Technical Documentation¶

Overview¶

This documentation covers technical methodologies, best practices, and implementation approaches used in geospatial systems architecture, data engineering, and cloud infrastructure development.

The content has been organized into focused, actionable guides that you can find in the navigation menu:

Best Practices¶

Core Development Practices¶

• Python Package - Building and shipping Python packages with uv and modern tooling
• Docker & Compose - Multi-stage Dockerfiles, Buildx + Bake, and production deployment
• Rust Development Environment - Setting up and operating Rust development environments

Geospatial & Data Engineering¶

• Geospatial Data Engineering - Spatial data management and raster processing
• GeoParquet Data Warehouses - Modern geospatial data warehouses with columnar storage
• ETL Pipeline Design - Building robust, scalable ETL pipelines with Airflow

Cloud & Infrastructure¶

• Cloud Architecture - AWS infrastructure patterns and Kubernetes deployment
• AWS Serverless Geospatial - Serverless geospatial processing with Lambda and Step Functions
• Data Engineering - ETL pipeline design and real-time data processing

API & Performance¶

• API Development - FastAPI geospatial API development
• FastAPI Geospatial - High-performance geospatial APIs with spatial data handling
• Database Optimization - PostgreSQL/PostGIS tuning and performance
• Web Performance Optimization - Core Web Vitals, SEO, and accessibility

Quality & Monitoring¶

• Testing Best Practices - Comprehensive test suites and quality assurance
• Performance Monitoring - Application performance monitoring and observability

Tutorials¶

PostGIS & Spatial Data¶

• PostGIS Geometry Indexing - Best practices for PostGIS geometry indexing and spatial queries
• PostGIS Raster Indexing - PostGIS raster indexes and coverage mosaics
• Raster–Vector Workflows - Hybrid raster-vector workflows in PostGIS

Data Processing & Analytics¶

• parquet_s3_fdw with Local, MinIO, Vast, and AWS - Using parquet_s3_fdw with various backends
• GeoParquet with Polars - Storing, querying, and optimizing GeoParquet files with Polars
• Real-Time Data Processing - Kafka and TimescaleDB for real-time geospatial data processing

Development & Deployment¶

• Creating MkDocs GitHub Site - Setting up a MkDocs GitHub site
• R Shiny Geospatial App - Building and deploying interactive geospatial R Shiny applications
• Monitoring with Grafana & Prometheus - Setting up monitoring with Grafana, Prometheus, and Node Exporter

Key Technologies Covered¶

• Languages: Python, Rust, R, SQL, JavaScript
• Databases: PostgreSQL/PostGIS, TimescaleDB, Redis
• Cloud: AWS (Lambda, S3, ECS, EKS), Docker, Kubernetes
• Data Formats: GeoParquet, Parquet, GeoJSON, Shapefile
• Processing: Kafka, Airflow, Polars, Pandas, GeoPandas
• APIs: FastAPI, RESTful APIs, OpenAPI
• Monitoring: Grafana, Prometheus, CloudWatch
• Development: Git, CI/CD, Testing, Documentation

Getting Started¶

1. Choose your focus area from the Best Practices or Tutorials sections
2. Follow the step-by-step guides with copy-paste runnable code
3. Adapt the examples to your specific use case
4. Reference the anti-patterns to avoid common mistakes

Each guide is designed to be: - Actionable: Copy-paste runnable code and configurations - Comprehensive: Covers the full development lifecycle - Production-ready: Includes monitoring, security, and optimization - Well-documented: Clear explanations and rationale for each step

Additional Resources¶

• Professional Profile - Background and experience
• Projects Portfolio - Technical implementations
• Contact & Collaboration - Get in touch