Write every dbt model as a simple
SQL SELECT statement.
As you write SQL, use the
ref function to naturally create dependencies between your models.
dbt run to materialize models as tables or views in your warehouse.
Every day your business makes critical decisions based on data, and you need your entire organization to trust that data. dbt includes a robust testing framework so you can define and test assumptions about your data sources and the results of your data transformations. Learn more in docs
Over decades, software engineering has developed best practices that allow engineers to collaborate on code and integrate changes continuously. With dbt, you can apply these same practices to analytics code: environments, package management, and continuous integration.
Transparency builds trust. With dbt, you can create transparency throughout your analytics engineering workflow, giving analysts and business stakeholders visibility into what the data is describing, how it was produced, and how it maps to business logic.
A customer upgrades to a higher pricing tier. An invoice is paid. In cases like these, the historical data remains relevant and important, and you’ll want to save these past states for future analysis. With dbt snapshots, you can create slowly changing dimensions on your raw data to capture historical data points. Learn more in docs
Collaborate on and maintain code with ease by connecting dbt to your preferred version control software.
Troubleshoot issues in your analytics engineering workflow, no need to wait for an engineer to send you the log files.
For data to be useful, it must be trusted. Alerting ensures that you are the first to know about any potential errors.
SSO and permissioning allow you to manage the security of your analytics engineering workflow at a level that works for your team.