Oracle BPM and Apache Airflow compete in process management and automation. Oracle BPM appears stronger for complex business processes, whereas Apache Airflow's flexibility makes it ideal for data workflows.
Features: Oracle BPM offers business process modeling with BPMN 2.0, integration with Oracle's SOA platform, and comprehensive analytics through advanced reporting tools. Apache Airflow, as a Python-based open-source platform, provides flexibility, a wide range of connectors, and excels in orchestrating ETL processes.
Room for Improvement: Oracle BPM has a steep learning curve, lacks native microservices support, and has costly pricing. Apache Airflow's dependency on DAGs limits workflow cyclicity, its UI needs updating, and it could improve on stateful step management.
Ease of Deployment and Customer Service: Oracle BPM requires complex deployment, often on-premises, and has notable customer service delays. Apache Airflow offers cloud deployment versatility and community support, though it lacks formal technical support.
Pricing and ROI: Oracle BPM is expensive but can deliver high ROI for large enterprises, with a focus on cloud migration incentives. Apache Airflow is open-source and cost-effective, appealing to budget-conscious entities, although operational costs may arise from infrastructure needs.
| Product | Market Share (%) |
|---|---|
| Apache Airflow | 5.5% |
| Oracle BPM | 1.9% |
| Other | 92.6% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 3 |
| Large Enterprise | 24 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 7 |
| Large Enterprise | 10 |
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.”
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.
