dbt vs Airflow: Which Data Transformation Tool to Choose?

dbt and Airflow are popular tools for automating data operations like transformation. The transformed data produced with these tools can be beneficial for generating actionable insights that improve business performance. While both tools are essential for modern data workflows, they serve different purposes.

dbt is primarily used for data transformation, while Airflow supports end-to-end data pipeline orchestration. Understanding the differentiating aspects of these tools is essential for selecting the right one for your workflow.

This article highlights the key differences between dbt vs Airflow and explores scenarios where each tool is better suited.

What Is dbt?

dbt, or data build tool, is a data transformation tool that lets you modify data with Structured Query Language (SQL). It was developed by dbt labs to allow the management of analytical workflows. Utilizing dbt, you can build, track, and test data transformation models.

For flexible deployment, dbt offers two versions: dbt Cloud and dbt Core. The Cloud version is a fully managed solution, where you do not need to stress over infrastructure management. dbt Core is a self-managed, open-source version that gives you more control over your data workflows.

Key Features

• dbt provides a web interface for model development. The interface offers features like SQL syntax highlighting and AI Copilot.
• With the dbt job scheduler, you can define triggers that automatically execute your dbt models. The scheduler supports cron-based and event-driven executions. Cron-based jobs run at specified time intervals. Event-driven jobs execute after the completion of another task, like API requests.
• dbt models are composed of small, reusable SQL modules containing SELECT statements. These commands turn data into an analysis-ready format. The modularity improves code readability and reusability.

Pros

• dbt enables you to auto-generate documentation for your transformation workflows. With dbt Explorer, you can view a project’s lineage and resources, such as models and tests.
• dbt complies with industry regulations, including SOC 2, GDPR, PCI, and HIPAA. It assists in securing data from unauthorized access.

Cons

• dbt is generally used with extract, load, and transform (ELT) workflows. This workflow involves extracting data from a source, loading it in a warehouse, and applying transformations. However, dbt has limited functionality for scenarios that require transformation before storage.

What Is Apache Airflow?

Apache Airflow is an open-source tool for building, scheduling, and monitoring batch data workflows. It uses directed acyclic graphs (DAGs) to define workflow execution sequences. Each task depends on the completion of previous tasks. DAGs are written in Python.

Airflow is widely used for data pipeline management and orchestration tasks, offering features like task dependencies, retries, and monitoring. For instance, Airflow’s free-of-cost workflow management features set it apart when comparing Airflow vs Informatica or Talend vs Airflow.

Key Features

• Airflow’s capabilities are not limited to data transformation. You can also use this tool to develop scripts for moving data across platforms. Using custom scripts, you can define ETL pipelines that automate the extraction of data and transform it into a format compatible with the destination. Finally, you can load the data into a target system.
• Airflow’s web interface can help monitor, schedule, and manage your workflows. It provides real-time insights into the progress and logs of every executed as well as ongoing task.
• Airflow offers a suite of plug-and-play operators for various platforms. These operators allow you to integrate data from and to prominent databases and warehouses.

Pros

• In Airflow, you can define parameters for task-level error handling. If a task fails, it can automatically re-execute. This feature is beneficial for resolving temporary issues like system outages.
• Airflow’s BaseNotifier class lets you set up custom alerts. Defining the notify method of this class enables the creation of custom notification logic. You can get information about the status of a task or a DAG run.

Cons

• Performing operations in Airflow requires an understanding of Python. Concepts like DAGs and task dependencies can be challenging to understand.
• Large-scale deployment of Airflow requires Kubernetes expertise.

dbt vs Airflow: Key Differences

Let’s explore detailed aspects differentiating Apache Airflow vs dbt.

Data Transformation Approaches

dbt uses SQL and Jinja—a web templating engine—for data transformation. Leveraging SQL queries, you can define transformation logic to modify data. dbt is a key component of modern ELT pipelines.

Data integration tools like Airbyte can help build such pipelines. With these pipelines, you can extract and load the data in a warehouse. Once the data arrives in the warehouse, you can execute dbt transformations.

On the other hand, Apache Airflow enables you to create data transformation scripts using Python. This makes Airflow a flexible platform, as Python backs the execution of SQL queries using its extensive libraries.

Another advantage of Airflow is its integration with dbt. If you intend to use dbt for transformations, leverage Airflow’s dbt Cloud connector. This approach allows you to define custom pipeline logic to extract data and apply dbt transformations.

