CNG Resources

In our daily work, we have collected experience and built expertise with respect to the cloud-native geospatial paradigm (or „cloud-native geo“ or „cng“). On this page we list some particularly interesting resources for people interested in cloud-native geo and its applications.

On this page, you’ll find:

Can’t find what you’re looking for? – Feel free to reach out with your questions!

Definition

We use the definition by the CNG Forum:

A cloud-native dataset is one with small addressable chunks via files, internal tiles, or both.

Broadly speaking, three elements combine to make cloud-native geo possible:

  • Data is divided into small, adressable components, e.g., chunks, tiles, or row groups.
  • Metadata is embedded into the data file and easily accessible.
  • File contents can be queried partially, thanks to HTTP range requests.

Interested in geospatial topics?

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Resources

Cloud-Native Geospatial: Was es kann, was nicht, und wann man es wirklich braucht

EBP’s balanced (if we dare say so) introduction to the cloud-native geospatial paradigm. This article explains the difference between merely storing geodata in the cloud and arranging data so that clients can retrieve addressable blocks through HTTP range requests. It covers Cloud-Optimized GeoTIFF or COG, FlatGeoBuf, GeoParquet and PMTiles and also discusses where traditional databases such as PostgreSQL and PostGIS remain the better choice.

Read this introduction

Cloud Optimized Geospatial Formats: Status Report

A Swiss whitepaper-style introduction published by OPENGIS.ch as supporting material for the Swiss Geoinformation Strategy. It examines various cloud-optimised formats and briefly touches upon the technical underpinnings. This report dates from March 2024, so in parts it is not up-to-date anymore (especially regarding format versions and standardisation efforts).

Read the status report (pdf)

Cloud-Optimized Geospatial Formats Guide

A comprehensive general format guide by the CNG Forum. It covers COG, Zarr, Cloud-optimized HDF/NetCDF, COPC, GeoParquet, FlatGeobuf and PMTiles, together with a glossary, „cookbooks“ and questions to consider when producing datasets.

Read the formats guide

Cloud-Native Geospatial in der Praxis: InSAR-Daten auf der Naturgefahrenplattform GIN

A concrete Swiss implementation by EBP on behalf of the Federal Office for the Environment FOEN. In this, we describe how COG, PMTiles, Parquet and DuckDB were used to integrate large InSAR (interferometric SAR) datasets into the Swiss natural-hazards platform GIN, including technical choices, encountered difficulties and take-home messages for practicioners. This serves as part 2 to the introduction at the beginning of this list.

Read the case study

Best Practices for Cloud-Optimized GeoTIFFs in Swiss Geospatial Infrastructures

A GitHub-hosted resource open to the community that covers Swiss implementations of Cloud-optimized GeoTIFFs. It provides a decision tree for viewing versus analysing data, raster types and compression choices, complete with GDAL-based example workflows.

Explore the Swiss COG best-practice repository

OGC Best Practices for Distributing GeoParquet

As part of its GeoParquet format repository, the OGC has published this guide to best practices for distributing data in GeoParquet forma. The goal behind the guide is offering more clarity how to optimally distribute GeoParquet files, given the many options of various libraries and tools working with Parquet files (that may not be ideal for GeoParquet handling).

Read the OGC best practice guide

Dynamische Karte mit PMTiles und Protomaps erstellen

A practical example of creating a web map of European one-way streets from OpenStreetMap (OSM) data using PMTiles and Protomaps. The article briefly introduces the PMTiles format and then describes a workflow using Overpass and tippecanoe as well as the webmap frontend based on Protomaps (for the basemap) and MapLibre.

Read the PMTiles and Protomaps tutorial

Cloudnative Geospatial: DIY Höhenlinien aus der Cloud, Part 1 & Part 2

The first part introduces the data and design considerations behind making derived contour data available in a cloud-native web-map workflow. The technical follow-up in part 2 covers producing and publishing cloud-native contour data with Protomaps and MapLibre GL JS.

Read Part 1 of the cloud-native contour series

Read Part 2 of the cloud-native contour series

Access SwissEO in QGIS

A swisstopo tutorial showing how to locate SwissEO products through the federal STAC browser and stream a COG directly into QGIS rather than downloading the complete raster.

Open the swisstopo QGIS COG tutorial

All articles on „cloud-native“ by EBP

This is a navigational link to the complete EBP collection on cloud-native. It includes some of the articles above and more (e.g., on hosting cloud-native data), but also some content on cloud-native data in general, not specific to the geospatial domain.

Browse all cloud-native articles on digital.ebp.ch

Resources curated by Ralph Straumann for Spatialists

On his private geospatial news blog, Spatialists.ch, Ralph Straumann has published several pointers to interesting cloud-native related case studies, tools and technologies, best practice guides, and more. We hope these links help you discover further interesting resources and to delve deeper in some areas:

General

Guides

Tools

Technical background

Other

Standards and specifications

Not cloud-native data formats, but relevant in conjunction with cloud-native data and access patterns:

tl;dr – Give me quick pointers

The following is the most concise format pointers to cloud-native geospatial formats we can offer:

  • Rasters and image pyramids: Look into COG.
  • Large vector analytics: Look into GeoParquet.
  • Pre-generated web-map tiles: Look into PMTiles.
  • Large vector streaming, spatially subsetting features: Look into FlatGeobuf.
  • Point-clouds: Look into COPC.
  • Multi-dimensional arrays and data cubes: Look into Zarr, Geozarr and maybe Cloud-Optimized NetCDF4 / HDF5 (the last two primarily if you already use NetCDF and HDF).
  • Spatio-temporal discovery and cataloguing of cloud-native files: Look into STAC.
  • Frequent updates, transactions, complex permissions or highly dynamic queries: Also evaluate a database or service-based architecture.

Still unsure where to start? –
Feel free to contact us at any time!

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