KiSSFLOW and Apache Airflow are competing workflow management solutions in the business process automation market. Apache Airflow appears to have the upper hand with its robust feature set tailored for complex workflows.
Features: KiSSFLOW provides low-code automation and process management, ideal for non-technical users who require an easy integration with business tools. It also supports form creation and streamlined approvals. Apache Airflow delivers dynamic pipeline management, extensive customizability, and flexibility. It excels in integration capabilities with its Python-based architecture, making it well-suited for technical users handling complex workflows.
Room for Improvement: KiSSFLOW could enhance its features for handling more complex workflows and increase flexibility for technical customization. It also needs improvements in scalability options. Apache Airflow could benefit from a more user-friendly UI for non-technical users and streamlined deployment processes. Its open-source nature might require enhancements in customer support and simplifying its documentation for easier understanding.
Ease of Deployment and Customer Service: KiSSFLOW is cloud-based, allowing quick deployment and offering comprehensive support, beneficial for businesses seeking rapid implementation. In contrast, Apache Airflow, being open-source, requires more technical expertise but provides detailed documentation, enabling greater flexibility in deployment options.
Pricing and ROI: KiSSFLOW's competitive pricing and predictable subscription costs are attractive for small to medium-sized enterprises, providing quicker ROI. Apache Airflow may involve higher initial setup costs but offers significant long-term value for organizations requiring extensive workflow management solutions.
There is enough documentation available, and the community support is good.
Forums and community resources like Stack Overflow are helpful.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
The solution is very scalable.
I would rate the stability of the solution as ten out of ten.
Apache Airflow is stable and I have not experienced significant issues.
It is not suitable for real-time ETL tasks.
There is no dashboard for us to check all the Directed Acyclic Graphs (DAGs); a dashboard would help us analyze the work better.
I prefer using the open-source version rather than the enterprise version, which helps manage costs.
Apache Airflow is a community-based platform and is not a licensed product.
Reliability is good, and when integrated with Kubernetes, it performs better compared to on-premises environments.
Apache Airflow is an open-source platform that allows easy integration with AWS, Azure, and Google Cloud Platform.
Apache Airflow is an open-source workflow management system (WMS) that is primarily used to programmatically author, orchestrate, schedule, and monitor data pipelines as well as workflows. The solution makes it possible for you to manage your data pipelines by authoring workflows as directed acyclic graphs (DAGs) of tasks. By using Apache Airflow, you can orchestrate data pipelines over object stores and data warehouses, run workflows that are not data-related, and can also create and manage scripted data pipelines as code (Python).
Apache Airflow Features
Apache Airflow has many valuable key features. Some of the most useful ones include:
Apache Airflow Benefits
There are many benefits to implementing Apache Airflow. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the Apache Airflow solution.
A Senior Solutions Architect/Software Architect says, “The product integrates well with other pipelines and solutions. The ease of building different processes is very valuable to us. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. It's very easy to create flows.”
An Assistant Manager at a comms service provider mentions, “The best part of Airflow is its direct support for Python, especially because Python is so important for data science, engineering, and design. This makes the programmatic aspect of our work easy for us, and it means we can automate a lot.”
A Senior Software Engineer at a pharma/biotech company comments that he likes Apache Airflow because it is “Feature rich, open-source, and good for building data pipelines.”
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