Data Management

Spatial and temporal resolutions of weather, environmental, and geodata are constantly growing — and so is the data volume. Large databases are at the heart of our data services and web applications. To attain flexible querying and low latency at the same time, special know-how and technologies are required. We're using so-called spatio-temporal time series databases, for both point and raster data:

• Point data typically originates from measurement stations or sensors and is stored in SQL databases. Raster data originates from numerical weather prediction models, weather satellites/radar, or from interpolated point data.
• Naturally, raster data volumes are quite big. The technology required to store and query such large amounts of data is therefore much more complex than for point data. Our raster databases are based on the “data cube” model and on open source technologies. The standard data model is three-dimensional (see figure), but additional dimensions such as “height” are possible, too.

Applications: APIs, apps, data science

Our point and raster databases are the foundation for most of our digital products and services like data APIs, visualizations such as maps and charts, or data analysis.

Reponse times are low even for data sets that consist of terabytes of data.

Are you looking for a solution to store and query your own weather, environmental, or geodata? We can help you with concepts, implementation and operations.