Geotribu, a community dedicated to “free and open geomatics”, has an interesting two-part article series (in French) by Michaël Galien giving an example of using the MDS1 in the geospatial domain. The example centers around Mapillary2 data:
The first article treats extracting data from Mapillary and loading it into a data warehouse.
The second article explains transforming and augmenting the data along a medallion architecture3 for use within QGIS.
The stack shown involves dbt, Airflow, PostgreSQL/PostGIS, SQL, and Python (but the articles also mention alternative technologies for building an MDS). And relevant data engineering tools and paradigms are touched upon, such as DAGs4, orchestration, lineage, data models, and model documentation.
Footnotes
“MDS” stands for “Modern Data Stack”, a term used to describe a set of tools and technologies for data integration, processing, and analysis. It typically includes components like ELT processes (extract, load, transform), data warehouses, data lakes, and analytics platforms.↩︎
Mapillary is a platform for street-level imagery and data collection. It was launched by Mapillary AB in Sweden in 2013. In 2020, it was acquired by Facebook, now Meta.↩︎
The medallion architecture is a data processing framework that posits three layers for data: bronze (raw data), silver (cleaned and transformed data), and gold (aggregated and ready for analysis).↩︎
Directed Acyclic Graphs↩︎