chat.md

Question	Answer
21. What are dbt exposures, and why are they useful?	dbt exposures link data models to the end-user's BI tools or reports, providing visibility into how the data is used and ensuring that transformations are aligned with business goals.
22. How do you handle schema changes in dbt?	Schema changes are managed by updating the SQL models and schema.yml files. For complex changes, you can use versioning or migration scripts to avoid breaking downstream dependencies.
23. What are the best practices for organizing a dbt project?	Best practices include using clear folder structures for staging and final models, writing modular and reusable SQL, implementing thorough tests, and documenting all transformations and models.
24. How do you version control dbt projects?	dbt projects are version-controlled using Git. You should use branches for feature development, make use of pull requests for code reviews, and tag releases for production deployments.
25. What is the dbt Cloud, and how is it different from dbt Core?	dbt Cloud is a managed service for dbt that offers additional features like a web-based IDE, job scheduling, and CI/CD integrations. dbt Core is the open-source, command-line version.
26. How do you schedule dbt jobs?	In dbt Cloud, you can schedule jobs using the built-in scheduler. For dbt Core, you can use external schedulers like Airflow, Cron, or other orchestration tools to automate dbt run and dbt test.
27. How do you handle model dependencies in large dbt projects?	In large dbt projects, model dependencies are managed using ref() function, which allows you to reference other models and ensures they are built in the correct order based on the DAG structure.
28. How do you ensure that dbt transformations are performant?	To ensure performant transformations, optimize SQL queries, use incremental models for large datasets, choose appropriate materializations, and leverage the native performance features of the data warehouse.
29. What is the role of dbt packages, and how do you use them?	dbt packages are reusable collections of dbt models, macros, and tests. They can be installed via packages.yml and used to share logic across different dbt projects.
30. How do you handle errors and exceptions in dbt?	Errors in dbt are handled by reviewing logs and debug output. dbt also supports error handling using custom tests and conditional logic in macros to catch and manage issues in data transformation.