Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
JBoss EAP is subscription based/open source platform. It's very reliable and great for deploying high transaction Java based enterprise applications. It integrates well with third party components like mod_cluster and supports popular Java EE web-based frameworks such as Spring, Angular JS, jQuery Mobile, and Google Web Toolkit.
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.
MOD_CLUSTER integration. JBoss EAP integrates pretty well with mod_cluster. This is an intelligent load balancer especially useful in highly clustered environments.
Supports enterprise-grade features such as high availability clustering, distributed caching, messaging etc.
Supports deployment in on-premise, virtual and hybrid cloud environments.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
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.
JBoss EAP is lightweight and doesn't really consumes much physical resources. It's high performing and suites well for high transaction Java enterprise applications. The out of box performance settings are not really great and you will have to configure the settings to suite your environment to leverage it's full benefits.
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
Jboss EAP is easy to deploy and configure. This lead to lower cost and faster delivery.
Even though we have large number of machines running JBoss, we have only two Jboss Administrators. It doesn't requires too much administration and maintenance on daily basis and reduces number of administrators required for large implementations.
