Apache Airflow vs. Portainer

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.

Incentivized

Many developers, especially lesser experienced developers, don't have a really good background in setting up containers from the command line. Portainer is invaluable to them. Giving a UI to them gives them much more confidence and allows them to learn properties and capabilities of containers under far less stress. On the flip side of this, giving then a UI on a production system can lead to chaos...never give junior developers access to production servers.

Incentivized

• 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.

Incentivized

• Separating server maintenance with application development, providing a clear user interface for developers who don't want to worry about the underlying server.
• RBAC for container deployment linked to a SAML IDP, not something particularly easy in a native Docker instance but point and shoot in Portainer, allowing the use of Azure / Okta etc to provide user access.
• Image management with multiple repositories is super clear and reduces incidents

Incentivized

• 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.

Incentivized

• When setting up static IPs for a new container, having the used ones for a specific network at hand would be cool or something like a mini IPAM of some sort...
• Using the developertools to see "oh, it's a 40x or 500" when something doesn't seem to load because the ui just states "Didn't work" is kinda annoying. expandable toasts or something would be nice.

Incentivized

It is an excellent tool.

In terms of deploying Airflow, you do need some level of technical expertise to set up the deployment, such as knowledge about Kubernetes if you are setting up the Apache Airflow cluster on Kubernetes. Writing DAGs is straightforward, as examples are available for different operators, and the Apache Airflow documentation is also well-maintained.

Incentivized

Accessibility for Non-Experts: even with some people having a bit longer on-boarding it is still very simple Quick setup is insanely useful, we can get it running in 10 seconds after installing docker Portainer has once again super clean UI and is very user friendly. Deployment/monitoring and management are super easy. I can tell just from a glance if something is out of date (watching at you Watchtower not doing your job for some reason)

Incentivized

One of their staff members jumped on a video call immediately with me and led me through the problem and solution during a quick session of screen sharing. In this day and age that is above and beyond, especially when it comes to software. It took approximately 5-10 minutes to diagnose and fix, including pleasantries!

It is really easy. Just follow the documentation.

Incentivized

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.

Incentivized

Portainer takes the cup in terms of usability and features. It is also more useful for smaller deployments, whereas Kubernetes in our opinion and experience, could probably be more suited to certain other use cases. Portainer is also a fresh feel among all the preexisting container management solutions and brings positivity and a new breeze in the industry.

• 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
• Donot use it if you have very less usecases

Incentivized

• Increased productivity: Portainer's user-friendly interface and streamlined container management can help increase the productivity of IT teams.
• Cost savings: By simplifying container management, Portainer can help reduce the time and resources required to manage container environments, potentially leading to cost savings.

Incentivized