Apache Airflow and AWS Step Functions compete in the workflow orchestration category. Apache Airflow seems to have the upper hand due to its flexibility with Python, extensive community support, and cost-effectiveness.
Features: Apache Airflow offers seamless integration with Python, ensuring high flexibility and customization. Its open-source nature and a robust set of plugins contribute to its popularity for developing complex workflows. AWS Step Functions provide a simple, intuitive UI that integrates well with the AWS ecosystem, offering a graphical design interface.
Room for Improvement: Apache Airflow could benefit from supporting cyclic workflows and enhancing its UI and scalability. It would also gain from better integration with other cloud services. AWS Step Functions need to address payload size limitations, improve third-party tool integration, and offer a more transparent pricing model while enhancing data transformation features.
Ease of Deployment and Customer Service: Apache Airflow provides diverse deployment options including public, hybrid, and on-premises solutions, though technical support is mostly limited to community forums. AWS Step Functions primarily support public cloud deployment, ensuring smooth AWS integration and offering better documented and structured technical support for reliability.
Pricing and ROI: Apache Airflow's open-source model eliminates software costs, providing strong ROI for businesses capable of managing its complexity. AWS Step Functions, with a usage-based pricing model, cater to varying scales but can be costly for high-volume workflows. While Apache Airflow offers high ROI due to flexibility and zero licensing cost, AWS's pricing structure provides a competitive edge for AWS-focused environments.
Forums and community resources like Stack Overflow are helpful.
There is enough documentation available, and the community support is good.
The speed of reply and the content of their responses usually solve the problems that arise.
I use enterprise support, which is excellent, providing responses within fifteen minutes.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
They can handle multiple simultaneous requests effectively.
I would rate its stability at nine out of ten.
I would rate the stability of the solution as ten out of ten.
Apache Airflow is stable and I have not experienced significant issues.
If one zone fails, others handle the demand, ensuring high availability.
It is not suitable for real-time ETL tasks.
If we desire to add custom messengers or a rest API, those options are unavailable.
There is no dashboard for us to check all the Directed Acyclic Graphs (DAGs); a dashboard would help us analyze the work better.
It would benefit from more integration with different applications or services.
It is a sub-feature and not an individual purchase.
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.
Pricing for Step Functions is very cheap.
The cost is average, provided it is configured correctly.
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.
Step Functions provide seamless integration with AWS services, which enhances the speed of application development.
AWS Step Functions offers advanced workflows that save time and enhance efficiency by reducing delays and ensuring consistent orchestration among various services.
It was particularly valuable for its integration capabilities, allowing for execution of scripts and data migration.
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.”
AWS Step Functions enables orchestration of complex workflows, parallel task execution, and seamless integration with AWS services, simplifying ETL automation and data pipeline management.
With AWS Step Functions, developers streamline task automation and ensure smooth microservices orchestration. It simplifies development and manages interdependencies, validates data, and integrates AWS services for cost-effective workflows. It offers a user-friendly graphical interface for creating pipelines, scalable parallelization via the "map" state, control over historical events, and visualization with JSON. Enhanced by a dashboard-like view, it aids in visual workflows and debugging.
What are the most valuable features?
What are the benefits and ROI to look for?
In specific industries such as finance, healthcare, and e-commerce, enterprises implement AWS Step Functions to manage data pipelines, orchestrate microservices, and automate complex ETL jobs. They benefit from seamless integration with AWS services, enhanced data validation, and streamlined development, resulting in efficient and cost-effective workflows tailored to their operational needs.
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.
