Five Reasons Why You Should Check Out Apache Airflow
Apache Airflow is an open-source workflow management platform for data engineering pipelines. The project was developed in October 2014, when Airbnb needed a way to manage increasingly complex workflows. Today, Apache Airflow is used by companies big and small. Here are five reasons why it’s worth checking out:
The design of Airflow is based on code, which means that you can write code at each step. Moreover, Apache airflow supports tasks that have dependencies and create data with self-references to avoid infinite loops. Its design is based on a graph, meaning that tasks are linked and in a logical order. This makes it easy to test, maintain, and even version your workflows. In addition, you can easily scale and connect tasks to monitor different processes.
Apache Airflow is highly extensible. If you have a custom use case, Airflow will adapt to your requirements. You can add custom operators, hooks, and plugins to further enhance its functionality. Many Data Engineers contributed to the project, making it capable of solving many Use Cases. Nevertheless, it’s important to remember that Airflow is not perfect. Its community is actively working on critical features and adding more functionality to make it even better.
Airflow relies on Directed Acyclic Graphs (DAGs) to structure batch jobs. The result is a flexible pipeline. Tasks are composed of code and are separated by the DAG. Airflow operators let you put code in tasks. This flexibility allows Airflow to handle large amounts of data and process it quickly and efficiently. However, it’s vital to note that Airflow is not available on all platforms.
Unlike other open source workflow management platforms, Apache Airflow provides a graphical user interface (GUI) that allows you to monitor your workflows and see how things are progressing. The GUI lets you easily see what’s happening with your workflows, and it’s possible to run ad-hoc workloads. However, Airflow is best for pipelines that change slowly or that are related to specific time intervals. The absence of versioning means that this application is not suitable for every workload.
Apache Airflow is a powerful data pipeline platform and workflow authoring platform. It allows you to manage complex data pipelines with ease and is free from proprietary software. This software is free and open-source and is not tied to any Microsoft products. It allows you to schedule workflows on any platform. You can also use it to monitor and track metrics. Once you’ve tried it, you’ll know it’s worth your time.
Unlike other “configuration as code” workflow platforms, Airflow is written in Python. This allows you to create workflows without writing markup languages. It’s also possible to import classes and libraries. The user interface allows you to view the last execution time of your pipeline. This means that Airflow users can see how long it takes for a pipeline to process a data set. If you’re concerned about the complexity of airflow’s user interface, this tool allows you to automate the task.