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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PagerDuty
Score 8.6 out of 10
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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 …
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.
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.
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.
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.
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.
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.
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.
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.
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
![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)
