Apache Airflow promotes itself as a community-based platform for managing and orchestrating programmatic workflows.
These are workflows for the data teams in charge of ETL pipelines, and a code-based system may simply play to the strengths of your tech-savvy team members.
Airflow is written in Python, and workflows are built using Python scripts. The airflow is designed using the “configuration as code” principle.
While other “configuration as code” workflow platforms exist that use markup languages such as XML, Python allows developers to import libraries and classes to help them create their workflows.
When hearing Python, we all know the greatest source for all the Python codes, Github, Github is a powerful tool with many advantages, but it requires careful tailoring to fit perfectly into any given process chain.
In this blog, we’ll provide an overview of these platforms and steps for Airflow Github Integration.
Table of Contents
Why Integrate Airflow with Github?
Here’s why you need Airflow Github Integration:
- Keeping scripts on Github will give you more flexibility because any changes to the code will be reflected and used directly from there.
- Airflow bridges a gap in its big data ecosystem by simplifying the definition, scheduling, visualization, and monitoring of the underlying jobs required to run a big data pipeline.
- Airflow is brittle and generates technical debt because it was designed for batch data. Looking up a code can also suffice in order to avoid any reliance on third parties for simple functions.
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Getting Started with Airflow Github Integration
Before diving in, have a peek at the prerequisites:
- To use a Git repository with the Python files for the DAGs, delete the default DAGs directory.
- Install Git and clone the DAG files repository.
- To choose GitHub as the DAG deployment repository, go to the Airflow Account Settings page and configure the Version Control Settings.
- See Configuring Version Control Systems for more information on configuring GitHub version control settings.
To initiate your Airflow Github Integration, follow the steps below:
- Step 1: Select Home > Cluster.
- Step 2: To change the Airflow cluster’s deployment repository, go to the Clusters page and click Edit.
- Step 3: Select the Advanced Configuration tab on the cluster details page.
- Step 4: Select GIT Repository from the Deployment Source drop-down list (under the AIRFLOW CLUSTER SETTINGS section).
- Step 5: In the Repository URL field, enter the location of the repository.
- Step 5.1: In the Repository Branch field, type the name of the branch.
- Step 6: Click Create or Update and Push to create a new Airflow cluster or edit an existing one.
Image Source: Airflow Docs
You have successfully completed the Airflow Github Integration
Benefits of Airflow Github Integration
Here are some advantages you can take from the Airflow Github Integration:
- Both are Free Source: Many data scientists would rather support and collaborate with their peers in the community than purchase commercial software. There are benefits, such as the ability to download it and begin using it immediately, as opposed to going through a lengthy procurement cycle and process to obtain a quote, submit a proposal, secure the budget, sign the licensing contract, and so on. It’s liberating to be in charge and able to choose whenever you want.
- Easy Support: The Airflow Github integration can benefit the non-developers, such as SQL-savvy analysts, who are unable to access and manipulate raw data due to a lack of technical knowledge. Even managed Airflow services like AWS Managed Workflows on Apache Airflow can get affected.
- Cloud Environment: There are options for running it in a cloud-native, scalable manner; it will work with Kubernetes and auto-scaling cloud clusters. It is essentially a Python system that is deployed as a couple of services. So, any environment that can run one or more Linux boxes with Python and a database for state management can run this environment, giving data scientists a lot of options.
What is Apache Airflow?
Image Source
Apache Airflow is a well-known open-source Automation and Workflow Management platform that can be used for Authoring, Scheduling, and Monitoring workflows.
Airflow enables organizations to write workflows as Directed Acyclic Graphs (DAGs) in the Python programming language, allowing anyone with a basic understanding of the language to deploy one.
Airflow assists organizations in scheduling tasks by specifying the flow plan and frequency. Airflow also has an interactive interface and a variety of tools for monitoring workflows in real-time.
Apache Airflow has grown in popularity among organizations that deal with large amounts of data collection, processing, and daily analysisis, IT professionals must perform a variety of manual tasks. Airflow initiates automatic workflow, reducing the time and effort required to collect data from various sources, process it, upload it, and finally create reports.
Key Features of Apache Airflow
Here are some key features of Apache Airflow:
- Simple to Use: You already know how to use Apache Airflow if you’re familiar with standard Python scripts. That’s all there is to it.
- Source Code: Apache Airflow is open-source, which means it is free to use and has a vibrant community of contributors.
- Dynamic: Python is used to define airflow pipelines, which can then be used to generate dynamic pipelines. This enables the creation of code that dynamically interacts with your data pipelines.
- Extensible: You can easily define your operators and extend libraries to fit the level of abstraction that is most appropriate for your environment.
- Scalable: Airflow has a modular architecture and orchestrates an arbitrary number of workers using a message queue. Airflow can be expanded indefinitely.
- Solid Integrations: Airflow can easily integrate with your existing services such as Google Cloud Platform, Amazon Web Services, Microsoft Azure, and many others.
What is GitHub?
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GitHub is a web-based code hosting platform for version control and collaboration in software development. Microsoft paid a whopping $7.5 billion for GitHub in 2018 because Microsoft was an active user of the platform.
Companies, coding communities, individuals, and teams all use GitHub to collaborate with others and maintain version control over their projects. GitHub is built on Git, an open-source version control system that speeds up software development.
The Enterprise editions include a wide range of third-party apps and services. GitHub provides Continous Integration, code performance, code review automation, Error Monitoring, and Task Management for Project Management.
Key Features of GitHub
GitHub assists developers in maintaining version control and accelerating the Software Development lifecycle. GitHub has the following features:
- Integrations: GitHub integrates with numerous third-party tools and software to sync data, streamline workflow, and manage projects. It also supports integration with various code editors, allowing developers to manage the repository and commit changes directly from the editor.
- Code Security: GitHub employs specialized technologies to detect and evaluate code flaws. To protect the software supply chain, development teams from all over the world work together.
- Controlling Versions: Developers can easily maintain different versions of their code on the Cloud with the help of GitHub.
- Demonstrating Skills: Developers can create new repositories and upload projects to show their expertise and experience. It allows companies to learn more about the Developer and benefits both parties during the hiring process.
- Project Administration: GitHub can be used by businesses to keep track of all Software Development progress and collaborate with team members.
Conclusion
This blog has introduced you to a simple step-by-step procedure for Airflow GitHub Integration, not only that, the blog has introduced you to these platforms and their key features respectively.
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Share your experience of learning the Airflow GitHub Integration in the comment section below! We would love to hear your thoughts.
Davor is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 100 articles on data integration and infrastructure.