15 artikelen in dit onderwerp.
Every paid dataset ships a free sample (where one is available) so you can validate schema, coverage and freshness before you buy a single license. Sample size depends on the dataset.
Discovery is the entry point to everything else. Learn to page, sort and filter the public dataset catalogue and find the stable table identifier every other endpoint needs.
A dataset page is the authoritative truth surface: description, quality grade, per-field profile, sample links and refresh cadence. Learn what each part tells you before you spend a credit.
Samples are free and unlimited to call; full exports cost credits. Use samples to validate schema, coordinate ordering and real values throughout development before you commit to a paid pull.
Listing what a dataset offers is free; downloading full exports, coordinates or historical archives consumes credits and needs an active license for the dataset. Here is the exact flow, cost and format for each.
CSV, JSON, GeoJSON, KML and SHP each suit a different tool. This is what every format contains, how geometry is carried, and which are available as free samples versus paid exports.
These two concepts get confused. A dataset is the data artifact; a product is the commerce wrapper that sells it. Understanding the split makes the whole platform click into place.
Datasets refresh on a published cadence. Until push-based refresh hooks ship, you detect updates by polling two free fields. Here is exactly how, and what the planned webhook will look like.
Send a single read-only SELECT — full PostGIS toolkit included — to an HTTP endpoint and get JSON rows back. It is an API for your code, not a database connection, and it is Enterprise-only.
Coordinates, full exports and spatial querying need an active license and credits. This maps each gated capability to what it requires, and explains the WGS84 reference system.
Not every dataset is for sale in the shop. Some are B2B-only: they exist, have full metadata and a dataset page, but have no product to check out. Here is how to recognise and access them.
Data Studio is the in-app pipeline builder. A project is its unit of work — an organization-scoped workspace holding your datasets and the configuration over them. Here is how to create and manage one.
Layers are the datasets you bring into a project workspace. There is no separate add-layer route — layers live inside the project configuration you write back with a single PUT. Here is the pattern.
A pipeline is an ordered set of processing steps over your layers that turns the datasets you added into the dataset you want. Preview it as a dry run first, then execute to materialise a result.
When a pipeline runs it produces an output shaped by your project's output configuration. Set the format and field mapping, execute, then download the generated result. Here is the full flow.
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