Platform Architecture Overview
Platform Architecture Overview
The SDGL-SaaS Platform uses a six-layer architecture that forms an integrated, modular system designed for scale, reliability, and continuous learning.
Six-Layer Architecture
┌─────────────────────────────────────────────────────────────┐│ LAYER 1: SOURCE LAYER ││ (Data Collection & Integration) │└─────────────────────────────────────────────────────────────┘ ↓┌─────────────────────────────────────────────────────────────┐│ LAYER 2: INTEGRATION LAYER ││ (Data Normalization & Consolidation) │└─────────────────────────────────────────────────────────────┘ ↓┌─────────────────────────────────────────────────────────────┐│ LAYER 3: PROCESSING LAYER ││ (Analytics, ML, Computations) │└─────────────────────────────────────────────────────────────┘ ↓┌─────────────────────────────────────────────────────────────┐│ LAYER 4: STORAGE LAYER ││ (Persistent Data & Audit Trail) │└─────────────────────────────────────────────────────────────┘ ↓┌─────────────────────────────────────────────────────────────┐│ LAYER 5: ANALYTICS LAYER ││ (Aggregation, Reporting, Insights) │└─────────────────────────────────────────────────────────────┘ ↓┌─────────────────────────────────────────────────────────────┐│ LAYER 6: VISUALIZATION LAYER ││ (Dashboard, Reports, User Interface) │└─────────────────────────────────────────────────────────────┘Each layer builds upon the previous, enabling:
- Separation of Concerns: Each layer has specific responsibilities
- Scalability: Can independently scale any layer
- Reliability: Failures in one layer don’t cascade
- Innovation: New algorithms/interfaces don’t require full rewrite
Layer 1: Source Layer →
Data collection from diverse sources (surveys, IoT, systems, databases, APIs)
Layer 2: Integration Layer →
Normalize and consolidate data into unified schema
Layer 3: Processing Layer →
Run analytics, machine learning, and complex computations
Layer 4: Storage Layer →
Persistent database with full audit trail and compliance
Layer 5: Analytics Layer →
Aggregate results, identify patterns, generate insights
Layer 6: Visualization Layer →
Present insights via dashboards, reports, and user interfaces
Modular Microservices Architecture
The platform is built on microservices that operate independently:
Core Services
- Authentication Service: User identity, permissions, SSO
- Assessment Service: ESGETC scoring, materiality calculations
- Planning Service (PDCA): Goal setting, progress tracking, learning
- Consortium Service: Multi-stakeholder partnerships, Delphi process
- Entity Service: Discovery, classification, relationship mapping
- Analytics Service: KPI calculations, reporting, insights
- Integration Service: External systems, APIs, webhooks
Supporting Services
- Notification Service: Alerts, emails, in-app messages
- Reporting Service: PDF/Excel exports, customizable templates
- Search Service: Full-text and semantic search
- File Service: Document upload, storage, OCR
- Audit Service: Compliance logging, version control
- Cache Service: Performance optimization, real-time updates
Each microservice:
- Owns its database (no shared data stores)
- Uses REST APIs for inter-service communication
- Scales independently based on demand
- Can be deployed/updated independently
- Has explicit versioning and backward compatibility
Data Flow & Processing Pipelines
Real-Time Data Pipeline
IoT Sensors → Kafka → Real-time Processor → Cache/Dashboard(production equipment) (message queue) (anomaly detection)Batch Processing Pipeline
Surveys → Data Lake → Python/Spark Jobs → Analytics DB → Reports(user input) (staging) (transformations) (aggregated)Machine Learning Pipeline
Tagged Training Data → ML Models → Prediction Serving → Dashboard(historical assessments) (NLP, clustering, forecasting) (recommendations)Entity Discovery Pipeline
Multiple Sources → NLP Extraction → Deduplication → Verification → Platform(Web, DBs, APIs) (entity recognition) (pairwise matching) (human review)Learn more about data pipelines →
Security, Privacy, and Compliance Architecture
Security Layers
- Network: TLS/SSL encryption, VPN, firewall rules
- Application: Input validation, SQL injection prevention, XSS protection
- Data: Row-level security, field-level encryption, API keys
- Identity: Multi-factor authentication, role-based access control
- Audit: All actions logged with user/timestamp/details
Privacy Controls
- Data Minimization: Collect only what’s necessary
- Anonymization: Benchmark data stripped of identifiers
- Retention: Automatic deletion after policy period
- Portability: Export personal data in standard formats
- Right to be Forgotten: Comply with GDPR and regional laws
Compliance Standards
- SOC2 Type II: Security and availability audit
- GDPR: EU data protection compliance
- HIPAA: Health data confidentiality (where applicable)
- SDG Frameworks: Alignment with UN standards
- ESG Standards: GRI, SASB, TCFD reporting
Learn more about security and compliance →
Core Technology Stack
Frontend
- React 18: Modern UI framework with hooks
- TypeScript: Type-safe JavaScript
- Tailwind CSS: Utility-first styling
- D3.js / Three.js: Data visualization and 3D rendering
- Redux: State management for complex UIs
Backend
- Node.js: JavaScript runtime for servers
- TypeScript: Type-safe backend code
- Wasp: Full-stack framework (TypeScript + Node + React)
- Express/Fastify: HTTP servers
- GraphQL: Flexible API queries (optional)
Data & AI
- PostgreSQL: Primary relational database
- Redis: Caching and real-time updates
- Python/Pandas: Data processing and analytics
- Spark: Big data processing at scale
- TensorFlow/PyTorch: Machine learning models
- spaCy/Hugging Face: NLP for entity classification
Infrastructure
- Docker: Container deployment
- Kubernetes: Container orchestration (production)
- GitHub Actions: CI/CD pipelines
- AWS/GCP/Azure: Cloud platforms
- Terraform: Infrastructure as Code
Scalability & Performance
Handles Growth
- Users: From 100s to 100,000+ concurrent users
- Data: From millions to billions of data points
- Assessments: From dozens to 100,000+ organizations
- Partnerships: From small consortiums to country-wide networks
- Real-time: 1,000+ events per second through IoT pipeline
Performance Optimization
- Asynchronous Jobs: Long-running tasks don’t block UI
- Caching Layers: Frequently accessed data pre-computed
- Database Optimization: Indexes, partitioning, query optimization
- CDN: Global distribution of static assets
- API Rate Limiting: Prevent abuse, ensure fairness
Monitoring & Observability
- Prometheus/Grafana: Metrics collection and visualization
- ELK Stack: Centralized logging (Elasticsearch)
- Distributed Tracing: Understand performance bottlenecks
- Alerting: Proactive notification of issues
- Incident Response: Runbooks for common issues
Integration Points
External Data Sources
- World Bank Indicators
- UNDP SDG databases
- ESG rating agencies
- Country statistical bureaus
- OpenStreetMap
- Wikidata
- Custom REST APIs
Business Systems
- ERP: SAP, Oracle, NetSuite integration
- CRM: Salesforce, HubSpot
- HR: Workday, BambooHR
- Accounting: QuickBooks, FreshBooks
- Project Management: Asana, Jira, Monday
Communication
- Email (Gmail, Office 365)
- SMS/WhatsApp
- Slack webhooks
- Teams integration
- Custom webhooks
Payment
- Stripe for subscription billing
- Wire transfers for bulk contracts
- Government purchasing card integration
- Cryptocurrency (future roadmap)