Apache Airflow and IBM Business Automation Workflow compete in the field of workflow automation and orchestration. Based on feature flexibility and Python integration, Apache Airflow seems to hold an advantage for Python-based environments, whereas IBM Business Automation Workflow offers more extensive features for enterprise automation needs.
Features: Apache Airflow is appreciated for its programmatic flexibility, Python-based orchestration, and integration capabilities, which are ideal for data workflows. It provides a user-friendly DAG presentation for workflow management. IBM Business Automation Workflow shines with comprehensive automation features, deep IBM product integration, and a robust feature set suitable for business process management.
Room for Improvement: Apache Airflow could boost its non-technical user interface, stabilize operations for large workflows, and expand database integrations. Documentation and real-time job support need enhancements. IBM Business Automation Workflow needs a more flexible UI, simplified integration processes, less complexity, and enhanced support with improved reporting and integration options for non-IBM systems.
Ease of Deployment and Customer Service: Apache Airflow offers flexible deployment across various cloud environments, supported by a vibrant community, though official technical support is limited. Conversely, IBM Business Automation Workflow, often deployed on-premises, requires expert assistance and technical support due to its complexity, with limited cloud deployment options.
Pricing and ROI: Apache Airflow presents a cost-effective solution due to no licensing fees, though infrastructure costs are applicable, leading to a high ROI for Python orchestrations. IBM Business Automation Workflow incurs significant licensing costs, justified by its comprehensive features for large enterprises, with ROI dependent on enterprise-specific applications.
Forums and community resources like Stack Overflow are helpful.
There is enough documentation available, and the community support is good.
If the solution is readily available to IBM support team, they provide it instantly.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
We are implementing load balancing and horizontal scalability.
Apache Airflow is stable and I have not experienced significant issues.
I would rate the stability of the solution as ten out of ten.
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.
Many developments are focused on AI, LLM models, and data science.
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.
The system is fully customizable, very resilient, and fault-tolerant.
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.”
IBM Business Automation Workflow combines business process management and case management capabilities in one workflow solution. It integrates the capabilities of business process and case management into a single workflow offering. It unites information, process, and users to provide a 360-degree view of work to drive more successful optimized business outcomes. IBM Business Automation Workflow on Cloud provides the same capabilities that are offered in the IBM Business Automation Workflow offering, all within a ready-to-use, cloud-based environment that is hosted in IBM Cloud data centers and managed by IBM.
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.
