Master Airflow and DBT: The Ultimate Tutorial

Table of Contents

1. Introduction
2. What is DBT and its features
3. The different ways of integrating DBT with Airflow
4. The issues with the bash operator integration
5. Integrating DBT with Airflow using DBT Cloud
6. The limitations of DBT Cloud integration
7. Introducing Cosmos: The best way to integrate DBT with Airflow
8. Understanding Cosmos and its components
9. Setting up Cosmos with the Astro CLI
10. Running Airflow locally with the Astro CLI
11. Creating a DAG to import seeds using Cosmos and DBT
12. Managing connections in Airflow with Cosmos
13. Running DBT models and tests in Airflow using Cosmos
14. Monitoring and debugging DBT projects with Cosmos in Airflow
15. Conclusion

DBT Integration with Airflow using Cosmos

DBT (Data Build Tool) is a popular command interface tool that simplifies data transformation by allowing data analysts and engineers to write SQL statements that are converted into tables and views. One of the common challenges faced by users is integrating DBT with Airflow, a platform used for workflow orchestration. In this article, we will explore the various ways of integrating DBT with Airflow and introduce Cosmos, a powerful tool that provides seamless integration between DBT and Airflow.

Introduction

Welcome to this article on integrating DBT with Airflow using Cosmos. In this article, we will discuss the different ways of integrating DBT with Airflow, the limitations of existing approaches, and introduce Cosmos as the best solution for seamless integration.

What is DBT and its features

DBT (Data Build Tool) is a command interface tool that simplifies data transformation by allowing data analysts and engineers to write SQL statements. It supports various databases like Postgres, Redshift, and Google BigQuery and enables the creation of dependencies between SQL statements using Jinja templating. DBT also facilitates documentation and data quality checks.

The different ways of integrating DBT with Airflow

There are several ways to integrate DBT with Airflow, including the bash operator and DBT Cloud integration. However, these approaches have limitations that can hinder the workflow and observability of DBT projects.

The issues with the bash operator integration

Using the bash operator to run DBT commands in Airflow can lead to issues such as running the entire DBT project with associated models and tests in a single task. This makes debugging and rerunning specific tasks difficult and costly. Additionally, there is a lack of observability, as debugging requires switching between DBT and Airflow.

Integrating DBT with Airflow using DBT Cloud

Using DBT Cloud provides better integration with Airflow by offering operators like the DBT Cloud run job operator and the DBT Cloud hook. These operators allow interactions with the DBT API and enable better visibility and control over DBT jobs. However, this integration is limited to DBT Cloud and may still require switching between DBT and Airflow for debugging.

The limitations of DBT Cloud integration

While DBT Cloud integration improves upon the bash operator approach, it still falls short in providing a seamless workflow. Users might need to switch back and forth between DBT and Airflow for debugging purposes, resulting in a disjointed experience.

Introducing Cosmos: The best way to integrate DBT with Airflow

Cosmos is a powerful tool that parses and renders third-party workflows like DBT projects as Airflow DAGs, task groups, or individual tasks. It provides a smooth integration between DBT and Airflow, allowing for better observability and management of DBT projects directly within Airflow. Cosmos consists of parsers and operators that convert workflows into Airflow-compatible components.

Understanding Cosmos and its components

Cosmos comprises two main components: parsers and operators. Parsers extract workflows from providers, like DBT, and convert them into Airflow DAGs, task groups, or individual tasks. Operators, on the other HAND, serve as lightweight classes that define the behavior of these Airflow components in Cosmos.

Setting up Cosmos with the Astro CLI

To set up Cosmos, we can use the Astro CLI, which simplifies the installation and configuration of Airflow. By following the instructions provided in the link, You can install the Astro CLI and set up Airflow on your local machine. Cosmos configuration involves adding files and dependencies to your project, ensuring smooth integration between DBT and Airflow.

Running Airflow locally with the Astro CLI

Once Airflow is set up using the Astro CLI, you can run Airflow locally on your machine. This allows you to access the Airflow user interface and manage your workflows efficiently. By utilizing the Astro CLI, you can easily set up an environment to run DBT projects seamlessly with Airflow using Cosmos.

Creating a DAG to import seeds using Cosmos and DBT

To demonstrate the power of Cosmos and its integration with DBT and Airflow, we will Create a DAG to import seeds. Seeds are CSV files with raw data that are loaded into a PostgreSQL database. By utilizing Cosmos operators like DBT run and DBT seed, we can create tasks that import the seeds and set up the dependencies between them.

Managing connections in Airflow with Cosmos

Cosmos simplifies the management of connections between Airflow and DBT projects. Instead of relying on DBT profile settings, connections can be managed directly within Airflow. This allows for a more streamlined approach, ensuring that all database connections are handled within the Airflow environment.

Running DBT models and tests in Airflow using Cosmos

With Cosmos, running DBT models and tests in Airflow becomes effortless. Cosmos parses your DBT project and automatically creates the corresponding tasks for each model and test in Airflow. This provides full observability and control over your DBT project within the Airflow user interface.

Monitoring and debugging DBT projects with Cosmos in Airflow

Cosmos enhances the monitoring and debugging capabilities of DBT projects within Airflow. You can easily track the execution state of your DBT project, view dependencies between models and tests, and debug any issues directly within the Airflow user interface. This eliminates the need to switch between DBT and Airflow, resulting in a more efficient workflow.

Conclusion

In conclusion, integrating DBT with Airflow using Cosmos provides a seamless and efficient workflow for data transformation. Cosmos simplifies the setup and management of DBT projects in Airflow, offering better observability, control, and debugging capabilities. By utilizing Cosmos, users can streamline the integration between DBT and Airflow, enhancing their data transformation processes.