{"id":291,"date":"2026-04-09T06:46:46","date_gmt":"2026-04-09T06:46:46","guid":{"rendered":"https:\/\/blog.ngocha.biz\/?p=291"},"modified":"2026-04-09T06:46:46","modified_gmt":"2026-04-09T06:46:46","slug":"apache-airflow-on-kubernetes","status":"publish","type":"post","link":"https:\/\/blog.ngocha.biz\/?p=291","title":{"rendered":"How to Deploy Apache Airflow on Kubernetes (Production Guide)"},"content":{"rendered":"

Apache Airflow is an open source workflow orchestration tool used for data, workflows. And many organizations today, run it on Kubernetes for scalability and flexibility.<\/p>\n

Also, Apache Airflow 3<\/a> is total redesign that expands its capabilities to support complex AI, ML<\/strong>, and near real-time data workloads.<\/p>\n

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Key Insight <\/strong><\/b>\ud83d\udca1
80,000 organizations use Airflow, with over 30% of users running MLOps<\/a> workloads and 10% using it for GenAI workflows<\/div>\n<\/div>\n

In this blog we covered Apache Airflow on a Kubernetes and configure it to automatically sync DAGs from GitHub.<\/p>\n

At the end of this blog, you will have learned<\/p>\n

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  1. What is Apache Airflow<\/li>\n
  2. What is a DAG<\/li>\n
  3. Understand Airflow executors.<\/li>\n
  4. Installation of Apache Airflow on Kubernetes<\/li>\n
  5. Configure GitSync for DAGS & Kubernetes executors.<\/li>\n
  6. Insights of few Airflow Day 2 operations and more..<\/li>\n<\/ol>\n

    Lets get started.<\/p>\n

    What is Apache Airflow?<\/h2>\n

    Apache Airflow is an open source application that helps build and manage complex workflows and data pipelines<\/strong>.<\/p>\n

    A data pipeline is a series of steps to collect data from sources, process it, and then store it in a storage. These steps include collecting, cleaning, transforming, filtering, and storing data. (ETL Process)<\/p>\n

    In the data pipeline, each individual step (collecting, cleaning etc) is called a Task. <\/strong>A Task is simply a single unit of work<\/strong> that Airflow executes.<\/p>\n

    Now, these tasks don’t run individually. They need to run in a specific order. Why? <\/em>Well, the logic is simple. You cant clean data before collecting it, and you can’t store data before transforming it. <\/p>\n

    This is where a DAG (Directed Acyclic Graph)<\/strong> comes in. It is a foundational concept in Data engineering and AI\/ML workflows. It is used to define how tasks depend on each other<\/strong> and in what order they should run.<\/p>\n

    Directed Acyclic Graph (DAG)<\/h2>\n

    A DAG is basically a definition of your entire pipeline written in Python<\/strong>. It describes all the tasks and the order in which they should run<\/strong>. Think of it as the blueprint of your data pipeline . Here is what DAG means.<\/p>\n