Apache Airflow vs. Bonita Platform

For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.

Incentivized

Well suited for low code/no code applications centered around approval flows. It has built-in task management for users to see their pending actions, comments, statuses, etc. It has a very nice design for process flows. Less appropriate may be for generic type applications with complex screens and logic within those screens that need a lot of data to process.

Incentivized

• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

Incentivized

• Efficient and fast prototyping: a process can be modeled and tried out quickly and with low investment.
• Full stack prototyping for development and implementation allows the process to be developed and implemented as an application from the prototype. It's not just drawings and wire frames that are tossed over the wall to developers.
• Data modeling is integral from the beginning of the prototype which is appropriate for the stakeholders in the beginning.

Incentivized

• A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment.
• Better feedback on DAG parse errors in the UI or CLI.
• Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually.

Incentivized

• There is only one business data model. Even if deploying new processes does not require stopping the platform, the BDM update requires it.
• During the platform evolution often new bugs were introduced so it was risky to deploy the platform in the low minor version. For example, there were memory leaks from 7.2.0 to 7.2.3.
• The administrator portal could be improved. It is hard to look at subprocess data, for example and it is sometimes better to investigate with SQL queries. I don't like new (7.3) task list either.

Incentivized

We just renewed our license

Incentivized

For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

Incentivized

Bonita Platform has allowed us to develop GUI relatively fast using its UI Designer while being able to seamlessly integrate our business logic in Java in a BPMN2 process diagram. It gives a nice productivity boost but still requires programming know-how to be able to deliver the final solution to your business problems.

Incentivized

Engine itself is efficient enough for most cases I dealt with. It can also be extended by clustering. I have done performance tests with JMeter and only managed to induce the crash of... JMeter. If there are efficiency issues they usually concern bad design/implementation of created apps or bottlenecks in integrated systems. Although I have met two cases with efficiency loss.

1. Java 7 related PermGen saturation caused by big number of installed apps (there is no jar dependency reusal between apps option).

2. Big number of waiting event handlers in processes stresses the database.

Incentivized

It was sometimes difficult to get support on Bonita API.

Incentivized

Nothing in particular

Incentivized

Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.

Incentivized

RHPAM is maybe a very good BPM process engine but is very limited to build a complete application.

We also try site soft W4 which needs more developments to build an application. We finally choose Bonita which is easier to get ready with.

Incentivized

Respect of BPMN standard over the long term. Good enhancements by Bonitasoft for new use cases, for example the introduction of a real form editor even if it has been technically difficult to manage. Once done though, we have far greater possibility of human interaction.

Incentivized

• Most of the ETL processes were automated, cutting down on human labor.
• Apache Airflow's user interface (UI) was very informative and straightforward.
• Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand.

Incentivized

• We've had serious problems with 'automated' processes in earlier versions of Bonita (via Talend), especially with connectors. In Bonita 7 we replaced these with REST calls, hoping for better performance.
• Overall, using Bonita has not had a positive impact on our development efficiency. Moving from Talend (using Bonita 5) to Bonita 7 has improved this somewhat. Still, it remains a pain to integrate Bonita in the development and delivery process.
• Migrating from Bonita 7.0 to 7.1 has proven to be a difficult undertaking, mainly on the database level. This has cost us a lot of time and better support would be welcomed.

Incentivized