In the domain of data platforms, data management and data analysis, there is an ongoing discussion on the merits and drawbacks of the different Architectures. Proponents and detractors alike argue passionately for and against their preferred brand of labels on data.

But how different are they actually, and how do they all seek to achieve their goal of helping us manage data better? I have looked at some of the significant cloud-based data architectures out there and performed a comparative analysis of them. The architecture in Question is Data Lake, Medalion, AWS Modern data architecture, and Kimball Enterprise data warehouse.

In this talk, I summarise my findings, distil what I think the essence of these architecture boils down to, and discuss how we can see past the names and descriptions to start organising data.