Compare Activiti vs. Apache Airflow

Businesses are increasingly seeking solutions for automation challenges within their distributed, highly scalable, and cost-efficient infrastructures. Activiti stands out as a premier lightweight, Java-focused open-source BPMN engine that effectively addresses the practical needs of process automation. The introduction of Activiti Cloud marks a transformative step in business automation, providing a suite of cloud-native components that are engineered to operate seamlessly on distributed infrastructures. With immutable, scalable, and user-friendly process and decision runtimes, it integrates effortlessly with your existing cloud-native setup. Additionally, it features a scalable, storage-agnostic, and extensible audit service alongside a similarly designed query service. This platform also simplifies system-to-system interactions to ensure they can effectively scale across distributed environments. Furthermore, it includes a scalable application aggregation layer, as well as secure WebSocket and subscription handling capabilities within its GraphQL integration, ensuring robust and reliable connectivity. Such comprehensive features position Activiti Cloud as an essential tool for modern enterprises navigating the complexities of automation in the cloud era.

Airflow is a community-driven platform designed for the programmatic creation, scheduling, and monitoring of workflows. With its modular architecture, Airflow employs a message queue to manage an unlimited number of workers, making it highly scalable. The system is capable of handling complex operations through its ability to define pipelines using Python, facilitating dynamic pipeline generation. This flexibility enables developers to write code that can create pipelines on the fly. Users can easily create custom operators and expand existing libraries, tailoring the abstraction level to meet their specific needs. The pipelines in Airflow are both concise and clear, with built-in parametrization supported by the robust Jinja templating engine. Eliminate the need for complex command-line operations or obscure XML configurations! Instead, leverage standard Python functionalities to construct workflows, incorporating date-time formats for scheduling and utilizing loops for the dynamic generation of tasks. This approach ensures that you retain complete freedom and adaptability when designing your workflows, allowing you to efficiently respond to changing requirements. Additionally, Airflow's user-friendly interface empowers teams to collaboratively refine and optimize their workflow processes.