This is a guide to setup an Airbyte Api connection that is orchestrated by Airflow.

• Airflow orchestration Dag example

  

• Create an AWS EC2 instance as follows

  • Name : airbyte
  • AWS Machine Image : Amazon Linux 2 Kernel 5.10 / 64 bit (x86)
  • Instance type : t2.large 8g Memory
  • Key pair (login) : Create a key pair to SSH login from your host
  • Network settings : Create security group & ALLOW SSH traffic from your host ip address
  • Configure storage : 15 GB gp2

• Edit security group inbound rules and expose the following ports

  • 8000 → for Airbyte webapp
  • 8080 → for airflow webapp
  • 5555 → for flower-monitoring

• SSH Log in to your AWS instance & Run the following commands to install docker & clone the github repo

  sudo yum -y update && \
  sudo yum install -y git && \
  sudo yum install -y docker && \
  sudo usermod -a -G docker $USER && \
  git clone https://github.com/ahmedfarag9/airbyte-airflow-simple-scraper.git && \
  cd airbyte-airflow-simple-scraper && \
  newgrp docker

• Prepare the environment

  bash install.sh

• Build airflow image

  make build-airflow-image

• Start Airflow/Airbyte stack

  make start-airflow-airbyte-stack

• Now your setup is ready!

• The current terminal session will be used for logging stream

• Access Airbyte at http://localhost:8000 and set up a connection

  Set up a Source

  

  Set up a Destination

  

  Set up a Connection

  

  Go to the connections page and choose your connection then copy connection id from page url

  

• Open a new terminal session & SSH Log in to your AWS instance & Run the following commands to set the API connection id

  cd airbyte-airflow-simple-scraper/ && \
  bash auto_connection.sh

• Then Enter the copied Airbyte connection ID when prompted

• Access Airflow at http://localhost:8080 & enter the credentials

  • USERNAME=airflow

  • PASSWORD=airflow

  

• Activate dag to triger Airbyte to fetch Api data

  

• Then you will notice task is executed successfully in airflow task diagram

  

• And data sync is triggered successfully in Airbyte UI

  

• Finally you can Access Airflow flower for data monitoring at http://localhost:5555

  

• Stop then start Airflow/Airbyte stack again

  • Hit control C to stop the stack then execute the following command to start it once again

    make start-airflow-airbyte-stack

• Uninstall Airflow/Airbyte stack

  make uninstall-airflow-airbyte-stack

• Remove containers and restart stack

  make restart-airflow-airbyte-stack

• Purge then clean install everything

  make purge-then-clean-install

• Refer to the Makefile for more info

• Airbyte --> data integration engine

  • UI: An easy-to-use graphical interface for interacting with the Airbyte API. (runs on port 8000)

  • Server: Handles connection between UI and API. (runs on port 800)

  • Scheduler: The scheduler takes work requests from the API and sends them to the Temporal service to parallelize.

  • Worker: The worker connects to a source connector, pulls the data and writes it to a destination.

• Airflow --> workflow management platform

  • Airflow consists of several components:

  • Postgres Database for Metadata --> Contains information about the status of tasks, DAGs, Variables, connections, etc.

  • Scheduler --> Reads from the Metadata database and is responsible for adding the necessary tasks to the queue

  • Executor --> Works closely with the Scheduler to determine what resources are needed to complete the tasks as they are queued

  • Web server --> HTTP Server provides access to DAG/task status information

• Postgres --> Metadata Database

• Celery Executor --> The Remote Executor to scale out the number of workers.

• Redis --> Used as a message broker by delivering messages to the celery workers.

• Flower --> Celery Monitoring Tool (runs on port 5555)