Organizing Your dbt Projects and Data Model Setup
In the world of data-driven decision making, efficient and effective management of data models is paramount. One tool that's making waves in this realm is dbt, an open-source data management solution.
In a dbt project, data models are structured into three main layers: staging, intermediate, and mart models, each serving distinct purposes and arranged in a logical workflow.
Staging models prepare atomic building blocks by cleaning and standardizing raw source data. They handle basic transformations like renaming columns, filtering rows, and applying consistent naming conventions and primary keys to reflect the source tables as closely as possible without complex logic. Staging models typically output one-to-one or simple transformations of source tables.
Intermediate models act as purpose-built transformations that break down complexity from mart models. They simplify the structure by joining a manageable number of staging or other intermediate models before producing marts, re-grain data, and isolate complex or hard-to-understand logic to improve overall model readability and modularity.
Mart models are the final layer that aggregates, combines, or shapes data specifically to meet business or analytical requirements. They typically join intermediate models to produce clean, final datasets used for reporting or analytics.
A typical dbt project layout using these layers reflects this workflow. In the directory, you'll find sub-directories for each data model type:
Additional style and naming conventions include:
- Models and tables should be pluralized and use underscores for naming (e.g., , ), avoiding dots.
- Each model should have a primary key, ideally named consistently like for clarity and easier joins downstream.
- Keeping the project structure clear supports configuration at directory level and smooth collaboration.
dbt, a development framework that combines modular SQL with software engineering best practices, helps create datasets and models used for analyses, reporting, Machine Learning modeling, and data workflows. By adopting this layered approach, organizations can ensure modularity, testing, clear data lineage, and ease of maintenance in their dbt projects. It also improves readability by isolating transformation complexity, while ensuring marts are efficient and focused on business logic.
In the example provided, business groups named "Retail" and "Finance" have sub-directories created for them to house their respective data models. Intermediate models need to follow a specific naming convention, while mart models should be named after the entity they represent. Duplicate entities should be avoided across multiple different business units.
In the context of a dbt project, data modeling layout is designed with distinct layers: staging, intermediate, and mart models. Staging models act as foundational entities, preparing atomic data blocks through basic transformations, while intermediate models tackle complexity, and mart models aggregated data according to business or analytical needs.
Incorporating this layered approach in a dbt project can lead to benefits such as improved modularity, testing, clear data lineage, and ease of maintenance. This organisation is particularly useful in projects related to data-driven analyses, reporting, machine learning modeling, and data workflows.
