chat.md

Question	Answer
1. What is dbt, and how does it work?	dbt (data build tool) is used for transforming data in a data warehouse. It works by enabling analysts and engineers to write transformations in SQL and execute them as part of a scheduled workflow.
2. Explain the difference between dbt models, seeds, and snapshots.	Models are SQL queries stored in files that transform raw data. Seeds are CSV files loaded into the data warehouse as tables. Snapshots capture data state at a point in time for historical analysis.
3. How do you test data quality in dbt?	Data quality in dbt is tested using built-in testing capabilities like unique, not null, and referential integrity tests. You define these tests in schema.yml files.
4. What are dbt materializations, and how do you use them?	Materializations in dbt define how a model is built in the warehouse (table, view, incremental, or ephemeral). You choose based on the use case, e.g., 'table' for large, frequently queried data.
5. Describe how dbt handles dependencies between models.	dbt uses the DAG (Directed Acyclic Graph) to manage dependencies. Models automatically build in the correct order based on their references.
6. What is the role of Jinja in dbt?	Jinja is a templating language that enables dynamic SQL generation in dbt. You use it to create reusable, parameterized SQL with variables and macros.
7. How do you document your dbt models?	You document models in dbt using schema.yml files, where you can describe each model, its columns, and the relationships. This documentation can be viewed in the dbt documentation site.
8. Can you explain the use of macros in dbt?	Macros are reusable SQL snippets defined using Jinja. You use macros to avoid repetitive SQL, making transformations more maintainable and efficient.
9. How do you handle sensitive information like credentials in dbt?	Sensitive information, like credentials, should be stored in environment variables or a secure credentials manager. dbt profiles.yml can reference these variables securely.
10. How do you deploy dbt projects in a CI/CD pipeline?	Deploying dbt in CI/CD involves automating dbt commands (e.g., dbt run, dbt test) using tools like GitHub Actions, Jenkins, or CircleCI, ensuring data transformations are tested and deployed systematically.