The DAD Stack: Dagster-Airbyte-dbt

Dagster and dbt work best in ELT (Extract, Load, Transform) pipelines. In an ELT setup, you first load raw data into your warehouse, then use dbt for in-warehouse transformations orchestrated by Dagster. This article provides a quick start guide for building a data pipeline orchestration using Airbyte, Dagster, dbt, and BigQuery. For this example, we will extract data from Postgres, load it into BigQuery, and apply necessary transformations using dbt, all orchestrated seamlessly with Dagster. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.

What is dbt & Dagster?

dbt converts raw data in your warehouse into analytics-ready models through SQL transformations, while handling testing, documentation, and dependencies. Dagster orchestrates entire data pipelines, including dbt runs, managing execution flow, monitoring, and scheduling. Together, they let you build reliable data pipelines where dbt handles the SQL transformations, and Dagster controls when and how everything runs.

How do Airbyte & Dagster complement each other?

Airbyte extracts data from various sources and loads it into your data warehouse using pre-built connectors, while Dagster orchestrates when and how these extracts run within your broader pipeline. Dagster can trigger Airbyte syncs, monitor their status, and coordinate subsequent transformations (like dbt) once the data lands. This integration lets you build end-to-end pipelines where Airbyte handles the EL (Extract-Load), and Dagster manages the workflow, ensuring data flows correctly from source to final transformation.

Prerequisites

Before you embark on this integration, ensure you have the following set up and ready:

1. Python 3.10 or later: If not installed, download and install it from Python's official website.
2. Docker and Docker Compose (Docker Desktop): Install Docker following the official documentation for your specific OS.
3. Airbyte OSS version: Deploy the open-source version of Airbyte. Follow the installation instructions from the Airbyte Documentation.
4. Terraform: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the official Terraform installation guide.

1. Setting an environment for your project

Get the project up and running on your local machine by following these steps:

1. Clone the repository (Clone only this quickstart):

git clone --filter=blob:none --sparse  https://github.com/airbytehq/quickstarts.git

cd quickstarts

git sparse-checkout add airbyte_dbt_dagster

2. Navigate to the directory:

cd airbyte_dbt_dagster

3. Set Up a Virtual Environment:

For Mac:

python3 -m venv venv
source venv/bin/activate

For Windows:

python -m venv venv
.\venv\Scripts\activate

4. Install Dependencies:

pip install -e ".[dev]"

2. Setting Up Airbyte Connectors with Terraform

Airbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:

1. Navigate to the Airbyte Configuration Directory:

Change to the relevant directory containing the Terraform configuration for Airbyte:

cd infra/airbyte

2. Modify Configuration Files:

Within the infra/airbyte directory, you'll find three crucial Terraform files:

• provider.tf: Defines the Airbyte provider.
• main.tf: Contains the main configuration for creating Airbyte resources.
• variables.tf: Holds various variables, including credentials.

Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and BigQuery connections. You can utilize the variables.tf file to manage these credentials.

3. Initialize Terraform:

This step prepares Terraform to create the resources defined in your configuration files.

terraform init

4. Review the Plan:

Before applying any changes, review the plan to understand what Terraform will do.

terraform plan

5. Apply Configuration:

After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.

terraform apply

6. Verify in Airbyte UI:

Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.

3. Setting Up the dbt Project

dbt (data build tool) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:

1. Navigate to the dbt Project Directory:

Change to the directory containing the dbt configuration:

cd dbt_project

2. Update Connection Details:

You'll find a profiles.yml file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.

3. Utilize Environment Variables (Optional but Recommended):

To keep your credentials secure, you can leverage environment variables. An example is provided within the profiles.yml file.

4. Test the Connection:

Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:

dbt debug

If everything is set up correctly, this command should report a successful connection to BigQuery.

4. Orchestrating with Dagster

Dagster is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:

1. Navigate to the Orchestration Directory:

Switch to the directory containing the Dagster orchestration configurations:

dbt debug

2. Set Environment Variables:

Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:

export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1
export AIRBYTE_PASSWORD=password

3. Launch the Dagster UI:

With the environment variables in place, kick-start the Dagster UI:

dagster dev

4. Access Dagster in Your Browser:

Open your browser and navigate to:

http://127.0.0.1:3000

Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on "view global asset lineage". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.

Next Steps

Once you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:

1. Create dbt Sources for Airbyte Data: Your raw data extracted via Airbyte can be represented as sources in dbt. Start by creating new dbt sources to represent this data, allowing for structured transformations down the line.
2. ‍Add Your dbt Transformations: With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.
3. Execute the Pipeline in Dagster: Navigate to the Dagster UI and click on "Materialize all". This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps.
4. Extend the Project: The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes.