Welcome to the "Airbyte-dbt-Airflow-Snowflake Integration" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Airflow, dbt, and Snowflake. Easily extract data from Postgres and load it into Snowflake using Airbyte, and apply necessary transformations using dbt, all orchestrated seamlessly with Airflow. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.
This quickstart is designed to minimize setup hassles and propel you forward.
Infrastructure Layout Airflow Pipeline DAG Prerequisites Before you embark on this integration, ensure you have the following set up and ready:
Python 3.10 or later : If not installed, download and install it from Python's official website .Docker and Docker Compose (Docker Desktop) : Install Docker following the official documentation for your specific OS.Airbyte OSS version : Deploy the open-source version of Airbyte. Follow the installation instructions from the Airbyte Documentation .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.gitgit sparse-checkout add airbyte_dbt_airflow_snowflake2. Navigate to the directory :
cd airbyte_dbt_airflow_snowflake3. Set Up a Virtual Environment :
For Linux & Mac:
For Windows:
4. Install Dependencies :
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:
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 Snowflake 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.
4. Review the Plan :
Before applying any changes, review the plan to understand what Terraform will do.
5. Apply Configuration :
After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.
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, Snowflake. 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:
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 Snowflake 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 Snowflake instance using:
If everything is set up correctly, this command should report a successful connection to Snowflake.
4. Orchestrating with Airflow Airflow 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 Airflow to oversee both the Airbyte and dbt workflows:
1. Navigate to the Orchestration Directory :
Switch to the directory containing the Airflow orchestration configurations:
2. Set Environment Variables :
The Airflow pipeline requires certain environment variables to run successfully. The variables will be set using the .env file. Populate the .env file with the contents of the .env.example file and modify to suit your use case.
Particularly, modify the AIRFLOW_AIRBYTE_CONN value which is the connection URI that Airflow uses to connect to the Airbyte API. See here for more details.
Also modify the AIRBYTE_CONN_ID value which is the id of the connection you have set up in Airbyte.
3. Build and Run Airflow Locally :
Build our Airflow image with the necessary packages and services:
And then run it:
4. Access Airflow in Your Browser :
When it's done, you can access the Airflow UI at http://127.0.0.1:8080 . The default username and password are both airflow, unless you changed it on the .env file.
Here, you should see the DAG for the Extract, Load and Transformation pipeline. To get an overview of DAG, click on the DAG's name and select the Graph view. This will give you a clear picture of the process lineage and visualize how the operation flows from extraction to transformation.
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 Airflow :
Navigate to the Airflow UI and Trigger the DAG. This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps. Modify the schedule as well to suit your use case.
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.