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.
N/A
PagerDuty
Score 8.6 out of 10
N/A
PagerDuty is an IT alert and incident management application from the company of the same name in San Francisco.
We are using Sentry also for our error reporting but you can say its subset of PagerDuty it doesn't offer that many integrations but do offer error reporting realtime. Grafana also does somewhat reporting tool but still lacks those integrations and very hard for deployment as …
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.
I think PagerDuty works great for medical practices. I have used other platforms through other companies, and PagerDuty is by far the best platform. It is because of the different features it has to communicate to other staff members how the call is being handled. It is easy to learn how to use.
When getting a phone call, PagerDuty doesn't seem to allow acknowledgments of alerts through the phone, which it says it does. I constantly receive a message that it was updated by another person - when in reality, it wasn't.
Smarter notifications. If an alert was snoozed for a time, when it comes back, it sends out another alert. It should, I think, send a message asking if the alert is still an issue and give the option to close.
The UI is more complex than I would like. Part of the challenge is that most users use PagerDuty infrequently; I don't remember how I changed a policy last time. Another part of the challenge is that some users expect alerting to be a trivial feature, and are reluctant to invest any time in reading the documentation.
PagerDuty is reliable and easy to set up. It gives an effective way to notify the team about critical incidents which results in a faster turnaround time on issues. users can customize their alerts rules based on their preferences. Overall it's effective and easy to use which adds great business value.
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.
I have not use the 2 technologies for as long as I have used PagerDuty but in my opinion PagerDuty makes things a lot easier. The other tools got the job done and got alerts out but PagerDuty just seemed to make the setup for on-call alert schedules and integrations easier than the others. This isn't to say the others are difficult, just that PagerDuty was slightly better. I also have noticed that more tools have options to integrate to PagerDuty over the other tools.
![Screenshot of Similar Incidents [Apple iPad], Open Incidents [iPhone 8], On-Call Schedule Menu [Apple Watch])](https://media.trustradius.com/product-screenshots/kf/M5/L6SIK3HGD31M.png)
