6.1 KiB
6.1 KiB
GIS and Weather Data Processing Container Plan
Overview
This document outlines the plan for creating Docker containers to handle GIS data processing and weather data analysis. These containers will be used exclusively in CTO mode for R&D and data analysis tasks, with integration to documentation workflows and MinIO for data output.
Requirements
GIS Data Processing
- Support for Shapefiles and other GIS formats
- Self-hosted GIS stack (not Google Maps or other commercial services)
- Integration with tools like GDAL, Tippecanoe, DuckDB
- Heavy use of PostGIS database
- Parquet format support for efficient data storage
- Based on reference workflows from:
Weather Data Processing
- GRIB data format processing
- NOAA and European weather APIs integration
- Bulk data download via HTTP/FTP
- Balloon path prediction system (to be forked/modified)
Shared Requirements
- Python-based with appropriate libraries (GeoPandas, DuckDB, etc.)
- R support for statistical analysis
- Jupyter notebook integration for experimentation
- MinIO bucket integration for data output
- Optional but enabled GPU support for performance
- All visualization types (command-line, web, desktop)
- Flexible ETL capabilities for both GIS/Weather and business workflows
Proposed Container Structure
RCEO-AIOS-Public-Tools-GIS-Base
- Foundation container with core GIS libraries
- Python + geospatial stack (GDAL, GEOS, PROJ, DuckDB, Tippecanoe)
- R with spatial packages
- PostGIS client tools
- Parquet support
- File format support (Shapefiles, GeoJSON, etc.)
RCEO-AIOS-Public-Tools-GIS-Processing
- Extends GIS-Base with advanced processing tools
- Jupyter with GIS extensions
- Specialized ETL libraries
- Performance optimization tools
RCEO-AIOS-Public-Tools-Weather-Base
- Foundation container with weather data libraries
- GRIB format support (cfgrib)
- NOAA and European API integration tools
- Bulk download utilities (HTTP/FTP)
RCEO-AIOS-Public-Tools-Weather-Analysis
- Extends Weather-Base with advanced analysis tools
- Balloon path prediction tools
- Forecasting libraries
- Time series analysis
RCEO-AIOS-Public-Tools-GIS-Weather-Fusion (Optional)
- Combined container for integrated GIS + Weather analysis
- For balloon path prediction using weather data
- High-resource container for intensive tasks
Technology Stack
GIS Libraries
- GDAL/OGR for format translation and processing
- GEOS for geometric operations
- PROJ for coordinate transformations
- PostGIS for spatial database operations
- DuckDB for efficient data processing with spatial extensions
- Tippecanoe for tile generation
- Shapely for Python geometric operations
- GeoPandas for Python geospatial data handling
- Rasterio for raster processing in Python
- Leaflet/Mapbox for web visualization
Data Storage & Processing
- DuckDB with spatial extensions
- Parquet format support
- MinIO client tools for data output
- PostgreSQL client for connecting to external databases
Weather Libraries
- xarray for multi-dimensional data in Python
- cfgrib for GRIB format handling
- MetPy for meteorological calculations
- Climate Data Operators (CDO) for climate data processing
- R packages: raster, rgdal, ncdf4, rasterVis
Visualization
- Folium for interactive maps
- Plotly for time series visualization
- Matplotlib/Seaborn for statistical plots
- R visualization packages
- Command-line visualization tools
ETL and Workflow Tools
- Apache Airflow (optional in advanced containers)
- Prefect or similar workflow orchestrators
- DuckDB for ETL operations
- Pandas/Dask for large data processing
Container Deployment Strategy
Workstation Prototyping
- Lighter containers for development and testing
- Optional GPU support
- MinIO client for data output testing
Production Servers
- Full-featured containers with all processing capabilities
- GPU-enabled variants where applicable
- Optimized for large RAM/CPU/disk requirements
Security & User Management
- Follow same non-root user pattern as documentation containers
- UID/GID mapping for file permissions
- Minimal necessary privileges
- Proper container isolation
- Secure access to MinIO buckets
Integration with Existing Stack
- Compatible with existing user management approach
- Can be orchestrated with documentation containers when needed
- Follow same naming conventions
- Use same wrapper script patterns
- Separate from documentation containers but can work together in CTO mode
Implementation Phases
Phase 1: Base GIS Container
- Create GIS-Base with GDAL, DuckDB, PostGIS client tools
- Implement Parquet and Shapefile support
- Test with sample datasets from reference posts
- Validate MinIO integration
Phase 2: Weather Base Container
- Create Weather-Base with GRIB support
- Integrate NOAA and European API tools
- Implement bulk download capabilities
- Test with weather data sources
Phase 3: Processing Containers
- Create GIS-Processing container with ETL tools
- Create Weather-Analysis container with prediction tools
- Add visualization and Jupyter support
- Implement optional GPU support
Phase 4: Optional Fusion Container
- Combined container for balloon path prediction
- Integration of GIS and weather data
- High-complexity, high-resource usage
Data Flow Architecture
- ETL workflows for processing public datasets
- Output to MinIO buckets for business use
- Integration with documentation tools for CTO mode workflows
- Support for both GIS/Weather ETL (CTO) and business ETL (COO)
Next Steps
- Review and approve this enhanced plan
- Begin Phase 1 implementation
- Test with sample data from reference workflows
- Iterate based on findings
Risks & Considerations
- Large container sizes due to GIS libraries and dependencies
- Complex dependency management, especially with DuckDB and PostGIS
- Computational resource requirements, especially for large datasets
- GPU support implementation complexity
- Bulk data download and processing performance