CMW Tracker and Apache Airflow are competitive products in the workflow management space. Apache Airflow has an edge due to its extensive feature set and recognized value, although CMW Tracker's pricing and support are appealing.
Features: CMW Tracker offers intuitive automation tools, project management capabilities, and user-friendly workflow design. Apache Airflow specializes in extensive workflow orchestration, complex scheduling options, and supports Python-based customizations.
Room for Improvement: CMW Tracker could enhance its technical depth for managing data workflows, expand integration capabilities, and offer advanced automation features. Apache Airflow may benefit from streamlining its setup process, improving user interface simplicity, and enhancing customer support options.
Ease of Deployment and Customer Service: CMW Tracker provides a straightforward deployment and strong customer service, ideal for businesses with limited technical resources. Apache Airflow, requiring more technical expertise, offers a self-service support model better suited for technically inclined users.
Pricing and ROI: CMW Tracker delivers a cost-effective solution with rapid deployment, allowing a quicker ROI. Apache Airflow may involve higher setup costs due to complex deployment but potentially offers significant ROI for managing intricate data workflows.
There is enough documentation available, and the community support is good.
Forums and community resources like Stack Overflow are helpful.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
Apache Airflow is stable and I have not experienced significant issues.
I would rate its stability at nine out of ten.
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.
The start date in Apache Airflow is also confusing because it is not straightforward. If you want it to start today, you should give tomorrow's date.
I prefer using the open-source version rather than the enterprise version, which helps manage costs.
It is a sub-feature and not an individual purchase.
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.
We can create notifications for successful or failed tasks, providing a practical way to monitor our workflows.
| Product | Market Share (%) |
|---|---|
| Apache Airflow | 3.5% |
| CMW Tracker | 0.9% |
| Other | 95.6% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 3 |
| Large Enterprise | 24 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 4 |
| Large Enterprise | 3 |
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.”
CMW Tracker offers a browser-accessible workflow management platform supporting low code development with features such as team collaboration and excellent customer service. Users value its affordability and ability to enhance transparency and standardize processes.
CMW Tracker streamlines project management by allowing non-technical users to create workflows, manage tasks, and synchronize tools efficiently. This platform emphasizes ease of deployment and modification, bringing transparency and clarity to processes while enabling workflow automation. Its user-friendly design, robust analytics, and cost-effectiveness make it a favored choice for organizations needing efficient workflow management.
What are the main features of CMW Tracker?CMW Tracker is widely implemented across industries requiring efficient workflow management, particularly in settings where collaboration and process transparency are critical. Organizations benefit from its ability to standardize operations, reduce IT dependency, and enhance service delivery by enabling seamless communication among dispersed team members working on joint projects.
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.
