Top 30 DBT (Data Build Tool) Interview Questions and Answers
Beginner-Level DBT Interview Questions
1. What is DBT (Data Build Tool)?
DBT is an open-source tool that enables analysts and engineers to transform raw data in a data warehouse into a format ready for analysis. It focuses on SQL-based transformations and automates workflows for data modeling.
2. How does DBT work?
DBT uses SQL and Jinja (a templating language) to allow users to build modular, reusable data transformation pipelines. It executes SQL queries against a data warehouse and manages the creation of tables and views.
3. What are the key components of DBT?
Models: SQL files for data transformations.
Seeds: CSV files that are loaded into the warehouse.
Tests: Framework for data quality checks.
Snapshots: Track historical changes in data.
Documentation: Auto-generates data lineage and details.
4. What is a DBT model?
A DBT model is a single SQL file containing transformations. When run, DBT compiles the model into executable SQL and runs it on the data warehouse.
5. What is the purpose of the dbt run command?
The dbt run command executes all the models in a DBT project and updates the data warehouse with the transformed data.
6. What is a DBT project?
A DBT project is a directory structure containing configurations, SQL models, tests, seeds, and other files required to manage data transformations.
7. What is a Seed in DBT?
A seed is a static CSV file stored in the DBT project, which can be loaded into the data warehouse as a table.
8. What is the role of Jinja in DBT?
Jinja is a templating engine used in DBT to create dynamic SQL by incorporating loops, variables, and conditions.
9. What databases does DBT support?
DBT supports databases like Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, and more.
10. What is a DBT test?
A DBT test validates the data’s quality and integrity. There are two types:
Generic tests: Built-in for common validations (e.g., uniqueness, non-null).
Custom tests: User-defined SQL queries for specific checks.
Intermediate-Level DBT Interview Questions
11. Explain the concept of materializations in DBT.
Materializations define how DBT persists a model’s data in the warehouse. Common types:
View: Creates a database view.
Table: Creates a physical table.
Ephemeral: Temporary, in-memory computations.
Incremental: Updates only new or changed data.
12. What is a DBT snapshot?
Snapshots capture historical changes in data by comparing the current state with a previous state and storing changes.
13. How do you manage dependencies between DBT models?
Dependencies are managed using ref() in SQL queries. For example:
sql
SELECT * FROM {{ ref('previous_model') }}
This ensures correct execution order.
14. What is a DBT macro?
A DBT macro is a reusable piece of code written in Jinja to simplify repetitive tasks.
15. What is the difference between DBT CLI and DBT Cloud?
DBT CLI: Command-line interface for executing DBT projects.
DBT Cloud: A managed service offering scheduling, monitoring, and team collaboration.
16. What is the dbt docs command used for?
The dbt docs command generates and serves interactive documentation, including data lineage.
17. What are hooks in DBT?
Hooks are SQL statements executed before or after certain DBT operations like running models.
18. How do you handle sensitive credentials in DBT?
Use environment variables or secret management tools (e.g., AWS Secrets Manager) to avoid hardcoding sensitive data.
19. Can DBT be used for ELT pipelines?
Yes, DBT is used for the Transformation (T) stage in ELT workflows after data has been extracted and loaded into the warehouse.
20. What are exposures in DBT?
Exposures define how downstream systems, like dashboards or reports, depend on DBT models.
Advanced-Level DBT Interview Questions
21. What is the use of the dbt compile command?
The dbt compile command generates the raw SQL that DBT executes, allowing users to debug and inspect the compiled SQL.
22. How do you implement incremental models in DBT?
Incremental models are created using the is_incremental() macro, enabling efficient updates by processing only new or changed data.
23. What is a surrogate key, and how is it used in DBT?
A surrogate key is a unique identifier for records. In DBT, it can be generated using macros like hash().
24. How do you schedule DBT jobs?
DBT jobs can be scheduled in DBT Cloud or orchestrated using tools like Apache Airflow, Prefect, or Dagster.
25. What are the best practices for organizing DBT projects?
Use modular SQL models.
Create separate directories for staging, intermediate, and final models.
Add tests and documentation for all models.
26. How do you debug failing DBT models?
Check the compiled SQL files.
Use –debug mode during execution.
Review logs and data warehouse error messages.
27. What is the role of YAML files in DBT?
YAML files store metadata for models, tests, seeds, and sources.
28. What is a source in DBT?
A source represents raw tables in the warehouse. They are defined in YAML files and serve as input for transformations.
29. What is the –full-refresh option in DBT?
The –full-refresh flag forces DBT to rebuild incremental models from scratch.
30. How do you test DBT models in CI/CD pipelines?
Integrate DBT with CI/CD tools to:
Run dbt run for transformations.
Use dbt test for data validations.
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