Building a Scalable STAC‑Based Geospatial Data Platform on AWS

# Context & History The agricultural technology sector has increasingly relied on high‑resolution satellite and drone imagery to provide field‑level insights. Early attempts to store such data used ad‑hoc file systems, leading to fragmented metadata and slow discovery. The introduction of the SpatioTemporal Asset Catalog (STAC) standard, documented on [Wikipedia](https://en.wikipedia.org/wiki/SpatioTemporal_Asset_Catalog), gave the industry a common language for describing raster and vector assets. BASF Digital Farming adopted this standard to replace legacy pipelines, enabling rapid onboarding of new sensors and consistent access across web and mobile tools. ## Implementation & Best Practices Before diving into component details, outline the deployment process first define the metadata schema and collections, then provision storage buckets and the PostgreSQL database with the pgSTAC extension, next set up tile services for raster and vector data, after that configure the Kubernetes cluster and autoscaling policies, and finally integrate API Gateway and monitoring. Following this roadmap ensures each layer is validated before the next is introduced, reducing integration friction. ### Storage Layer with Amazon S3 All raw and processed assets reside in S3. Raster images are stored as Cloud‑Optimized GeoTIFFs (COGs), which support range requests for partial reads. Vector data uses formats such as FlatGeobuf that allow streaming and efficient indexing. Organize buckets by data type and acquisition date to simplify lifecycle rules and cost management. ### Metadata Management with pgSTAC on Amazon RDS pgSTAC extends PostgreSQL/PostGIS to index millions of STAC Items. Deploy the database on Amazon RDS for managed backups and high availability. Apply the pgSTAC plugin, create collections that reflect sensor families, and load item metadata during ingestion. This approach yields fast spatial, temporal, and attribute queries. ### Tile Services TiPG and TiTiler For vector visualization, TiPG serves Mapbox Vector Tiles directly from PostGIS, removing the need for a separate tile server. Raster tiles are delivered via TiTiler, which reads COGs on demand and can generate custom composites such as NDVI. Both services expose standard WMTS/XYZ endpoints, making them compatible with common web‑map libraries. ### Orchestration on Amazon EKS and Autoscaling with KEDA Containerize all services and deploy them to an Amazon EKS cluster. Use Kubernetes Event‑Driven Autoscaling (KEDA) to adjust pod counts based on queue depth or request latency. This ensures the platform can handle peak ingestion periods while keeping idle resources minimal. ### API Gateway and Security Expose the platform through Amazon API Gateway, which provides a single entry point for web and mobile clients. Configure JWT validation, rate limiting, and request routing to the appropriate micro‑services. This layer also simplifies CORS handling and audit logging. ### Ingestion Pipeline Build a decoupled ingestion component that pulls imagery from satellite providers, validates files, extracts metadata, and writes both to S3 and pgSTAC. Use Amazon SQS to queue new acquisitions and trigger Lambda functions or container jobs that perform quality checks such as cloud detection. The pipeline can be extended to accept UAV data and prescription maps with minimal changes. ### Monitoring and Cost Optimization Collect metrics with Amazon CloudWatch and visualize them in Grafana dashboards. Set alerts for storage growth, database CPU, and pod scaling events. Periodically review S3 storage classes and enable Intelligent‑Tiering to shift infrequently accessed assets to lower‑cost tiers. Key Takeaways - Standardized metadata via STAC reduces search latency and simplifies cross‑system integration. - Serverless‑friendly formats like COGs and FlatGeobuf enable on‑the‑fly rendering without heavy compute. - Event‑driven scaling with KEDA matches resources to workload, cutting operational spend. - Managed services (RDS, API Gateway) offload routine maintenance, letting teams focus on domain logic. For additional guidance on web‑based data delivery, see the Web Interoperability 2024 guide. Performance tuning techniques for client‑side rendering are covered in the Page Visibility API article.