A set of generic techniques and principles to design a sturdy, cost-efficient, and scalable data model to your post-modern data stack.
Over the past few years, because the Modern Data Stack (MDS) introduced latest patterns and standards for moving, transforming, and interacting with data, dimensional data modeling regularly became a relic of the past. As a substitute, data teams relied on One-Big-Tables (OBT) and stacking layer upon layer of dbt models to tackle latest use cases. Nevertheless, these approaches led to unlucky situations through which data teams became a value center with unscalable processes. So, as we enter a “post-modern” data stack era, defined by the pursuit to scale back costs, tidy up data platforms, and limit model sprawl, data modeling is witnessing a resurrection.
This transition puts data teams in front of a dilemma: should we revert back to strict data modeling approaches that were defined a long time ago for a very different data ecosystem, or can we introduce latest principles which can be defined based on today’s technology and business problems?
I imagine that, for many corporations, the proper answer lies somewhere in the center. In this text, I’ll discuss a set of knowledge modeling standards to maneuver away from…
