Snowflake with dbt — Accelerating Data Teams
Modern businesses need modern data strategies built on platforms that support agility, growth, and operational efficiency. Using dbt with Snowflake, data teams can quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation.
The power of Snowflake’s cutting-edge platform and the seamless integration with dbt that elevate data pipeline development and administration, tackle complex data challenges and build data assets at scale.
How dbt works
Develop — Write modular data transformations in .sql or .py files. dbt handles the chore of dependency management.
Test and Document — Test every model prior to production, and share dynamically generated documentation with all data stakeholders.
Version Control and CI/CD — Deploy safely using dev environments. Git-enabled version control enables collaboration and a return to previous states.
Why use dbt along with Snowflake
- Easily maintain separate environments (like production and development) and snowflake layers (like raw and analytics) with fine-grained permissions, mitigating the impact of errors and reducing data downtime.
- Automatic data lineage establishment by easy declaration of sources and maintenance of references to depending models using ref() and source() functions in every dbt mode. This becomes very powerful as a project grows in size to include hundreds of models, which is conveniently referenced through visual graph (DAG).
- Easy assertion of generic tests like unique, not null and relationships (referential integrity) in dbt, without which additional process need to be build to check for constraints that Snowflake doesn’t enforce. Additionally in dbt we can establish accepted_values test and singular tests.
- Effective data discovery via documentation that is generated with information of the project code, tests added on columns, column data types and data lineage. dbt also provides a way to add descriptions to models, columns and sources to further enhance the documentation.
Key outcomes promoted by dbt’s development framework
Collaboration — dbt code is self-documenting; everyone can work together
Velocity — Focus on analytics, not infrastructure & ship data products faster
Quality — Test and work from the same assumptions to ensure alignment
Governance — Standardize process & control access to simplify compliance
Get started with Snowflake & dbt with hands on
- https://quickstarts.snowflake.com/guide/accelerating_data_teams_with_snowflake_and_dbt_cloud_hands_on_lab/
- https://docs.getdbt.com/quickstarts/snowflake
- https://quickstarts.snowflake.com/guide/data_teams_with_dbt_core/
- https://quickstarts.snowflake.com/guide/data_engineering_with_snowpark_python_and_dbt/
Follow and Clap if you like the content and feel free to ask if you have any questions in the comments. I will be more than happy to assist and guide you.
