Deker™

OpenWeather are thrilled to announce the launch of the Deker™ platform where you can effortlessly obtain free weather data to suit your specific requirements.

Deker™ empowers users to store and access a wide range of data types, virtually anything that can be represented as arrays, like geospatial data, satellite images, machine learning models, sensors data, graphs, key-value pairs, tabular data, and more. That's a simple and comfortable alternative to complex API responses for getting access to OpenWeather data, all in one place.

Open source under GPL 3.0

Scalable storage of huge virtual arrays via tiling
Parallel processing of virtual array tiles
Own locking mechanism enabling arrays parallel read and write
Array level metadata attributes
Fancy data slicing using timestamps and named labels
Support for industry standard NumPy and Xarray
Storage level data compression and chunking (via HDF5)

Security

Deker™ library is inherently secure to be used in your internal projects as its source code is public and available for audit, and also regularly scanned for dependency vulnerabilities using GitHub security audit mechanisms.

Naturally, weather data is a multi-dimensional array, where each dimensional is a geo coordinates, time and weather parameters such as temperature, precipitation, wind direction and speed, etc.

Why Deker™ is a useful instrument for getting weather data:

Data Slicing Made Easy: Deker™ simplifies the process of selecting specific slices of weather data.
Seamless Integration with Python: You can seamlessly incorporate Deker™ into your Python codebase without the need to decipher complex API responses. This immediate accessibility streamlines the data utilization process, saving valuable time and effort.
Flexible Data Formats: Deker™ offers the convenience of accessing data in multiple formats, including nparray and Xarray objects.