It uses a combination of SQL and a custom language, Jinja, which enables tons of macros that can simplify your SQL. This makes it easy for data engineers to adopt and design the various models in the environment. Once the models are all completed, jobs can be configured to schedule how frequently your models will be refreshed, and how they’ll be refreshed (i.e. as a full refresh, incremental, etc…).
dbt also has several features in it that help improve data governance so that your end users can have more trust in their data. For the data engineers, dbt’s version-control helps with collaboration and enables users to share work with their team. Moreover, dbt provides a robust testing framework that ensures the quality of your data and helps you catch errors before they impact your business (i.e. primary keys are unique, required fields are not null, historical data is accurate). Lastly, with dbt docs, all production code and all data lineage is made available to users, so end users have a working data dictionary.
In conclusion, if you’re looking for a powerful data transformation solution, look no further than dbt with Snowflake. By combining the two, you can create a scalable and reliable data pipeline that can help you make better business decisions.