A set of generic techniques and principles to design a robust, cost-efficient, and scalable data model for your post-modern data stack.
Over the past few years, as the Modern Data Stack (MDS) introduced new patterns and standards for moving, transforming, and interacting with data, dimensional data modeling gradually became a relic of the past. In its place, data teams relied on One-Big-Tables (OBT) and stacking layer upon layer of dbt models to tackle new use cases. However, these approaches led to unfortunate situations in which data teams became a cost center with unscalable processes. So, as we enter a “post-modern” data stack era, defined by the pursuit to reduce 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 decades ago for a completely different data ecosystem, or can we introduce new principles that are defined based on today’s technology and business problems?
I believe that, for most companies, the right answer lies somewhere in the middle. In this article, I’ll discuss a set of data modeling standards to move away from…