Testing and Validation Capabilities

dbt offers two ways to define tests: generic and custom. Generic data tests are parameterized queries for automated validation. The custom tests assist in creating your own validation strategies for assessing the data quality.

In contrast, Airflow supports DAG testing to ensure your tasks operate as expected. Setting up tests requires checking for import errors, dependencies, and custom code requirements. By establishing and deploying validation strategies within a CI/CD pipeline, you can monitor DAG performance.

Scalability and Performance Considerations

dbt Cloud has a cell-based architecture that operates across multiple regions. Each region is physically separate from others and has its own set of cells. This setup enhances performance and reliability.

Depending on your regulatory needs, you can deploy dbt Cloud in a region of your choice. New cells are added to meet your scalability demands. At the same time, you operate independently from the cell that hosts your account.

You can boost dbt performance by following best practices like adopting parallel processing.

Conversely, Airflow provides a Kubernetes Executor for scalability. In this setup, you can use Kubernetes to manage growing business workloads. It helps automatically create separate worker pods for each task in a cluster.

However, this executor comes with some challenges, such as resource overhead. To fix this issue, you can use Celery, which is a distributed task queue system. You can run it on Kubernetes using the Kubernetes-based Event-Driven Autoscaling (KEDA). This setup can improve performance while making better use of resources.

Pricing

dbt Cloud offers three plans, including Developer, Team, and Enterprise. The Developer plan is free of cost, while the Team version has pay-as-you-go pricing. dbt Enterprise version pricing depends on the total number of users and the amount of successful models built.

Apache Airflow, in hindsight, is an entirely free-to-use software.

When to Use dbt?

When comparing Airflow vs dbt, consider their key differences. If your primary focus is simple data transformation and modeling, choose dbt. Using dbt, you can clean and enrich your data in a format that is ready for downstream processing. Transitioning to dbt is simpler if you have SQL experience.

The only limitation of dbt is its lack of data integration capabilities. To overcome this challenge, you can use tools like Airbyte, an Airflow alternative for data migration.

Airbyte is a no-code AI data integration tool. It lets you extract data from dispersed sources and load it into your preferred destination. With 550+ pre-built connectors, Airbyte empowers you to centralize information in data storage systems. If your required connector is unavailable, you can use Airbyte’s Connector Builder or Connector Development Kits (CDKs) to build custom connectors.

Let’s explore a few features of Airbyte:

• You can utilize Airbyt’s dbt Cloud integration to execute dbt transformations immediately after the data reaches the target system. Using this approach, you can clean and enrich your data, making it analysis-ready.
• The CDC functionality allows you to identify incremental changes made to the source and replicate them to the target system. With this feature, you can track updates and maintain data consistency.
• Airbyte’s Connector Builder includes an AI assistant that can read any platform’s API documentation. It can then auto-fill most configuration fields to set up the connector.
• To streamline data management and operation scheduling, you can connect Airbyte with popular data orchestration tools like Apache Airflow, Prefect, and Kestra.

When to Use Airflow?

Airflow is a good option when you need to orchestrate complex workflow and automate ETL jobs. It also helps manage large-scale data pipelines across different environments. Another advantage of Airflow is its support for operators and hooks, which allow you to connect to data storage platforms like Amazon S3.

Scenarios Where dbt & Airflow Complement Each Other

• Although dbt is a robust solution, it provides limited capabilities for data consolidation. You can resolve this issue by connecting dbt with Airflow. With this integration, you can develop dynamic pipelines that use Airflow to orchestrate tasks and trigger dbt jobs.
• You can use Airflow dbt integration to enhance error handling. Airflow lets you monitor dbt runs and retry failed tasks. When any issue arises, you will receive automated notifications.
• To develop robust data pipelines, you can use Airflow software integrations. However, Airflow has a limited number of operators. To connect to platforms that are unavailable in Airflow software integrations, you might have to write custom scripts.

An alternative approach is to combine Airbyte with dbt and orchestrate this process using Airflow. This is an effective solution for modern data infrastructures.

Conclusion

When comparing dbt vs Airflow, it is essential to understand both serve different purposes. dbt can help you define transformation models. Airflow can assist in managing data pipelines and scheduling operations.

To get optimal results, integrate dbt with Airbyte and use Airflow to orchestrate the process. This integration allows you to generate high-quality datasets that can enhance decision-making.