Apache Airflow vs. ignio AIOps

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.

Incentivized

It's good for issue resolution, user access request automation, standard report generation, health checks, executing self-healing as configured in the attributes. Currently not good at real-time monitoring to trigger an action. Health checks have to be on a scheduled basis.

• In charge of the ETL processes.
• As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.

Incentivized

• ignio handles 100+ use cases covering the entire organization including applications and infrastructure.
• Centralize the dashboard to view and executed the health of systems in our environment.
• It handles CA services desk Incidents and requests. Using automated tools with power shell scripts.
• ignio event management helps the organization to manage the alerts well and make an informed decision.

• they should bring in some time based scheduling too not only event based
• they do not store the metadata due to which we are not able to analyze the workflows
• they only support python as of now for scripted pipeline writing

Incentivized

• There is a lot more the desktop tool can do. For example, we need to apply an upgrade to get the tool to talk to our infrastructure while employees are working from home. The tool was initially installed with the assumption that the desktops would be in UserLand. Instead after COVID-19 the desktop/laptops have been used for over a year on people's home networks. As of right now, we have to sync when the devices are connected to VPN. Moving forward with the upgrade, we will be getting this data over TLS when they are connected to the untrusted networks.
• The concept of ignio AlOps requires OCM efforts within most operational teams. This isn't necessarily the fault of the tool itself, but when implementing ignio, or any AIOps tool, the team will get a lot of pushback as an outside team is centralizing the operational improvements. The tool should have a centralized intake process that will allow the collection, ranking, and management of automation opportunities. ignio AlOps should then simulate the proposed efficiencies from implementing something within the backlog. Right now a lot of local teams are having a hard time getting on the same page as the enterprise teams, and a common methodology for prioritizing (even if overly simplistic) would go a long way to enterprise planning.
• These tools are very new and things get added to them all the time. There should be a way for the product's stakeholders and process owners to understand the additional value ignio AlOps is gaining over time.

It is a very good product and it helps our organization.

ignio AIOps version upgrades were a heavy lift. Having to learn a new language versus an industry standard language took time. More consideration on overall internal long-term support needs to be determined.

Incentivized

It was up than Dynatrace

Incentivized

We had performance issues in older version butterfly. But the new version cheetah is simply amazing

We have built a healthy relationship with the vendor support team throughout the implementation phase, all incidents raised were resolved within the SLA without a fail

Implementation team has provided necessary training & enablement.

Online training materials are shared by the implementation team and it was good.

I am happy with the way team has implemented and shared the product for our organization. However, would like to see it get extended to the other line of business too.

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.

Incentivized

Self HEAL OOB Capabilites

Quite Scalable!

• A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
• Allowed us to implement complex data pipelines easily and at a relatively low cost

Incentivized

• ignio has had a positive impact on our organization by saving 7,000+ hours within Operations and automatically resolving 84% of our service requests.
• ignio has increased our alert coverage by over 60%.