

Appian and Apache Airflow compete in the business process and data workflow management categories. Appian is often preferred for its rapid ROI and effective business outcomes, while Apache Airflow is favored for its robustness as a cost-effective open-source solution.
Features: Appian offers a low-code development approach, robust BPM integration, and ease of cross-platform deployment. Apache Airflow is customizable, utilizes Python for defining workflows, and provides excellent data orchestration capabilities.
Room for Improvement: Appian users wish for better UI customization, enhanced DevOps integration, and flexibility. Apache Airflow needs improvement in versioning, UI support for non-technical users, and simplification of integration processes.
Ease of Deployment and Customer Service: Appian supports diverse deployment methods with on-premises and cloud configurations and is known for responsive customer service. Apache Airflow has strong community support due to its open-source nature but lacks professional support compared to Appian. Both can deploy on public and private clouds.
Pricing and ROI: Appian's pricing may be higher but is offset by rapid ROI via process automation. It offers flexible pricing models often out of reach for smaller enterprises. Apache Airflow, being open-source, eliminates licensing fees but comes with potential operational costs, making it appealing to organizations capable of self-managing open-source aspects.
Using Appian is saving us five full-time employees, which is significant since we currently have only four team members.
They see return on investment in terms of cost savings, time savings, more efficient processes, and more efficient employees.
Appian is very efficient, allowing us to build a lot of applications within a financial year, making it cost-effective.
There is enough documentation available, and the community support is good.
Forums and community resources like Stack Overflow are helpful.
We can see what bugs are currently being addressed and what fixed versions are released in the official Git repository.
I would give Appian's customer support 10 out of 10 due to their next-level support.
Their customer service is responsive, and the team is very prompt for support.
The technical support for Appian rates as 10 out of 10 because they have a great support team.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
There is an auto-scaling feature called KEDA, which is Kubernetes event-driven auto-scaling offered by Apache Airflow.
On a scale of one to 10, Appian rates as a nine for scalability.
Our volume has increased by 20% in the two years since using Appian, and it can handle the increased volume effectively.
Initially, without much coding, I can easily handle five thousand records.
I would rate the stability of the solution as ten out of ten.
Apache Airflow is stable and I have not experienced significant issues.
I would rate its stability at nine out of ten.
We have tested Appian during peak usage and off-peak times, and we have not experienced any issues such as lagging or system disruptions.
It depends on how it has been designed and how it has been configured.
The stability of Appian would rate as nine, as it's a stable environment.
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.
If we desire to add custom messengers or a rest API, those options are unavailable.
It has room to improve for use cases where the users are public facing, where anonymous users could come to a site and run a business workflow or interact with some data.
Appian can be improved by adding a geo-location tagging feature, which would be really helpful for identifying remitter addresses.
If there is a very complex process that includes a lot of data transitioning and memory-centric processes, it consumes a lot of memory.
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.
Appian provides value for money, is easy to use, has a straightforward setup procedure, and offers great support from the Appian team.
On the pricier side, both Appian and Pega are enterprise-level solutions, placing them on the slightly higher side.
The pricing of Appian is based on the number of users and generally ranges from 70 to 100 USD per user per month.
Apache Airflow is an open-source platform that allows easy integration with AWS, Azure, and Google Cloud Platform.
Reliability is good, and when integrated with Kubernetes, it performs better compared to on-premises environments.
The positive impact and benefits I have seen from using Apache Airflow on my company is that since it is an open-source tool and not licensed, we can get that tool as open source and integrate and modify it as much as we can.
The zero-code integration feature is remarkable, allowing for ease of data transfer and workflow enhancement.
Appian is aiding in leveraging AI technologies in multiple ways: one way is for developers, as they make development efficient and quick by enabling developer co-pilots across various phases of the application, which helps design Appian quickly and provides suggestions along the way.
After switching to Appian, it can extract data from MT103, eliminating the need for manual data entry.
| Product | Mindshare (%) |
|---|---|
| Apache Airflow | 2.8% |
| Appian | 3.3% |
| Other | 93.9% |

| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 4 |
| Large Enterprise | 24 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 9 |
| Large Enterprise | 44 |
Apache Airflow is a Python-based platform that simplifies task scheduling, workflow orchestration, and monitoring of ETL processes with a user-friendly UI and integration capabilities.
Apache Airflow facilitates workflow automation through its open-source framework, offering extensive customization and scalability. Users benefit from its visual DAG representation, event-based scheduling, and task retry functionality. Frequent updates and rich integration features allow seamless interaction with platforms like AWS and Google Cloud, while Python-friendly configurations enable robust error handling and notifications. Despite requiring improvements in integration and documentation, its application spans industries such as technology, finance, and entertainment, supporting tasks like data ingestion and synchronization.
What are the key features of Apache Airflow?Apache Airflow's deployment in industries like technology, finance, and entertainment is primarily focused on automating ETL processes, managing media workflows, and orchestrating data transformation tasks. It effectively integrates with tools such as SQL scripts and Databricks, enabling organizations to manage data pipelines efficiently in both cloud and on-premises environments.
Appian provides a low-code platform designed for rapid application development, allowing businesses to enhance their efficiency through automation and integration while offering scalable and mobile-friendly solutions.
Appian stands out with its capability to streamline business processes through its intuitive drag-and-drop functionality, robust process management, and AI integration. It supports rapid deployment, automation, and scalability, making it suitable for enterprise-wide applications. Its continuous updates ensure agile customization, while seamless integration with external systems enables efficient data management. Despite some challenges with UI customization and external integrations, particularly with Microsoft products, Appian remains a valuable tool for businesses across industries seeking improved workflow management and data integration.
What are Appian's key features?Appian is primarily used in industries like banking, telecom, and manufacturing, assisting companies in managing workflows and automating processes. From application creation to data integration, it aids in enterprise-wide initiatives including mobile application deployment, real-time data processing, and intelligent automation.
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