Managing data models at scale is a common challenge for data teams using dbt (data creation tool)In the beginning, teams often start with simple models that are easy to manage and implement. However, as The volume of data is growing and business needs evolvehe complexity Of these models increases.
This progression often leads to a monolithic repository where all the dependencies are intertwined, making it difficult for different teams to collaborate efficiently. To address this, data teams may find it beneficial to distribute their data models across multiple dbt projects. This approach not only promotes Better organization and modularity but also improves the scalability and maintainability of the entire data infrastructure.
One of the most significant complexities that arise when managing multiple dbt projects is how they are executed and deployed. Managing library dependencies becomes a critical concern, especially when different projects require different versions of dbt. While dbt Cloud offers a robust solution for scheduling and executing dbt projects across multiple repositories, it involves significant investments that not all organizations can afford or find the resources to manage.
