Components
A NodeGraph configuration is centered around four important building blocks that must be understood to use it properly and to be able to set up a good adaption in an environment. These four blocks are: Containers, Connectors, Rules, and Categories.
The different components of NodeGraph.
Containers
Containers are the highest organizational unit inside NodeGraph. This is the box in which you decide what to show and link together, and what will be placed inside is situation dependent.
In smaller NodeGraph setups, there might be only one or two containers with a Prod and Dev environment. In larger adaptions, it might be of relevance to separate the raw Source Databases and SSIS, and the DW and consumer end of things. If multiple BI tools are used, it could be relevant to split these into separate containers.
The information could also be split in containers based on security reasons. It is only on container levels that security can be applied. While it is not necessary to have clear insight into this in the beginning, knowing the rough outlines of what separation of users might be relevant down the road helps create a better configuration from the start.
Connectors
Connectors are tools purpose-built around a specific technology that are designed to extract metadata and resources referred inside these technologies that might be relevant on the larger map. These are pipes through which NodeGraph can be populated with information and each connector has their own configuration depending on their purposes.
Rules
A connector can have one or more rules. While the connector is responsible for the overall communication with the technology they target, the rules dictate what is extracted from those systems and how they should be visualized.
Categories
When each rule is executed, each connector encounters different types of data. These data nodes can be targeted into different categories to assist in organizing and understanding how each node relates to one another.
Think of categories as buckets; they have in themselves no information, but through them, you can visualize the general idea of where data exists in the environment and how they relate.