Being data processing agnostic: Airflow does not make any assumptions on how the data is processed by any of the myriads of services it uses thus it makes it easy to change and replace any of the particular services it uses (today we use Spark, to process this step, tomorrow we can change to, for example, Flink).Availability: you can set up and run Airflow on-premises, but you can also choose among multiple managed services: Astronomer, Google Cloud Composer, Amazon MWAA.In September 2021, Airflow surpassed Apache Spark as the Apache Software Foundation tool with the highest number of contributors. The tool is constantly growing and adapting to users’ needs. Recently, the community has launched Airflow 2.2, and now customizable timetables are available to the joy of all the Airflow users. Very active, constantly growing open-source community.Airflow provides a world-class, highly available scheduler that allows scaling your orchestration workloads horizontally and vertically as well - in the latest version, Airflow 2.2 enables you to leverage asyncio framework to scale asynchronous orchestration tasks almost infinitely with minimal use of computing resources Scalability: Airflow users have lots of control over their supporting infrastructure and multiple choices of executors that make scaling possible for each individual use case.A powerful Jinja engine for templating makes it possible to parametrize scripts. Due to rich visualization components, you can see all of the running pipelines and follow their progress. It makes it easy to turn schedules on and off, visualize DAG’s progress, watch pipelines in production, access logs, and resolve emerging issues at once. Rich UI: The user interface is really intuitive and a truly practical way to access task metadata.Moreover, Python allows for effortless collaboration with data scientists. Customization of complex transformations doesn’t get any simpler than this. Workflows defined as code are easier to test, maintain, and collaborate on. Code-first: Airflow and all the workflows are written in Python (although each step can be written in any language), allowing users great flexibility in defining their DAGs.The Airflow scheduler runs your tasks on an array of workers while adhering to the requirements you specify. These tasks may depend on one another, meaning that one task can only be triggered once the task it depends on has finished running. Tasks can consist of Python code you write yourself, or it may use built-in operators designed to interact with external systems. Python also makes orchestration flows easy to set up (for people familiar with Python, of course). It doesn’t get any more customizable than that. Using BashOperator and PythonOperator Airflow can run any bash or Python script. Airflow can orchestrate ETL/ELT jobs, train machine learning models, track systems, notify, complete database backups, power functions within multiple APIs, and more. The tool is a super-flexible task scheduler and data orchestrator suitable for most everyday tasks. Heavy users claim that Airflow can do anything, but more precisely, Airflow is an open-source workflow management tool for planning, generating, and tracking processes. Although they have some overlapping use cases, there are many things that only one can handle well. When digging a little deeper, we will find significant differences in the capabilities of these two tools and the programming models they support. It’s probably because it has more applications, as by nature Airflow serves different purposes than Beam. Airflow seems to have a broader reach with 23.5K GitHub stars and 9.5k forks, and more contributors. Both tools visualize the stages and dependencies in the form of directed acyclic graphs (DAGs) through a graphical user interface (GUI). Both were designed to organize steps of processing the data, to ensure that these steps are executed in the correct order. On the surface, Apache Airflow and Apache Beam may look similar. Today, we will help you choose by looking into the differences and similarities between two of our favorites: Apache Airflow and Apache Beam. That’s why it’s worthwhile to find out which workflow manager is ideally suited for your specific needs and ready to grow with you. Once you go all-in with a data orchestrator, moving to another tool would be a waste of time, money, and resources. We get it - choosing the right data management tool for a business is a virtually final decision. The need to compare data tools and to keep hunting for the perfect one seems never-ending. Streamline your data pipeline workflow and unleash your productivity, without the hassle of managing Airflow.
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