Apache Airflow - Free & Open-source Tool For Your Workflow Management (Review 2021)
Most businesses have to deal with different workflows. It includes processes like collecting data from multiple databases, preprocessing it, uploading it, and reporting it. Consequently, it would be great if the daily tasks just automatically trigger on defined time. Moreover, it’s also important that the process of the tasks execute in order.
Apache Airflow is one such tool that can be very helpful for you to build and maintain your own workflow.
Apache Airflow is an open-source workflow management platform to programmatically author, schedule, and monitor workflows. Using it, you can easily schedule and run your complex data pipelines. Apache Airflow, or simply Airflow, will ensure that the execution of each task of your data pipeline is in the correct order and gets the required resources.
Starting at Airbnb in October 2014, Airflow was initially a solution to manage the company’s increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface. From the beginning, the project was open-source, becoming an Apache Incubator project in March 2016 and a Top-Level Apache Software Foundation project in January 2019
Airflow uses Directed Acyclic Graphs (DAGs) to manage the task workflow. Airflow’s rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. It connects with multiple data sources and can send an alert via email or Slack when a task completes or fails. Due to Airflow being distributed, scalable, and flexible, it’s suitable to handle the orchestration of complex business logic.
What Is Apache Airflow Used For?
Apache Airflow is used for the scheduling and orchestration of data pipelines or workflows. Orchestration of data pipelines refers to the sequencing, coordination, scheduling, and managing of complex data pipelines from diverse sources. These data pipelines deliver data sets that are ready for consumption either by business intelligence applications and data science, machine learning models that support big data applications.
Generally speaking, you can use Apache Airflow to:
- Define, schedule, and monitor workflows
- Orchestrate third-party systems to execute tasks
- Provide a web interface for excellent visibility and management capabilities
How Does Apache Airflow Work?
Airflow uses directed acyclic graphs (DAGs) to manage workflow orchestration. Tasks and dependencies are defined in Python or external scripts. Then, Airflow will manage the scheduling and execution. It runs tasks, which are sets of activities, via operators, which are templates for tasks. Developers can create operators for any source or destination.
DAGs can be run either on a defined schedule (e.g. hourly or daily) or based on external event triggers (e.g. a file appearing in Hive). Moreover, it ensures that all tasks went through the right order and completed the requirements.
The previous DAG-based schedulers like Oozie and Azkaban tended to rely on multiple configuration files and file system trees to create a DAG. However, in Airflow, DAGs can often be written in one Python file.
Read more about Apache Airflow’s core concept.
What Are The Features of Apache Airflow?
No more command-line or XML black-magic. Now, you can use standard Python features to create your workflows. You can also include date-time formats for scheduling and loops to dynamically generate tasks. Therefore, you are able to maintain full flexibility when building your workflows.
Modern User Interface
With Airflow, you can monitor, schedule, and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. The graphical interface shows you right away through administrative tasks such as workflow management and also user management. Moreover, it provides numerous visualizations of the structure and status of a workflow. It also applies to the evaluation of execution times. Hence, it’s easy to get a good overview of the current status of workflow runs.
With the smart sensor in Airflow, tasks are executed sequentially when they have not met certain conditions. It happens if you set a variety of conditions at fixed intervals then the smart sensor will check whether the tasks have met the condition or not. Therefore, they will not continue the workflow until they are met the conditions set. In addition to that, the sensors are executed in bundles, therefore, consume fewer resources.
Airflow provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure, and many other third-party services. As a result, Airflow is easy to apply to current infrastructure and extends to next-gen technologies.
Anyone with Python knowledge can deploy a workflow. A typical Airflow deployment is often to simply deploy it on a single server. However, you can get it to run very well inside a Docker container, even on your laptop, to support the local development of pipelines.
Airflow does not limit the scope of your pipelines. You can use it to build ML models, transfer data, manage your infrastructure, and more. Moreover, you can also scale it up to very large deployments with dozens of nodes running tasks in parallel in a highly available clustered configuration.
Wherever you want to share your improvement you can do this by opening a PR. It’s simple as that, no barriers, no prolonged procedures. Airflow has many active users who willingly share their experiences.
Why Should I Use Apache Airflow?
Apache Airflow is open source and has a large community, therefore, it’s easy if you want to customize it to meet your company’s individual needs. Moreover, it is a great tool for managing various dependencies between tasks. After learning Airflow, you will find out that it provides intuitive and simple monitoring and managing interface which makes work fast and easy. If you need an effective and easy-to-use tool for managing workflows, then Airflow may be the proper tool for your company.
Airflow has 4 key principles that make it a great choice workflow tool for you.
Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment.
Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.
Is Apache Airflow Free?
Yes, Airflow is free and open-source, licensed under Apache License 2.0.
How to Use Apache Airflow?
- Completely free
- Has big community
- Pure Phyton
- Extensible with plugins and adding custom operators
- Wide range of operators
- No versioning of data pipelines
- Not friendly for new users
- Setting up Airflow architecture for production is not easy
- Lack of data sharing between tasks
What Are the Alternatives to Apache Airflow?
There are other tools to create a customized workflow just like in Apache Airflow. The following tools can be the Apache Airflow alternatives:
Apache Airflow, or Airflow, is a free tool to easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status. You can use Airflow to build ML models, transfer data, and manage infrastructure. The tool is pure phyton so you don’t need to use command-line or XML anymore to create your own workflows.
Moreover, the user interface of Airflow is quite modern with a few visualizations so you can get a better overview of the current status of your workflows. Even though Airflow is not user-friendly for new users, but you still can get plenty of knowledge base due to its big community and open-source environment.
- Apache Airflow Official Website
- The Ultimate Guide to Apache Airflow – Qubole
- New Features of Apache Airflow 2.0 for Your Workflow Management – NextLytics
- 6 Best Alternatives to Apache Airflow – Analytics India Mag
- Musings on the Pros and Cons of Apache Airflow – Bio-IT World
- Is Apache Airflow Good Enough for Current Data Engineering? – Towards Data Science