Apache Airflow and Boomi AtomSphere Flow are distinguished workflow automation platforms in the field of process automation. Apache Airflow seems to have the upper hand for data-heavy tasks with its advanced orchestration capabilities, while Boomi AtomSphere Flow dominates in integration capabilities, offering a more user-friendly setup.
Features: Apache Airflow possesses capabilities such as complex orchestration, effective scheduling, and dynamic pipeline configuration, proving ideal for robust data processing environments. The system supports extensive configuration thanks to its open-source nature. Boomi AtomSphere Flow shines through its cloud-native environment, low-code development functionality, and real-time integrations, supporting rapid deployment scenarios.
Room for Improvement: Apache Airflow requires enhancements in user accessibility and reducing dependency on technical skills, which could limit its audience. Additionally, improved integration ease with non-Python systems would broaden applicability. Better visualization tools could also enhance user experience. Boomi AtomSphere Flow needs improved data-heavy task handling, an extension of its feature set to support more complex automation scenarios, and enhanced configurability for technical users seeking deeper customization than currently available.
Ease of Deployment and Customer Service: Apache Airflow's open-source model demands significant technical know-how and is more suited to organizations with a dedicated technical team. In contrast, Boomi AtomSphere Flow offers streamlined deployment with extensive managed services and proactive customer service, easing the setup burden and facilitating smoother operation for users.
Pricing and ROI: Apache Airflow's open-source framework boasts a lower initial setup cost, making it cost-effective for firms equipped to harness its full potential in-house. Boomi AtomSphere Flow necessitates higher initial spending due to licensing fees but yields a quicker ROI via its seamless integration and robust support services, appealing to businesses prioritizing ease of use and rapid deployment.
Forums and community resources like Stack Overflow are helpful.
There is enough documentation available, and the community support is good.
It lacks clarity on how to reach out, write emails, or understand responsiveness.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
I would rate the scalability as nine out of ten, as it effectively scales to meet our clients’ needs.
Apache Airflow is stable and I have not experienced significant issues.
I would rate the stability of the solution as ten out of ten.
The solution maintains high stability, which makes it reliable.
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.
We are particularly interested in exploring AI features more thoroughly, focusing on next-generation enhancements like ChatGPT.
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.
The pricing is considered a bit expensive yet not excessively so, particularly when compared to similar solutions like MuleSoft.
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.
The most valuable features include the ease of designing Boomi flows and the significant cost savings and reduced time to market they offer our clients.
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.”
Boomi Flow is a modern, cloud-native service for creating customer journeys and automating simple and sophisticated workflows that accelerate your business outcomes.
We monitor all Business Process Management (BPM) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
