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Postgres Data Replication Stack

Welcome to the "Postgres Data Replication Stack" repository! This repo provides a quickstart template for building a postgres data replication solution using Airbyte. We will easily synchronize two Postgres databases with Airbyte using Change Data Capture (CDC) and Postgres Write Ahead Log (WAL). While this template doesn't delve into specific data, its goal is to showcase how the data replication solution can be achieved.

This quickstart is designed to minimize setup hassles and propel you forward.

Table of Contents

Infrastructure Layout

infrastructure layout

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 postgres_data_replication

  2. Navigate to the directory:

    cd postgres_data_replication

  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 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.

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. Extend the Project:

    The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or add some 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.