Airflow is a powerful and flexible workflow automation and scheduling system, powered by Python with a rich set of integrations and tools available out-of-the-box. In this article I will demonstrate how to use Airflow with Docker.
Airflow is a powerful and flexible workflow automation and scheduling system, powered by Python with a rich set of integrations and tools available out-of-the-box.
Although the vast amount of documentation and large configuration files make learning Airflow look like a daunting task, it is easy to get either a simple or more complex configuration up and running quickly for developers to begin writing code and learn how the product actually works. In this article I will demonstrate how to use Airflow with Docker to achieve this using a public Github repo I authored here:
DAG — directed acyclic graph. How to run a workflow, visible in the dashboard on the web interface.
Worker — one or more systems responsible for running the code in the workflow task queues
Webserver — displays UI for managing Airflow, manages user requests for running tasks, and receives updates from DAG runs via workers
Scheduler — determines if a Task needs to be run and triggers work to be processed by a Worker
Operator — a step of what actually gets run inside a DAG
Task — an instantiated Operator created by the scheduler, a single unit of work
**Task Instance** — stored state of a task
In this post I will show you how to set up official Apache/Airflow with PostgreSQL and LocalExecutor using docker and docker-compose. In this post, I won’t be going through Airflow, what it is, and how it is used. Please check the official documentation for more information about that.
I’ve been using it for around 2 years now to build out custom workflow interfaces, like those used for Laboratory Information Management Systems (LIMs), Computer Vision pre and postprocessing pipelines, and to set and forget other genomics pipelines.
Neste episódio colocamos o serviço #python para rodar com #Docker e Docker compose e o próximo passo será a #api Multistreaming with https://restream.io/?ref...
In this article we will be talking about how to deploy Apache Airflow using Docker by keep room to scale up further. Being familiar with Apache Airflow and Docker concepts will be an advantage to follow this article.
In this post, we are going to run the sample dynamic DAG using docker. Before that, let's get a quick idea about the airflow and some of its terms. What i