Introduction

dbt empowers data professionals to transform raw data into clean, analytics-ready datasets. Mastering its essential commands is key to unlocking its full potential. In this tutorial, we’ll explore the key dbt commands, providing both the syntax and a short description for each command. This tutorial is also a valuable resource for those preparing for the dbt Analytics Engineering Certification Exam.

Overview of dbt commands

Let's have a look at some the most essential dbt commands you should know.

Project Setup & Configuration

  • dbt init - Initialize a new dbt project.
  • dbt deps - Install dependencies from packages.yml.
  • dbt debug - Debug and validate your environment setup.
  • dbt clean - Remove temporary directories and files created by dbt.

Development

  • dbt compile - Compile dbt models into raw SQL without executing them.
  • dbt show - Preview compiled code or SQL for a specific resource.
  • dbt clone - Clone a resource (e.g., a model, seed, or snapshot).

Core Operations

  • dbt run - Build models by executing SQL in the data warehouse.
  • dbt build - End-to-end execution: runs models, tests, seeds, and snapshots.
  • dbt seed - Load CSV files into the data warehouse.
  • dbt snapshot - Create and manage historical data snapshots.

Testing & Validation

  • dbt test - Run tests to validate model logic.
  • dbt source freshness - Verify the freshness of source data.

Documentation & Metadata

  • dbt docs generate - Generate project documentation.
  • dbt list - List all resources (models, seeds, snapshots, etc.).

Conclusion

Congratulations! Now you are one step closer to become a Data Engineer. With these essential dbt commands, you’re equipped to efficiently work with dbt. Keep practicing and experimenting to deepen your knowledge. The best way to learn is through hands-on experience, so try it yourself and explore all that dbt has to offer. Happy coding!