GIS and Weather Data Processing Container Proposal

Proposal Summary

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.

Business Rationale

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:

Self-hosted GIS stack for privacy and control
Processing public datasets (NOAA, European APIs, etc.)
ETL workflows for both technical and business data processing
Integration with MinIO for data output to business systems

Technical Approach

Follow the same disciplined container architecture as the documentation tools
Use layered approach with base and specialized containers
Implement same security patterns (non-root user, UID/GID mapping)
Maintain consistent naming conventions
Use same operational patterns (wrapper scripts, etc.)
Include PostGIS, DuckDB, and optional GPU support
Implement MinIO integration for data output
Support for prototyping on workstations and production on large servers

Technology Stack

GIS Tools: GDAL, Tippecanoe, DuckDB with spatial extensions
Database: PostgreSQL/PostGIS client tools
Formats: Shapefiles, Parquet, GRIB, GeoJSON
Weather: cfgrib, xarray, MetPy
ETL: Pandas, Dask, custom workflow tools
APIs: NOAA, European weather APIs
Visualization: Folium, Plotly, command-line tools

Benefits

Consistent environment across development (workstations) and production (large servers)
Proper file permission handling across different systems
Isolated tools prevent dependency conflicts
Reproducible analysis environments for GIS and weather data
Integration with documentation tools for CTO mode workflows
Support for both technical (GIS/Weather) and business (COO) ETL workflows
Scalable architecture with optional GPU support
Data output capability to MinIO buckets for business use

Resource Requirements

Development time: 3-4 weeks for complete implementation
Storage: Additional container images (est. 3-6GB each)
Compute: Higher requirements for processing (can be isolated to CTO mode)
Optional: GPU resources for performance-intensive tasks

Expected Outcomes

Improved capability for spatial and weather data analysis
Consistent environments across development and production systems
Better integration with documentation workflows
Faster setup for ETL projects (both technical and business)
Efficient processing of large datasets using DuckDB and Parquet
Proper data output to MinIO buckets for business use
Reduced technical debt through consistent patterns

Implementation Timeline

Week 1: Base GIS container with PostGIS, DuckDB, and data format support
Week 2: Base Weather container with GRIB support and API integration
Week 3: Advanced processing containers with Jupyter and visualization
Week 4: Optional GPU variants and MinIO integration testing

Approval Request

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.

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