The Open Data Cube Ecosystem#
The Open Data Cube (ODC) software ecosystem is made up of a number of Python packages, which are described in ODC Software Packages.
Importantly, you may need different parts of the ecosystem depending on your use case. To help you identify how you might best use the Open Data Cube, consider whether you want to manage data, process data, or both!
Managing Data#
In the ODC ecosystem, managing means curating and organising data so that people can find the data they want. The ODC uses metadata to organise the data. The metadata includes the data’s spatial and temporal information, creation, lineage, and versioning.
This aspect of the ODC is for data providers and holders, be they large organisations with their own satellites or individuals with bespoke collections of Earth observation data.
To read more about managing data, see Architectural Overview.
Working With Data#
In the ODC ecosystem, working with data could be displaying a true-colour image by combining the red, green, and blue bands from a multispectral sensor, or producing a land cover dataset by applying a machine learning algorithm to multiple Earth observation datasets. Data can be used and processed at any scale, from small regions to entire continents.
This aspect of the ODC is for those who want to work with the data. This includes analysts and scientists.
To read more about working with data, see Accessing Data.
ODC Software Packages#
The ODC has numerous Python packages that provide additional functionality, such as web services and large scale processing. To read more about the packages, see ODC Software Packages, which provides a description of each package and relevant links to their repositories.
Existing Deployments#
Many countries and organisations run their own Open Data Cubes, meaning you may be able to access data without needing to set up your own. To see a list of existing deployments, see Existing Deployments.