Paul Andrew continues a series on applying data mesh principles in Azure:
This principal is very broad, so I want to break down the theory vs practice as before. The idea of self-service is always a goal in any data platform and the normal thing for analytics is to focus on this within the context of our data consumption. Whereby a semantic layer technology can be used in a friendly business orientated, drag-drop type environment to create dashboards or whatever.
However, my interpretation of ‘self-serve’ for a data mesh architecture goes further than just the dashboard creation use case. This should not just apply at the data consumption layer, but all layers within the solution and for clarify, not just related to the data itself. Hence the term in this principal ‘data infrastructure as a platform’. This then unlocks the deeper implication of this serving for a data product, all abstracts of the platform can be consumed in a self-service manner from a series of predefined assets. Let’s think about this serving more like an internal marketplace or catalogue of assets for delivering everything the data product needs to enable a new node within the wider data mesh.
Read on for some deep thoughts on the topic.