Managing data models at scale is a common challenge for data teams using dbt (data build tool). Initially, teams often start with simple models that are easy to manage and deploy. However, as the volume of data grows and business needs evolve, the complexity of these models increases.
This progression often leads to a monolithic repository where all 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 organisation and modularity but also enhances the scalability and maintainability of the entire data infrastructure.
One significant complexity introduced by handling multiple dbt projects is the way 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 multi-repo dbt projects, it comes with significant investments that not every organisation can afford or find…
