Setting Up Data Pipelines Using Apache Airflow on Kubernetes

Setting Up Data Pipelines Using Apache Airflow on Kubernetes

Learn how to Setting Up Data Pipelines Using Apache Airflow on Kubernetes. Deploying a Scalable, Production-Ready Airflow on Kubernetes. Apache Airflow is an open-source workflow orchestration tool for scheduling and monitoring data pipelines.

Deploying a Scalable, Production-Ready Airflow on Kubernetes

Introduction

Apache Airflow is an open-source workflow orchestration tool for scheduling and monitoring data pipelines. It can be also used for machine learning workflows and other purposes. Kubernetes is a de facto orchestration tool for scheduling containerized applications. Airflow can utilize the ability provided by Kubernetes to scale up/down the worker depending on the workload.

Last year, I have deployed Apache Airflow on Kubernetes. Bitnami provides an Apache Airflow Helm chart which makes me easy to deploy and scale. It mainly consists of three parts: webserver, scheduler, and worker.

airflow kubernetes software-development data-engineering

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