A dynamic geographic data processing service for next-generation retail technologies.
GeoResolver is a powerful geographic data processing and resolution system designed to provide advanced location-based insights for retail operations. As the modern retail ecosystem becomes increasingly dependent on hyper-personalized experiences and optimized logistics, GeoResolver will fulfill a critical role in enabling StarlightRetail to leverage location intelligence to improve customer engagement, operational efficiency, and decision-making. GeoResolver transforms raw location data into actionable insights by resolving geographic coordinates, regions, and context while supporting integrations with other systems in the StarlightRetail technology stack.
This project will deliver value by addressing challenges such as geospatial data normalization, distance calculation, boundary determination, and location-based event triggering, ensuring that StarlightRetail remains competitive in an increasingly data-driven retail marketplace.
GeoResolver aims to solve the problem of fragmented and difficult-to-interpret geographic data in retail systems. It provides businesses with the ability to:
- Normalize inconsistent location inputs (e.g., customer address inaccuracies or GPS mismatches).
- Derive meaningful insights such as point-of-sale geolocation analysis, delivery area optimization, and personalized store offers tailored to geographic context.
- Enable faster, smarter decision-making by integrating with StarlightRetail's broader retail tech ecosystem.
Benefits of GeoResolver include enhanced customer satisfaction, reduced logistics costs, improved inventory planning, and localized marketing initiatives. It will serve a wide range of stakeholders within StarlightRetail, including data analysts, product managers, logistics coordinators, and marketing teams.
- Geospatial Data Normalization: Convert raw geographic inputs (addresses, coordinates, etc.) into clean, consistent formats.
- Address Validation & Resolution: Validate user-provided addresses and resolve them into geographic coordinates or regions.
- Boundary Mapping: Identify and classify geographic boundaries like delivery zones, store locations, and customer regions.
- Distance Calculations: Enable calculations between geographic points for delivery routes, store proximity, or customer-to-store distances.
- Integration with StarlightRetail Systems: Provide APIs for seamless integration with customer profiles, logistics, and analytics dashboards.
- Event Triggering: Support location-based event triggers such as notifications for nearby offers or store promotions.
- Scalable Architecture: Ensure GeoResolver can handle high volume data processing for enterprise-level retail operations.
- Observability & Monitoring: Streamlined tools for tracking service performance, with real-time alerts for geo-resolution failures.
- Privacy Compliance: Adhere to data privacy regulations for location-based services such as GDPR and CCPA.
- Documentation and Developer SDKs: Provide extensive docs and easy-to-use SDKs for internal and external developers.
Tasks:
- Project Scaffolding: Initialize the GitHub repository with CI/CD pipeline (GitHub Actions).
- Core Architecture Decisions: Define microservice architecture for GeoResolver.
- Development Environment Configuration: Configure local and containerized dev environments using Docker.
- Task Breakdown:
- Add
.gitignoreand README.md scaffolding. - Define folder structure (
src/,tests/) and initialize key files. - Install pre-commit hooks for linting and code formatting.
- Set up CI/CD pipelines for automated testing and code quality checks.
- Add
Tasks:
- Build Geospatial Data Normalization Service: Implement core functionality for standardizing geographic inputs.
- Develop Address Resolution System: Configure API endpoints for address validation (via third-party APIs, if needed).
- Boundary Management Module: Create an internal module for mapping geographic boundaries.
- Distance Calculation Logic: Use geospatial libraries (e.g., GeoPy, PostGIS) to implement distance calculation algorithms.
- Database Design: Deploy PostgreSQL with PostGIS extension for efficient geospatial data storage.
- API Development: Build RESTful APIs to expose GeoResolver functionalities.
Tasks:
- System Integration: Connect GeoResolver APIs with StarlightRetail’s CRM and logistics platforms.
- Comprehensive Testing: Write unit, integration, and end-to-end tests to ensure robustness.
- Performance Benchmarking: Stress-test GeoResolver against high-volume data scenarios and optimize for scalability.
Tasks:
- Deployment Pipeline: Implement CI/CD pipelines to automate production deployments.
- Monitoring and Observability: Add monitoring tools (e.g., Prometheus and Grafana) for real-time system tracking.
- Finalize Documentation: Ensure all APIs are documented with clear instructions for use.
- Team Onboarding: Create comprehensive onboarding materials to train StarlightRetail’s developers for GeoResolver usage.
| Component | Purpose |
|---|---|
| GeoResolver Service | Core microservice for geospatial data processing. |
| Database Layer | PostgreSQL with PostGIS for storing geographic data efficiently. |
| API Gateway | RESTful API layer for external integrations. |
| Monitoring Suite | Prometheus/Grafana for observability. |
| Data Sources | External APIs for enhanced address resolution. |
| CRM Systems | Integration layer for StarlightRetail CRM services. |
| Analytics Dashboards | Endpoint data feeds to support analytical reporting. |
| Category | Technology | Rationale |
|---|---|---|
| Language | Python | Python has rich geospatial libraries like GeoPy. |
| Framework | FastAPI | High-performance API framework with Python. |
| Database | PostgreSQL + PostGIS | Efficient handling of geospatial data queries. |
| Testing Framework | pytest | Mature testing framework suitable for Python. |
| CI/CD | GitHub Actions | Automate builds, tests, and deployments. |
| Deployment | Docker/Kubernetes | Scalable and containerized deployments. |
| Monitoring | Prometheus + Grafana | Real-time observability tools for microservices. |
| External API | Google Maps API | Address validation and geocoding. |
GeoResolver/
├── src/
│ ├── resolvers/
│ │ ├── normalization.py
│ │ ├── boundary.py
│ │ ├── distance.py
│ ├── api/
│ │ ├── app.py
│ │ ├── routes.py
│ └── utils/
├── tests/
│ ├── test_normalization.py
│ ├── test_boundary.py
│ ├── test_distance.py
├── docs/
│ ├── api_specs.md
│ └── onboarding.md
├── Dockerfile
├── requirements.txt
├── README.md
├── .gitignore
└── .github/
├── workflows/
├── ci.yaml
- Prerequisites:
- Install Python 3.9+ and Docker.
- Clone the repository:
git clone https://github.com/STARLIGHTRETAIL/GeoResolver.git.
- Configure Environment:
- Run
python3 -m venv envto create a virtual environment. - Activate it, then install dependencies:
pip install -r requirements.txt.
- Run
- Run Locally:
- Start the development server:
python src/api/app.py.
- Start the development server:
- Run Tests:
- Execute tests using
pytest tests/.
- Execute tests using
| Milestone | Target Date | Success Criteria | Status |
|---|---|---|---|
| Foundation setup | Week 2 | Repository initialized with CI/CD and scaffolding | Not Started |
| Core development | Week 6 | Implemented primary features | Not Started |
| Testing & integration | Week 8 | Completed testing and system integrations | Not Started |
| Production launch | Week 10 | GeoResolver deployed and operational | Not Started |
| Documentation finalization | Week 10 | Detailed documentation for APIs and onboarding | Not Started |
- Branch Naming: Use
feature/<feature-name>for new features andfix/<issue-description>for bug fixes. - Pull Requests: Ensure PRs are small, focused, and linked to an issue.
- Code Reviews: All PRs require review by at least one senior developer prior to merging.
This project is currently unlicensed, but MIT is recommended for open-source use.
StarlightRetail — Building the future of retail technology
Made with 💜 by the StarlightRetail team