Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.
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CA Workload Automation
Score 7.3 out of 10
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As the name may suggest, CA Workload Automation is CA Technologies workload automation offering.
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Pricing
Apache Airflow
CA Workload Automation
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Apache Airflow
CA Workload Automation
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Free/Freemium Version
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Premium Consulting/Integration Services
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Community Pulse
Apache Airflow
CA Workload Automation
Features
Apache Airflow
CA Workload Automation
Workload Automation
Comparison of Workload Automation features of Product A and Product B
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.
CA Workload Automation is well suited for firms having to manage and monitor multiple applications operating in multiple environments. and having SLA requirements. CA Workload Automation is less appropriate and not cost-effective for non-critical applications and/or jobs. CA Workload Automation is ideal for companies that require reliable and stable Production support for their business critical jobs to finish on time.
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.
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.
This product along with the Broadcom support team fits out needs and scheduling requirements with no issues. The support team are very aware of their product and how to support it.
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.
We selected CA Workload Automation for the ease of integration and less time to setup. It has less overhead to manage the application and is a very robust application.
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
