Newgen OmniFlow and Apache Airflow are competing products in the workflow automation category. Apache Airflow appears to have the upper hand due to its robust features and scalability, although Newgen OmniFlow is noted for its strong customer support and pricing strategies.
Features: Newgen OmniFlow offers powerful process automation with seamless integration across multiple platforms, low-code no-code designer tools, and comprehensive document management capabilities. Apache Airflow provides a rich ecosystem for managing complex workflows, utilizing a Python-based configuration, advanced orchestration tools, and extensive monitoring capabilities.
Room for Improvement: Newgen OmniFlow could improve by enhancing its deployment flexibility, expanding its community support, and further reducing implementation time. Apache Airflow needs to improve its centralized customer support, simplify its initial setup process for new users, and enhance its visual interface for less technical stakeholders.
Ease of Deployment and Customer Service: Newgen OmniFlow benefits from a straightforward deployment process and comprehensive customer support to ensure smooth implementation. Apache Airflow, although equipped with extensive documentation for open-source deployment, lacks centralized customer support, relying instead on community-driven assistance.
Pricing and ROI: Newgen OmniFlow generally requires a higher initial setup cost but promises robust ROI through tailored solutions across various industries. Apache Airflow's open-source model minimizes initial costs, offering scalability and flexibility as economic benefits, leading to long-term returns when effectively managed.
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.
