Apache Airflow vs ProcessMaker comparison

Apache Airflow is an open-source workflow management system (WMS) that is primarily used to programmatically author, orchestrate, schedule, and monitor data pipelines as well as workflows. The solution makes it possible for you to manage your data pipelines by authoring workflows as directed acyclic graphs (DAGs) of tasks. By using Apache Airflow, you can orchestrate data pipelines over object stores and data warehouses, run workflows that are not data-related, and can also create and manage scripted data pipelines as code (Python). 

Apache Airflow Features

Apache Airflow has many valuable key features. Some of the most useful ones include:

• Smart sensor: In Apache Airflow, tasks are executed sequentially. The smart sensors are executed in bundles, and therefore consume fewer resources.

• Dockerfile: By using Apache Airflow’s dockerfile feature, you can run your business’s Airflow code without having to document and automate the process of running Airflow on a server. 

• Scalability: Because Apache Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers, you can easily scale it. 

• Plug-and-play operators: With Apache Airflow, you can choose from several plug-and-play operators that are ready to execute your tasks on many third-party services.

Apache Airflow Benefits

There are many benefits to implementing Apache Airflow. Some of the biggest advantages the solution offers include:

• User friendly: Using Apache Airflow requires minimal python knowledge to get started.

• Intuitive user interface: The Apache Airflow user interface enables you to visualize pipelines running in production, monitor progress, and also troubleshoot issues when needed.

• Easy integration: Apache Airflow can easily be integrated with cloud platforms (Google, AWS, Azure, etc).

• Visual DAGs: Apache Airflow’s visual DAGs provide data lineage, which facilitates debugging of data flows and also aids in auditing and data governance. 

• Flexibility: Apache Airflow provides you with several ways to make DAG objects more flexible. At runtime, a context variable is passed to each workflow execution, which is quickly incorporated into an SQL statement that includes the run ID, execution date, and last and next run times.

• Multiple deployment options: With Apache Airflow, you have several options for deployment, including self-service, open source, or a managed service.

• Several data source connections: Apache Airflow can connect to a variety of data sources, including APIs, databases, data warehouses, and more.  

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the Apache Airflow solution.

A Senior Solutions Architect/Software Architect says, “The product integrates well with other pipelines and solutions. The ease of building different processes is very valuable to us. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. It's very easy to create flows.”

An Assistant Manager at a comms service provider mentions, “The best part of Airflow is its direct support for Python, especially because Python is so important for data science, engineering, and design. This makes the programmatic aspect of our work easy for us, and it means we can automate a lot.”

A Senior Software Engineer at a pharma/biotech company comments that he likes Apache Airflow because it is “Feature rich, open-source, and good for building data pipelines.”