Two months ago, we introduced Data Graph in Cube Cloud as a human-friendly, visual way to explore contents of the semantic layer.
Data Graph aims to represent the data model as an entity relationship diagram and it's the best way to take a bird’s-eye view of the data model and familiarize oneself with the contents of a semantic layer. Initially, Data Graph only visualized cubes, allowing data engineers to explore join relationships and find clusters of interconnected cubes.
Today, we're proudly adding the visualization of views to Data Graph.
Views in Data Graph
Views sit on top of the graph of cubes and create a facade of your whole data model with which data consumers can interact. Views are useful for defining metrics, managing governance and data access, and controlling ambiguous join paths.
We recommend that all cubes should be made non-public (thus, not exposed to data consumers); instead, views should be defined on top of cubes and exposed to BIs, data applications, etc.
Now, data engineers working with the data model of the semantic layer can define views with the code-first approach using YAML syntax and use Data Graph to explore the end result: review join paths between cubes referenced in a view, see the lineage of view members, and check their types and visibility.
What's next?
We've got some positive and insightful feedback from early Data Graph users. Still, we’d like to hear more of your feedback to ensure that Data Graph is indeed the ultimate way to explore the full data model of the semantic layer.
Data Graph is available in Cube Cloud on all tiers, and you can try it today. Check that your deployment uses version 0.32.30 or above and give Data Graph a spin. Also, please join our Slack community at slack.cube.dev to share your thoughts and opinions about Data Graph.