What are the best ways to manage data modeling dependencies?

Leaving aside basics - define document name track etc. which are 101. The world is complex today with structured, unstructured, semi-structured -the age of simple data models are long gone and so one needs to have good knowledge across the spectrum of data that is often silo-ed. You cannot design modular models, one around structured, another around unstructured etc. Visualization of these data models and the lineage around them are provided by tools. Data Model automation has come a long way What is important is that the relationships established across the model entities and the connections between the data points, reflect the business correlation of those entities and how it reflects in the real world.

You can use version control systems to track and manage the different versions of the data model. A central data model repository or to use a model governance framework. The prevailing approaches encompass employing a centralized repository for data models or adopting a framework for model governance. The former entails consolidating all data models within a solitary location, thereby facilitating the monitoring and administration of interdependencies among said models. Conversely, the latter entails establishing a set of regulations and procedures to oversee data model management, thereby upholding uniformity throughout the organization.

Data lineage is like a data storytelling tool, revealing the journey data takes from origin to purpose through transformations and validations. This history provides a deep understanding of data quality and its impact on various models and components. It also unravels the intricate dependencies between them, crucial for data engineers and stakeholders. Whether documented manually or with specialized tools, data lineage is the guiding thread that empowers data management, troubleshooting, and optimization, making data a comprehensible and actionable asset.

I could not emphasize more about data lineage. This one is the very first thing that any stakeholder checks if their dashboard couldn't load. It seems simple, yet implementing data lineage could take ages for the data team to build. To build data lineage, - You SHOULD NOT define it manually, because it'll likely be very prune to error. - Take advantage of dbt or other data modelling library to preserve the logic of data lineage - If you have to do it manually, try to parse the build up SQL table to extract its upstream tables. You can try SQL parsing libraries like sqlglot or sql-lineage

Modular design is a very helpful method to minimize Data modeling dependencies. For instance, the Kimball Architecture has a provision for the Supertype and Subtype schemas for heterogeneous products. This is used when there is high variance in the entity being modeled. Some businesses have a wide variety of products with their own features that may not be compatible with the other products. Modeling them as a single entity may prove to be ineffective & complex. Think about a home loan application and a vehicle loan application. While they are both loan applications, they have very different features. These detailed features get modeled as subtype tables and the common features among the subtypes can be combined into a supertype table.