Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
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PagerDuty
Score 8.6 out of 10
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PagerDuty, Inc. (NYSE:PD) provides digital operations management. Serving organizations of all sizes, PagerDuty aims to help them deliver a perfect digital experience to their customers, every time.
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.
PagerDuty is well-suited for teams or companies that need immediate response, such as production outages, server downtime, failed deployments, API failures, or critical infrastructure alerts. For example, if any company is doing work that requires immediate attention to any problem that arises due to a delay, it means the company loses money; PagerDuty would be the best fit for that company.
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.
PagerDuty feels like something you can absolutely rely on... because in the rarest case where an alert is not acknowledged by the relevant agent, the alert automatically is escalated to our TL, which saves any possible errors or misses.
In terms of integration, I would rate it a 9.4 as it's absolutely seamless with Microsoft Teams or emails, ultimately resulting in a reduction of errors in work, which I greatly appreciate about PagerDuty.
In some cases, when an account requires input from multiple agents, PagerDuty makes sure to notify each of the relevant ones.
Other than this, sometimes when we have new joinings, it becomes easy for us to train them because every alert or response is recorded or logged. Because of this feature, we are able to check our past actions as well, so that a good feature about PagerDuty.
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 a beginner, it is quite confusing to get hold of the tool. The learning curve is steep.
I'd like to see more AI integration features. Other tools we use like Jira, GitHub are easily integrated and used from AI tools like Claude
It would be great if I'm able to add my rota from PagerDuty to be calendar. There are weeks I actually did not realise I was on-call (missed the email from PagerDuty :()
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.
There were more features and more ways to contact the provider. Ring Central would send out a call. I don't remember sending an email or text when I was paged. It seemed it was only an app on my phone and not located on my computer, where I could access it when I was working on the computer.
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
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