64 lines
3.5 KiB
Markdown
64 lines
3.5 KiB
Markdown
# GIS and Weather Data Processing Container Proposal
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## Proposal Summary
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Create specialized Docker containers for GIS data processing and weather data analysis to support CTO-mode R&D activities, particularly for infrastructure planning and balloon path prediction for your TSYS Group projects.
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## Business Rationale
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As GIS and weather data analysis become increasingly important for your TSYS Group projects (particularly for infrastructure planning like solar farms and building datasets, and balloon path prediction), there's a need for specialized containers that can handle these data types efficiently while maintaining consistency with existing infrastructure patterns. The containers will support:
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- Self-hosted GIS stack for privacy and control
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- Processing public datasets (NOAA, European APIs, etc.)
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- ETL workflows for both technical and business data processing
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- Integration with MinIO for data output to business systems
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## Technical Approach
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- Follow the same disciplined container architecture as the documentation tools
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- Use layered approach with base and specialized containers
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- Implement same security patterns (non-root user, UID/GID mapping)
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- Maintain consistent naming conventions
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- Use same operational patterns (wrapper scripts, etc.)
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- Include PostGIS, DuckDB, and optional GPU support
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- Implement MinIO integration for data output
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- Support for prototyping on workstations and production on large servers
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## Technology Stack
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- **GIS Tools**: GDAL, Tippecanoe, DuckDB with spatial extensions
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- **Database**: PostgreSQL/PostGIS client tools
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- **Formats**: Shapefiles, Parquet, GRIB, GeoJSON
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- **Weather**: cfgrib, xarray, MetPy
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- **ETL**: Pandas, Dask, custom workflow tools
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- **APIs**: NOAA, European weather APIs
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- **Visualization**: Folium, Plotly, command-line tools
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## Benefits
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- Consistent environment across development (workstations) and production (large servers)
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- Proper file permission handling across different systems
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- Isolated tools prevent dependency conflicts
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- Reproducible analysis environments for GIS and weather data
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- Integration with documentation tools for CTO mode workflows
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- Support for both technical (GIS/Weather) and business (COO) ETL workflows
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- Scalable architecture with optional GPU support
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- Data output capability to MinIO buckets for business use
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## Resource Requirements
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- Development time: 3-4 weeks for complete implementation
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- Storage: Additional container images (est. 3-6GB each)
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- Compute: Higher requirements for processing (can be isolated to CTO mode)
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- Optional: GPU resources for performance-intensive tasks
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## Expected Outcomes
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- Improved capability for spatial and weather data analysis
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- Consistent environments across development and production systems
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- Better integration with documentation workflows
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- Faster setup for ETL projects (both technical and business)
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- Efficient processing of large datasets using DuckDB and Parquet
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- Proper data output to MinIO buckets for business use
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- Reduced technical debt through consistent patterns
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## Implementation Timeline
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- Week 1: Base GIS container with PostGIS, DuckDB, and data format support
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- Week 2: Base Weather container with GRIB support and API integration
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- Week 3: Advanced processing containers with Jupyter and visualization
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- Week 4: Optional GPU variants and MinIO integration testing
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## Approval Request
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Please review and approve this proposal to proceed with implementation of the GIS and weather data processing containers that will support your infrastructure planning and balloon path prediction work. |