Data modeling concepts
Core data modeling concepts
This illustration shows how the data modeling building blocks in Cognite Data Fusion (CDF) relate to each other.
A space can contain both schemas and instances, and is an efficient resource to organize your graph. It functions as a namespace, and lets you choose identifiers without interference from other spaces.
For access management, you can use CDF user groups and capabilities to define who has access to read from and write to a space. You can, for example, create a data model in a protected space to prevent it from being changed, and then create instances in a space where users can read and write to the data.
To delete a space, you have to remove all schema resources assigned to it first.
Nodes can represent anything, for example, real-world objects like pumps,
Edges describe relationships between nodes.
Every instance has an external ID that must be unique for that space.
Containers are the physical storage for properties. They're defined within a space, and hold a set of properties that logically belong together.
You must define types for your properties, and you can add optional constraints that the data in the container must adhere to. You can also define indexes for properties, or groups of properties, to optimize query performance.
Use views to create logical schemas to consume from, or populate, a graph. The views should be tailored to meet the needs of specific use cases. Like containers, views contain a group of properties. You define views by either mapping container properties, or by creating connection properties to express the expected relationships in the graph.
Use data models to group views. For example, you can define a
EquipmentInspection data model containing a
BasicValve, and a