Add PAD-BigQuery quickstart

airbyte_dbt_prefect/.gitignore

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airbyte_dbt_prefect/README.md

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+# Airbyte-dbt-Prefect Integration (with BigQuery)
+
+Welcome to the "Airbyte-dbt-Prefect Integration" repository! This repository offers a quickstart template for building a full data stack using Airbyte, Prefect, dbt, and BigQuery. Effortlessly extract data from Postgres, transfer it to BigQuery, and execute essential transformations using dbt, all smoothly orchestrated through Prefect. While this template doesn't dive into the specifics of data or transformations, its objective is to highlight the seamless collaboration of these powerful tools.
+
+This quickstart is designed to minimize setup hassles and propel you forward.
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [Setting an environment for your project](#1-setting-an-environment-for-your-project)
+- [Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)
+- [Setting Up the dbt Project](#3-setting-up-the-dbt-project)
+- [Orchestrating with Prefect](#4-orchestrating-with-prefect)
+- [Next Steps](#next-steps)
+
+## 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](https://www.python.org/downloads/).
+
+2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-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](https://docs.airbyte.com/quickstart/deploy-airbyte/).
+
+4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).
+
+
+## 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)**:  
+   ```bash
+   git clone --filter=blob:none --sparse  https://github.com/airbytehq/quickstarts.git
+   ```
+
+   ```bash
+   cd quickstarts
+   ```
+
+   ```bash
+   git sparse-checkout add airbyte_dbt_prefect
+   ```
+
+
+2. **Navigate to the directory**:  
+   ```bash
+   cd airbyte_dbt_prefect
+   ```
+
+3. **Set Up a Virtual Environment**:  
+   - For Mac:
+     ```bash
+     python3 -m venv venv
+     source venv/bin/activate
+     ```
+   - For Windows:
+     ```bash
+     python -m venv venv
+     .\venv\Scripts\activate
+     ```
+
+4. **Install Dependencies**:  
+   ```bash
+   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:
+   ```bash
+   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.
+   ```bash
+   terraform init
+   ```
+
+4. **Review the Plan**:
+
+   Before applying any changes, review the plan to understand what Terraform will do.
+   ```bash
+   terraform plan
+   ```
+
+5. **Apply Configuration**:
+
+   After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.
+   ```bash
+   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)](https://www.getdbt.com/) 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:
+   ```bash
+   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:
+   ```bash
+   dbt debug
+   ```
+
+   If everything is set up correctly, this command should report a successful connection to BigQuery.
+
+## 4. Orchestrating with Prefect
+
+Prefect is a modern workflow orchestrator designed to streamline the creation, testing, and monitoring of data workflows. In this section, we'll guide you through setting up Prefect to manage both the Airbyte and dbt workflows:
+
+1. Navigate to the Orchestration Directory:
+
+Change your working directory to the one containing the Prefect orchestration configurations:
+```bash
+cd orchestration
+```
+
+2. Set Environment Variables:
+
+Prefect requires specific environment variables to interact effectively with other tools like dbt and Airbyte. Configure the following variables:
+```bash
+export DBT_PARSE_PROJECT_ON_LOAD=1
+export AIRBYTE_PASSWORD=password
+```
+Note: The AIRBYTE_PASSWORD is set to password as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.
+
+3. Run the Prefect Flow:
+
+With the environment variables properly configured, you can run your Prefect workflow:
+```bash
+python flows.py
+```
+
+In this Prefect-based setup, you use Python scripts to define and execute your workflows, making it easier to manage and monitor data pipelines involving Airbyte and dbt.
+
+## 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](https://docs.getdbt.com/docs/build/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. **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.

airbyte_dbt_prefect/dbt_project/.gitignore

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+
+target/
+dbt_packages/
+logs/
+
+#Desktop Services Store
+.DS_Store
+
+#User cookie
+.user.yml

airbyte_dbt_prefect/dbt_project/README.md

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+Welcome to your new dbt project!
+
+### Using the starter project
+
+Try running the following commands:
+- dbt run
+- dbt test
+
+
+### Resources:
+- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)
+- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
+- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support
+- Find [dbt events](https://events.getdbt.com) near you
+- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices