chat.md

Question	Answer
11. What are dbt seeds, and how do you use them?	dbt seeds are CSV files stored in your dbt project that can be loaded into your data warehouse as tables. You use the dbt seed command to populate the warehouse with this data.
12. Explain incremental models in dbt and when to use them.	Incremental models only update new or changed data since the last run. They are used to handle large datasets efficiently, reducing load times and compute costs.
13. What is the difference between ephemeral models and views in dbt?	Ephemeral models do not create physical tables or views in the database; instead, they exist only as CTEs (Common Table Expressions) within other models. Views are stored as actual database views.
14. How do you handle dependencies between dbt projects?	Dependencies between dbt projects can be managed using packages.yml to install other dbt projects as packages, allowing shared logic and code reuse.
15. How do you handle changing data sources in dbt?	dbt allows you to use variables and source definitions in schema.yml files to reference different data sources. This enables you to easily switch data sources without changing your SQL code.
16. What is the role of schema.yml files in dbt?	schema.yml files define tests, documentation, and relationships for your models. They help maintain data quality and provide metadata for your data warehouse.
17. Can you explain dbt’s integration with data warehouses like Snowflake?	dbt connects to data warehouses like Snowflake using a configuration in profiles.yml. It uses native SQL and features of the warehouse to perform transformations and manage data efficiently.
18. How do you set up a dbt project from scratch?	To set up a dbt project, initialize it with dbt init, configure the profiles.yml file with your data warehouse credentials, create models, and run transformations using dbt run.
19. How do you manage different environments (e.g., dev, prod) in dbt?	dbt manages different environments using different configurations in profiles.yml or by setting environment variables that switch between development, testing, and production datasets.
20. How does dbt handle logging and debugging?	dbt generates logs for each run in the logs/ directory. You can use the logs to debug issues, monitor performance, and understand the order of execution for models.