Airflow VS Docker Compose

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte

curl -fsSL https://get.docker.com -o get-docker.sh sudo sh get-docker.sh # Install docker compose sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose # Apply executable permissions to the binary sudo chmod +x /usr/local/bin/docker-compose # Run Project docker-compose up -d

Docker,

This foundation now opens the door to even more powerful concepts. You can explore more advanced concepts such as container networking, streamlining the management of complex applications with Docker Compose, and how to make your application data persistent using volumes.

In many projects that are containerized, especially in cases where development is also done locally with docker-compose, teams often have two Dockerfiles, 1 for Development, the other for Production. If you happen to have multiple environments like pre-prod, staging and so on, some teams could have different Dockerfiles for these environments.

sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

You can also use Docker Compose to deploy the cluster, without having to write all the previous commands.

[ec2-user]$ sudo yum update -y [ec2-user]$ sudo yum install -y docker [ec2-user]$ sudo service docker start [ec2-user]$ sudo curl -L https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose [ec2-user]$ sudo chmod +x /usr/local/bin/docker-compose [ec2-user]$ docker --version Docker version 20.10.23, build 7155243 [ec2-user]$ docker-compose --version Docker Compose version v2.18.1

Docker Compose Documentation

groupadd -g 997 docker gpasswd -a jenkins docker curl -L https://github.com/docker/compose/releases/tag/1.29.2/docker-compose-`uname -s`-`uname -m` > /usr/local/bin/docker-compose chmod +x /usr/local/bin/docker-compose #confirm the docker-compose is installed docker-compose version

Docker Compose overview Docker compose Doc