Airflow vs Apache NiFi: What are the differences?
Developers describe Airflow as "A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb". Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. On the other hand, Apache NiFi is detailed as "A reliable system to process and distribute data". An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
Airflow can be classified as a tool in the "Workflow Manager" category, while Apache NiFi is grouped under "Stream Processing".
Some of the features offered by Airflow are:
- Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
- Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
- Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.
On the other hand, Apache NiFi provides the following key features:
- Web-based user interface
- Highly configurable
- Data Provenance
Airflow is an open source tool with 13.3K GitHub stars and 4.91K GitHub forks. Here's a link to Airflow's open source repository on GitHub.
According to the StackShare community, Airflow has a broader approval, being mentioned in 98 company stacks & 162 developers stacks; compared to Apache NiFi, which is listed in 10 company stacks and 12 developer stacks.