Ce repository a pour objectif de mettre en place rapidement une infrastructure Airflow permettant à chacun de tester son DAG avant mise en production.
L'infrastructure actuelle est basée sur du LocalExecutor (le scheduler, le webserver et worker sont hébergés sur le même container)
git clone [email protected]:etalab/data-engineering-stack.git
cd data-engineering-stack
# Create directories necessary for Airflow to work
./1_prepareDirs.sh
# Prepare .env file
./2_prepare_env.sh
nano .env
# Edit POSTGRES_USER ; POSTGRES_PASSWORD ; POSTGRES_DB ; AIRFLOW_ADMIN_MAIL ; AIRFLOW_ADMIN_FIRSTNAME ; AIRFLOW_ADMIN_NAME ; AIRFLOW_ADMIN_PASSWORD
# Launch services
docker-compose up --build -d
# After few seconds, you can connect to http://localhost:8080 with login : AIRFLOW_ADMIN_MAIL and password : AIRFLOW_ADMIN_PASSWORD
# Airflow used to have a little time before dag refreshing when dag is created. You can force refreshing with :
./refreshBagDags.sh
Connections can be created manually or with python scripts createConn.py (using Airflow API) inside each projects. You need also to add your ssh key inside ssh folder of repository for the container to be able to see it in /home/airflow/.ssh/ folder of container.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent