DBT Project Structure: Building Blocks of a Well-Organized Data Transformation Pipeline

This is the second post in my DBT blog series. If you missed the first part introducing DBT and its role in modern data workflows, you can find it here.

After understanding what DBT is and why it's revolutionary for data transformation workflows, it's essential to dive into how DBT projects are structured. A well-organized project structure is crucial for maintainability, scalability, and collaboration among data team members.

The Six Core Components of a DBT Project Structure

DBT projects follow a logical progression that transforms raw data into business-ready insights. Let's explore the six main building blocks that form the backbone of any well-structured DBT project:

1. Sources

Sources represent raw data tables in your warehouse that DBT references as input data. These are typically CSV files you load into your warehouse through extract and load tools. Sources serve as the foundation upon which all your transformations are built.

2. Seeds

Seeds are CSV files that you load directly through DBT, typically used for small lookup/reference tables that change infrequently. These might include country codes, currency conversion rates, or product categories that enhance your core data.

3. Snapshots

Snapshots are special models that track historical changes in your source data by implementing slowly changing dimensions Type 2. They ensure you maintain historical context even when source systems overwrite data, making them vital for accurate point-in-time analysis.

4. Staging Models

Staging models form the first layer of transformation, cleaning and standardizing source data in a one-to-one relationship with source tables. They create a consistent foundation for downstream transformations and isolate your models from changes in source systems.

5. Tests

Tests are assertions you write to validate your data models, ensuring referential integrity, uniqueness, null checks, and business logic. They act as safeguards that help maintain data quality throughout your transformation pipeline.

6. Marts

Marts are business-oriented models that combine & transform staging models into analytics-ready datasets. These are the final data products organized by business domain that your BI tools and business users will access.

Defining Sources in Your Project

Sources in DBT are essentially raw data tables referenced as input data for your transformations. They create a clear separation between imported data and your transformations, making it easier to manage changes and dependencies.

Key Aspects of Working with Sources:

1. Naming & Description: Sources describe the data loaded into your warehouse by extract & load tools. Proper naming and documentation here set the tone for your entire project.

2. Declaration in YAML: Sources are declared in a .yml file under the sources key, which helps DBT understand where to find your input data.

3. Reference Function: Use the {{ source() }} function to select a source referenced from a model:

   • DBT compiles it to the full table name

   • DBT creates a dependency between the model & source table

4. Testing Source Data: Adding data tests to sources helps catch data quality issues early in your pipeline, preventing them from propagating through your transformations.

Best Practices for Source Management

When working with sources in your DBT project, consider these best practices:

1. Centralize source definitions: Keep all source definitions in a dedicated YAML file for easier management.

2. Document thoroughly: Add descriptions to all sources and columns to build a knowledge base about your data.

3. Test at the source level: Implement tests on sources to catch issues early in your transformation pipeline.

4. Use source freshness checks: Leverage DBT's ability to check when source data was last updated to ensure you're working with current information.

5. Maintain consistent naming conventions: Establish clear patterns for how sources are named in relation to their staging models.

Bringing It All Together

The DBT project structure flows logically from raw data (sources and seeds) through various transformation stages (snapshots and staging) to final business-ready outputs (marts), with tests ensuring quality throughout. This organized approach creates a clear, maintainable transformation workflow that scales with your organization's needs.

When building your DBT project, start by properly defining your sources, then build your staging models to clean and standardize them. From there, create intermediate models as needed, and finally develop your marts organized by business domain. Throughout this process, add tests to ensure data quality and documentation to enhance knowledge sharing.

In the next post of this series, we'll explore how to effectively create and manage transformations in DBT through the powerful concepts of models and materializations.