GIS data lake
The GIS data lake is the foundational storage layer for the platform's GIS pipeline — a flat-file store that holds raw geospatial points ingested from open sources (OpenStreetMap, Overture Maps Foundation) in separate retail and civic landing zones, available immediately to every downstream step in the same pipeline without an ETL step. Retail records — commercial operators, anchor stores, fuel outlets — and civic records — hospitals, universities, transport hubs — are kept in distinct subtrees so the filtering and clustering steps can work on each domain independently. This data lake is a distinct component from service-fs — the WORM ledger backbone, the platform's separate per-tenant WORM ledger — the two share no code and no storage format.
Key Takeaways
- Two separate landing zones — retail and civic — hold raw points from OpenStreetMap and Overture Maps Foundation. Downstream steps read directly from the landing zones; no ETL transformation step sits between ingestion and consumption.
- Data persistence is decoupled from analytical logic. If GIS orchestration application is reprovisioned, the raw data assets in the data lake remain intact and are immediately available to any replacement analytical layer.
- Today the landing zones are plain directories on the host filesystem, populated and read directly by the GIS pipeline's own ingestion and analysis scripts. There is no dedicated storage service, no restricted API, and no unikernel envelope in front of them. The business-clustering and places-filtering steps that read this data run as steps inside the same Python-based pipeline documented in GIS orchestration application, not as separate crates or services reading through a boundary.
- The flat-file, open-format design avoids proprietary format lock-in. Raw geospatial records are stored as plain files readable by any toolchain in any decade.
Data Ingestion and Storage
The pipeline maintains a unified filesystem structure with separate landing zones for retail and civic infrastructure data.
- Retail landing: raw commercial operator records ingested from open geospatial registries (OpenStreetMap, Overture Maps Foundation).
- Civic landing: raw civic and institutional facility records from the same open sources.
Architectural role
As the stateful layer of the GIS pipeline, the data lake is responsible for data persistence, kept independent of the analytical code that reads it — if the GIS orchestration layer is re-provisioned, the core data assets remain intact within this layer. The clean separation between data persistence and analytical logic is a core design invariant of this pipeline. It is a separate design from the platform's WORM ledger (service-fs — the WORM ledger backbone), which anchors institutional records for compliance rather than storing raw geospatial points — the two are not layers of one shared four-layer system.
What this is not
There is no dedicated storage service or restricted API in front of these landing zones today — they are plain host-filesystem directories, read and written directly by the GIS ingestion and analysis scripts with ordinary file I/O. No service-business or service-places crate exists in the codebase; the business-clustering and places-filtering steps are Python steps inside GIS orchestration application's own pipeline, not separately deployed services with their own storage boundary. The retail co-location tier methodology describes how the clustering output is used to generate tier rankings.
See also
- Business clustering
- Places filtering
- GIS orchestration application
- service-fs — the WORM ledger backbone — the platform's separate WORM ledger; a distinct component from this data lake