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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Trellix Cloud Workload Security
Score 6.8 out of 10
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Trellix Cloud Workload Security (formerly from McAfee) to give users a real-time view of running workloads through detecting workloads and pods. This product can integrate with both public and private cloud infrastructures.
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Pricing
Apache Airflow
Trellix Cloud Workload Security
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Offerings
Pricing Offerings
Apache Airflow
Trellix Cloud Workload Security
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
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Community Pulse
Apache Airflow
Trellix Cloud Workload Security
Features
Apache Airflow
Trellix Cloud Workload Security
Workload Automation
Comparison of Workload Automation features of Product A and Product B
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.
Suitable scenario: The McAfee Cloud Workload Security console has helped me a lot in saving physical resources. We no longer have to have a server and spend resources to maintain the console, everything is in the cloud. Unsuitable scenario: After the change to the cloud the McAfee Cloud Workload Security reduced many things that were made very easy when it was on a physical server. Active directory server integration with the console is a bit tricky. That should be improved and be somewhat more friendly when connecting to be able to migrate user machines.
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.
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.
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.
We use McAfee Endpoint Security on user PCs and I must say that it is an excellent antivirus. The alerts we have had have been resolved without any problem. I totally recommend it.
