What are some project recommendations for dbt data teams?
For dbt data teams, it is recommended to use version control, maintain separate environments, facilitate multi-project collaboration, ensure unique code structure, establish coding conventions, and isolate source data. These practices can enhance productivity, improve code quality, and promote efficient collaboration among team members.
- Version control: Utilizing Git branches for managing new features and bug fixes, and reviewing code changes in a Pull Request before merging into the master can help maintain code integrity and prevent errors.
- Separate environments: Maintaining separate development and production environments using targets within a profile can help manage different stages of the project effectively.
- Multi-project collaboration: Allowing each team to work in their own domain-specific project and defining which models are publicly usable can promote efficient collaboration and reduce conflicts.
- Unique code structure: Ensuring that each developer has their own unique way of organizing and structuring code can foster creativity and individuality within the team.
- Coding conventions: Establishing coding conventions for the team to follow and using CTEs on top of .sql files when referencing source data can standardize coding practices and improve code readability.
What other recommendations can enhance the efficiency of dbt data teams?
Other recommendations include providing tailored project overviews, making it easy to onboard colleagues to collaborate in dbt Explorer, embedding and sharing views to align on trust signals and share data products, analyzing historical trends of model executions to identify opportunities to save data team time, and pinpointing data quality issues and tracing impacts.
- Tailored project overviews: Providing tailored project overviews can help team members understand the project better and align their tasks with the project goals.
- Easy onboarding: Making it easy for colleagues to collaborate in dbt Explorer can promote team collaboration and improve project outcomes.
- Embedding and sharing views: Embedding and sharing views can help align on trust signals and share data products, promoting transparency and collaboration.
- Historical trend analysis: Analyzing historical trends of model executions can help identify opportunities to save data team time and improve efficiency.
- Data quality issues: Pinpointing data quality issues and tracing impacts can help maintain the integrity of the data and improve the quality of the project outcomes.
