Running a production Airflow cluster doesn’t need to be expensive. CNDI provides the simplest and most affordable alternative to Cloud Composer and Astronomer. Running your own cluster is a breeze. Thanks to GitOps, changing your cluster infrastructure or config is as simple as making a Pull Request.
GCP’s official managed Kubernetes Service. Running your Airflowcluster with GKE provides a clean management experience at a great cost. GKE is the most popular way to run Kubernetes on GCP and it's a great choice for new projects thanks to it's easy to use interface.
Apache Airflow is an open-source workflow orchestration platform originally developed by Airbnb. It enables users to author, schedule, and monitor complex data engineering pipelines through a user-friendly interface. With a "configuration as code" approach using Python scripts, Airflow allows developers to easily create workflows by importing libraries and classes. It utilizes directed acyclic graphs (DAGs) to handle task dependencies and scheduling, offering a streamlined alternative to legacy schedulers that relied on disjointed configurations.
Apache Airflow is designed for Data Engineers, Data Scientists, and organizations seeking a robust workflow orchestration and scheduling platform. It is suitable for those working on data pipelines, ETL (Extract, Transform, Load) processes, and complex data workflows. With its focus on programmable task dependencies, flexible scheduling, and extensive plugin ecosystem, Apache Airflow enables users to create, monitor, and manage complex workflows with ease. Whether you are working with big data, machine learning, or analytics pipelines, Apache Airflow provides the tools to efficiently orchestrate and automate your data workflows.
