A set of generic techniques and principles for designing a robust, cost-effective, and scalable data model for your postmodern 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. Instead, data teams relied on One-Big-Tables (OBT) and stacking layer upon layer of dbt models to address new use cases. However, these approaches led to unfortunate situations where data teams became a cost center with unscalable processes. Thus, as we enter a “post-modern” data stack era, defined by the quest to reduce costs, declutter data platforms, and limit model proliferation, data modeling is witnessing a resurrection.
This transition places data teams in a dilemma: should we return to strict data modeling approaches that were defined decades ago for a completely different data ecosystem, or can we introduce new principles 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 will discuss a set of data modeling standards to move away from the traditional ones.
