Apache Airflow vs. Apache Camel

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

Message brokering across different systems, with transactionality and the ability to have fine tuned control over what happens using Java (or other languages), instead of a heavy, proprietary languages. One situation that it doesn't fit very well (as far as I have experienced) is when your workflow requires significant data mapping. While possible when using Java tooling, some other visual data mapping tools in other integration frameworks are easier to work with.

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

• open source and a great set of component feature set - always latest features available for integration
• works well with spring boot
• great community and support for any kind of workflow
• based on enterprise integration patterns which helps our developers achieve integration tasks with all kinds of API services

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

• Some of the documentation is a little sparse. In particular, its TCP-based routes use an underlying Netty server, and the interactions between Netty's decoder capabilities and Apache Camel's routing/handler capabilities can be a little muddy at times. In general it is clear which routes and endpoints are the more frequently used and which haven't been given as much attention.

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

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

Apache Camel has been the integration framework of choice, but I was not the person to make the decision to use it. Compared to other competing products like Tibco Business Works, etc., it is free and open source and its licensing policy is acceptable to the management of Cox.

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

• Very fast time to market in that so many components are available to use immediately.
• Error handling mechanisms and patterns of practice are robust and easy to use which in turn has made our application more robust from the start, so fewer bugs.
• However, testing and debugging routes is more challenging than working is standard Java so that takes more time (less time than writing the components from scratch).
• Most people don't know Camel coming in and many junior developers find it overwhelming and are not enthusiastic to learn it. So finding people that want to develop/maintain it is a challenge.

Incentivized