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.
There is enough documentation available, and the community support is good.
Forums and community resources like Stack Overflow are helpful.
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.
Starting separate Step Functions for each request, making it highly scalable.
There isn't much need for scalability when creating orchestration in AWS Step Functions, which explains the rating of six.
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.
If one zone fails, others handle the demand, ensuring high availability.
I never faced any faults or issues due to its stability with AWS, making it very reliable with no downtime and 100% reliability.
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.
It would benefit from more integration with different applications or services.
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.
Pricing for Step Functions is very cheap.
The cost is average, provided it is configured correctly.
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.
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.
It was particularly valuable for its integration capabilities, allowing for execution of scripts and data migration.
AWS Step Functions offers advanced workflows that save time and enhance efficiency by reducing delays and ensuring consistent orchestration among various services.
| Product | Market Share (%) |
|---|---|
| Apache Airflow | 4.3% |
| AWS Step Functions | 1.8% |
| Other | 93.9% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 3 |
| Large Enterprise | 24 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 2 |
| Large Enterprise | 5 |
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 integrate seamlessly with other AWS services to offer efficient pipeline and workflow management. Its intuitive design allows for streamlined orchestration, easily handling complex tasks.
AWS Step Functions provide robust orchestration and automation capabilities, simplifying the creation of workflows with graphical and JSON-based designs. It excels in managing tasks through advanced parallelization and error handling features. Automatic scaling further enhances performance, ensuring reliability in varied environments. However, improvements are needed in IDE integration, larger data handling, and fault tolerance. Users find value in its capacity for microservice orchestration and data integration, although dependency on the Amazon ecosystem and limited third-party integrations pose challenges.
What are the key features of AWS Step Functions?In industries managing data pipelines, AWS Step Functions orchestrate workflows, execute parallel ETL jobs, and integrate various AWS services, enabling smoother operations and efficient data migration. Companies benefit from streamlined processes and robust handling of interdependent tasks.
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.
