How to Integrate Apache Airflow with GitHub, Easy Guide!

Apache Airflow advertises itself as a platform built by the community for managing and coordinating programmatic workflows. These are the workflows for the data teams in charge of ETL pipelines, and a code-based system may just play to the strengths of your tech-savvy team members.

Python is used to write Airflow, and Python scripts are used to build workflows. The “configuration as code” principle is used to plan the airflow. There is other “configuration as code” workflow platforms that use markup languages like XML, but Python lets developers import libraries and classes to help them build their workflows.

When we hear the word “Python,” we all know that GitHub is the best place to find Python codes. GitHub is a powerful tool with many benefits, but it needs to be carefully tweaked to fit into any given process chain.

In this blog, you’ll learn about these platforms and the steps for Airflow GitHub Integration.

Table of Content

Things You Should Know About GitHub Airflow

Why do you need airflow? Integration with GitHub:

  • When you keep scripts on Github, any changes you make to the code will be reflected there and can be used right away.
  • Airflow fills in a gap in its big data ecosystem by making it easier to define, schedule, visualize, and keep an eye on the jobs that make up a big data pipeline. Because it was made for batch data, Airflow is fragile and leads to technical debt.
  • To avoid having to rely on third parties for simple tasks, you can also just look up a code.
  • As the amount of data that businesses can collect grows, data teams are becoming more and more important for making decisions that are based on data.
  • Still, it’s hard for them to put all the data in their warehouse in one place so they can build a single source of truth.
  • Data integration is a nightmare because of broken pipelines, bad data quality, bugs, and errors, as well as a lack of control and visibility over the flow of data.
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How to Get Started with GitHub Airflow Integration

Before you start, make sure you have the following:

  • To use a Git repository with Python files for the DAGs, delete the default DAGs directory.
  • Install Git and make a copy of the DAG files repository.
  • Go to the Airflow Account Settings page and change the Version Control Settings to make GitHub the DAG deployment repository.

Follow the steps below to get your Airflow Github Integration up and running:

  • Go to Home and click on Cluster.
  • Go to the Clusters page and click Edit to change the Airflow cluster’s deployment repository.
  • On the page with information about the cluster, click on the Advanced Configuration tab.
  • Choose GIT Repository from the drop-down list next to Deployment Source (under the AIRFLOW CLUSTER SETTINGS section).
  • Type the repository’s location into the Repository URL field.
  • Type the name of the branch in the Repository Branch field.
  • Click Create or Update and Push to make a new Airflow cluster or make changes to one that already exists.

The Good Things of Integrating Airflow with Github

The Airflow Github Integration offers the following benefits:

  • They’re both free Instead of buying commercial software. Many data scientists would prefer to support and work with their peers in the community. There are advantages, such as being able to download it and start using it right away rather than having to go through a drawn-out procurement cycle and process to get quotes, submit proposals, set budgets, secure licenses, and other steps. Being in charge and having the freedom to decide whenever you want is liberating.
  • Simple Support: The Airflow Github integration can help non-developers who are unable to access and manipulate raw data due to a lack of technical knowledge, such as SQL-savvy analysts. AWS Managed Workflows on Apache Airflow and other managed Airflow services are also susceptible.
  • Cloud Environment: It is possible to run it in a scalable, cloud-native fashion; it is compatible with Kubernetes and auto-scaling cloud clusters. In essence, it is a Python system that has been set up as a few services. This environment can therefore be run in any environment that can support one or more Linux servers with Python and a database for state management, giving data scientists a wide range of options.
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  • Apache Airflow is a community-based workflow management platform.
  • A code-based approach may play to your tech-savvy team’s strengths for ETL pipeline workflows.
  • Python scripts build Airflow workflows, which “Configuration as code” guides airflow design.
  • Python allows developers to import libraries and classes to design processes, unlike XML-based “configuration as code” platforms.

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