Apache Airflow vs. AppFog (discontinued)

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

It was very good to use in small scale projects. Considering the high end projects with many instances and multi-platform architectures, it is better to test before the application is deployed. I think few of the questions can be general - who are the system users and what size is the application focussing on? How much resources are required? Will the application require any additional services?

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

• Quick deployment of pre-built virtual machines
• Some of the virtual machines also are readily available with a pack of softwares
• Good Stability. Using it as a student, I have never experienced any downtime issues with the projects deployed on AppFog.

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

• Though it is good and easy for developers, it lacks operational activities and monitoring tools.
• It is easy to deploy WAR on AppFog with its console but sometimes it can lack on the performance and feasibility.

Incentivized

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.

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

Primarily because it used to have a good free tier earlier, which it does not anymore. It's simple, and things are available to use. Compared to it's competitors, it does has less features, but that kind of acts in its favor. That adds to the simplicity, and ease of use for a new user.

Incentivized

• 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

• Our project was deployed with good efficiency and easily accessible.
• The platform was much recommeded across the groups and peers.

Incentivized