehouse.dim_dealership lead_key -> warehouse.fact_lead customer_key -> warehouse.dim_customer salesperson_key -> warehouse.dim_employee bdc_employee_key -> warehouse.dim_employee vehicle_key -> warehouse.dim_vehicle vehicle_model_key -> warehouse.dim_vehicle_model
History policy
Insert-only transaction fact
Privacy classification
Non-personal throughout.
Analytical use
Isolates showroom appointment outcomes, a different grain from leads, to compute show rate and show-to-sale conversion.
Reporting view
reporting.vw_appointments
Lead funnel
docs/source-to-target/STM-012-fact-appointment.mdsource-to-target mappingView docs/source-to-target/STM-012-fact-appointment.md (source-to-target mapping) on GitHub (opens in a new tab)
FACT · 212 ROWS
Marketing spend
warehouse.fact_marketing_spend
Built and tested
Declared grainOne row per dealership, campaign, and calendar month
Primary key
month_date_key, dealership_key, campaign_key
Foreign keys
month_date_key -> warehouse.dim_date dealership_key -> warehouse.dim_dealership campaign_key -> warehouse.dim_marketing_campaign lead_source_key -> warehouse.dim_lead_source
History policy
Insert-only periodic fact
Privacy classification
Non-personal throughout.
Analytical use
Monthly marketing investment by store and campaign; the denominator for cost-per-lead and cost-per-sale, and divisor for gross return on advertising spend.
Reporting view
reporting.vw_marketing_spend
Marketing
docs/source-to-target/STM-014-fact-marketing-spend.mdsource-to-target mappingView docs/source-to-target/STM-014-fact-marketing-spend.md (source-to-target mapping) on GitHub (opens in a new tab)

Eight conformed dimensions, five facts, declared grains

There is no personal data in this model, by construction

Filter the model

No entity selected

Every entity, with its declared grain

Calendar

Dealership

Employee

Customer

Vehicle

Vehicle model

Lead source

Marketing campaign

Vehicle sale

Inventory snapshot

Lead

Appointment

Marketing spend