users
1/17- id (PK)
- tenant_id (FK)
- name
- password_hash
- role_id
- status
- created_at
- updated_at
Training Manual — Section 49 · Ultra Technical
Global Data Model & Database Schema
The ultra-technical data backbone — entity models, relationships, schemas, indexing strategy, multi-tenant isolation, audit logging, versioning, and AI-ready data pipelines.
01
Data Architecture Overview
Purpose
Create a global, unified, multi-tenant data architecture that supports QC, testing, fabric, production, sustainability, training, certification, community, partnership, AI, and governance.
Data Architecture Layers
Entity layerRelationship layerSchema layerIndexing layerVersioning layerAudit layerMulti-tenant layerAI data pipeline layer
02
Core Entity Model (Master Entities)
Master Entities
- User
- Factory
- Brand
- Buyer
- University
- Lab
- Tenant
- Role
- Permission
Operational Entities
- QC record
- Testing record
- Fabric inspection
- Production record
- Sustainability record
- Training module
- Assessment
- Certification
- Partnership
- Community post
- GGKI node
- SOP
- Version record
AI Entities
- AI decision log
- AI knowledge node
- AI model version
- AI inference record
03
Entity Relationship Model (ERM)
User ↔ Tenant
Many-to-OneUser ↔ Role
Many-to-ManyFactory ↔ QC records
One-to-ManyFactory ↔ Testing records
One-to-ManyFactory ↔ Production records
One-to-ManyFactory ↔ Sustainability records
One-to-ManyBuyer ↔ Testing rules
One-to-ManyBuyer ↔ QC rules
One-to-ManyGGKI node ↔ SOP
Many-to-ManyGGKI node ↔ AI knowledge node
One-to-OneAssessment ↔ Certification
One-to-OneCommunity post ↔ User
Many-to-OneVersion record ↔ SOP
One-to-ManyVersion record ↔ Testing rule
One-to-ManyVersion record ↔ QC rule
One-to-Many04
Database Schema (Top-Level Tables)
tenants
2/17- id (PK)
- name
- type (factory, brand, buyer, university, lab, NGO)
- region
- status
- created_at
- updated_at
roles
3/17- id (PK)
- name
- description
permissions
4/17- id (PK)
- name
- description
qc_records
5/17- id (PK)
- factory_id (FK)
- style_id
- date
- inspector_id
- defect_type
- defect_severity
- measurement_deviation
- aql_result
- corrective_action
- preventive_action
- version_id
- created_at
- updated_at
testing_records
6/17- id (PK)
- factory_id (FK)
- test_type
- test_method
- test_result
- buyer_requirement
- compliance_status
- version_id
- created_at
- updated_at
fabric_inspections
7/17- id (PK)
- factory_id (FK)
- roll_number
- defect_points
- gsm
- shrinkage
- color_consistency
- acceptance_status
- created_at
- updated_at
production_records
8/17- id (PK)
- factory_id (FK)
- line_id
- date
- output
- smv
- efficiency
- bottlenecks
- created_at
- updated_at
sustainability_records
9/17- id (PK)
- factory_id (FK)
- water_usage
- energy_usage
- waste_generated
- chemical_compliance
- carbon_emission
- created_at
- updated_at
training_modules
10/17- id (PK)
- title
- category
- difficulty
- content
- created_at
- updated_at
assessments
11/17- id (PK)
- user_id (FK)
- module_id (FK)
- mcq_score
- practical_score
- case_score
- viva_score
- project_score
- total_score
- created_at
- updated_at
certifications
12/17- id (PK)
- user_id (FK)
- certification_type
- level
- score
- issued_at
- expiry_at
partnerships
13/17- id (PK)
- partner_type
- partner_name
- status
- roadmap
- created_at
- updated_at
community_posts
14/17- id (PK)
- user_id (FK)
- category
- content
- upvotes
- status
- created_at
- updated_at
ggki_nodes
15/17- id (PK)
- category
- title
- description
- relationships (JSON)
- version_id
- created_at
- updated_at
sop_versions
16/17- id (PK)
- sop_id (FK)
- version_number
- change_log
- approved_by
- released_at
ai_decision_logs
17/17- id (PK)
- user_id (FK)
- input_text
- output_text
- model_version
- ggki_nodes_used (JSON)
- created_at
05
Indexing Strategy
High-Priority Indexes
users.email
qc_records.factory_id
testing_records.factory_id
fabric_inspections.factory_id
production_records.factory_id
sustainability_records.factory_id
ggki_nodes.category
sop_versions.sop_id
Indexing Goals
- Fast queries
- Low latency
- High scalability
06
Multi-Tenant Data Isolation
Isolation Methods
- Tenant ID column
- Row-level security
- Tenant-specific encryption keys
- Tenant-specific storage buckets
Tenant Types
Factory tenantBrand tenantBuyer tenantUniversity tenantLab tenantNGO tenant
07
Audit Logging Model
Audit Logs Include
user_id
action
entity
entity_id
old_value
new_value
timestamp
Audit Goals
- Full traceability
- Compliance
- Security
08
AI Data Pipeline Model
Pipeline Stages
- 1Input parsing
- 2Category classification
- 3GGKI retrieval
- 4Decision logic
- 5AI inference
- 6Logging
- 7Monitoring
- 8Retraining
AI Data Stored
- Inputs
- Outputs
- GGKI nodes
- Model version
- Confidence score
09
Data Governance Rules
No orphan data
No unversioned data
No unapproved data
No unencrypted data
No cross-tenant data leakage
10
Data Architecture Roadmap (2026–2036)
1
Phase 1
Master data model
2
Phase 2
Multi-tenant schema
3
Phase 3
AI data pipeline
4
Phase 4
Predictive data engine
5
Phase 5
Autonomous data governance system
Clean data, AI-ready, globally consistent
The data model connects to the enterprise architecture, GGKI governance, and knowledge versioning engine.