Bringing transparency and fairness to agricultural markets through intelligent data engineering
This enterprise-grade data platform addresses pricing transparency challenges in Ghana's agricultural markets by leveraging cloud-native Azure services to deliver fair, explainable commodity price recommendations.
The Challenge: Agricultural pricing in local markets often lacks transparency, leaving farmers vulnerable to price manipulation and traders without reliable market insights.
The Solution: An automated, scalable data pipeline that processes daily market data, applies transparent pricing algorithms, and delivers actionable insights through interactive dashboards and APIs.
- Farmers receive fair price recommendations based on transparent market analysis
- Traders detect market anomalies and optimize their buying strategies
- Policymakers gain data-driven insights for agricultural policy decisions
flowchart TB
subgraph "Data Sources"
A["Market Data Sources<br/>(CSV, API)"]
end
subgraph "Ingestion Layer"
B["Azure Data Factory<br/>Ingest Pipeline"]
end
subgraph "Storage & Processing"
C["Data Lake Raw Zone<br/>(Landing)"]
D["ADF Data Flows<br/>Normalize & Clean"]
E["Data Lake Curated Zone<br/>(Analytics Ready)"]
end
subgraph "Analytics & Compute"
F["Python Pricing Engine<br/>(Medians, Seasonality, Anomalies)"]
G["Azure Synapse Analytics<br/>Fact & Dimension Tables"]
end
subgraph "Consumption Layer"
H["Power BI Dashboards<br/>Farmers • Traders • Policymakers"]
I["Optional Node.js API<br/>Price Recommendations Endpoint"]
end
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
G --> I
style A fill:#e1f5fe,color:#000000
style B fill:#f3e5f5,color:#000000
style C fill:#fff3e0,color:#000000
style D fill:#fff3e0,color:#000000
style E fill:#fff3e0,color:#000000
style F fill:#e8f5e8,color:#000000
style G fill:#e8f5e8,color:#000000
style H fill:#fce4ec,color:#000000
style I fill:#fce4ec,color:#000000
- Automated ETL Pipelines: Azure Data Factory orchestrates daily data ingestion from multiple sources
- Multi-Zone Data Lake: Segregated raw, staged, and curated storage zones for optimal data governance
- Data Quality Assurance: Built-in validation, cleansing, and normalization processes
- Transparent Algorithms: Rolling median calculations with seasonal adjustments
- Anomaly Detection: Statistical outlier identification using MAD and Z-score techniques
- Explainable Results: Every price recommendation includes clear reasoning and confidence intervals
- Enterprise Data Warehouse: Star schema implementation in Azure Synapse Analytics
- Interactive Dashboards: Power BI reports tailored for different stakeholder groups
- Real-time Monitoring: Live tracking of price trends and market anomalies
- RESTful Endpoints: Node.js/TypeScript microservice for programmatic access
- Scalable Architecture: Cloud-native design supporting high-throughput requests
- Comprehensive Documentation: OpenAPI specifications for seamless integration
| Layer | Technology | Purpose |
|---|---|---|
| Orchestration | Azure Data Factory | ETL pipeline management & scheduling |
| Storage | Azure Data Lake Gen2 | Scalable, cost-effective data storage |
| Compute | Azure Synapse Analytics | Distributed data processing & warehousing |
| Analytics | Python (Pandas, NumPy, SciPy) | Statistical analysis & pricing algorithms |
| Visualization | Power BI | Interactive dashboards & reporting |
| API | Node.js + TypeScript + Express | RESTful web services |
| Infrastructure | Azure Bicep/ARM | Infrastructure as Code |
- Historical vs. recommended price comparisons
- Seasonal pattern identification
- Market volatility indicators
- Real-time spike/drop alerts
- Confidence scoring for price recommendations
- Market trend deviation analysis
- Farmer View: Fair price recommendations with market context
- Trader View: Arbitrage opportunities and risk indicators
- Policy View: Market health metrics and intervention triggers
- Azure subscription with required service permissions
- Python 3.9+ development environment
- Node.js 16+ (for optional API layer)
- Power BI Pro license
# Clone repository
git clone https://github.com/SamMintah/GCP-engine
cd GCP-engine
# Deploy Azure infrastructure
az deployment group create --resource-group rg-commodity-pricing \
--template-file infrastructure/main.bicep
# Configure data pipelines
python scripts/setup-pipelines.py
# Start local API (optional)
cd api && npm install && npm start- System Architecture Design - Comprehensive cloud-native architecture
- Infrastructure Planning - Azure services selection and configuration
- Data Model Design - Dimensional modeling for analytics warehouse
- Data Ingestion Pipeline - Azure Data Factory implementation
- Pricing Algorithm Engine - Python-based statistical processing
- Data Warehouse Setup - Synapse Analytics configuration
- Power BI Dashboard Development - Interactive reporting layer
- API Microservice - RESTful price recommendation service
- Performance Optimization - Query tuning and caching strategies
- Monitoring & Alerting - Operational observability implementation
This project demonstrates how modern data engineering practices can address genuine socioeconomic challenges. By focusing on transparency, scalability, and user-centric design, it showcases the potential for technology to create positive change in traditional agricultural markets.
- Price Accuracy: Target <5% deviation from true market value
- Processing Speed: <2 second response time for price recommendations
- System Reliability: 99.9% uptime for critical data pipelines
- User Adoption: Dashboard engagement metrics and API usage analytics
Interested in improving agricultural market transparency? Contributions are welcome!
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ for Ghana's agricultural community