Deploying Airflow with Docker
Containerizing Airflow using Docker
As far as data science models are concerned, it is safe to say that they are meant to operate periodically. Airflow is a platform where one can author, monitor, and schedule processes programmatically. Airflow is instrumental in the parallel execution of jobs in addition to scheduling and monitoring. This makes Airflow a highly optimized ETL tool. Airflow is gaining a lot of traction in ETL and data engineering use cases where automation and parallel processing plays a huge role.
Given the optimization of Airflow, it’s important that Airflow is deployed in an optimized manner as well, and that is where Docker comes in. Docker containerizes Airflow within Linux containers such that all Airflow dependencies are contained. This enables easy installation of airflow in workstations without worrying about installing and managing dependencies. The integration of Airflow for data management and Docker for containerization provides a seamless platform for using and applying Airflow.
Apache airflow
Apache airflow consists of a web server, a scheduler, and a meta-database as its components. The web server allows for user interaction with the graphical user interface. The scheduler on the other hand is responsible for scheduling jobs for airflow and the meta…
