SpiffWorkflow allows Citizen Developers to automate, monitor and improve a set of complex tasks and business decisions. It uniquely combines BPMN, a widely adopted and researched notation, with Python, a popular and easy to learn programming language.
SpiffWorkflow's answer
For our BPMN competitors - we are easier to learn, and anyone in an organization can potentially automate the processes. We are particularly good for agile shops and tech startups - places that don't have or want an army of Java developers, but who want to encapsulate and model a potentially exploding number of shifting business rules in a maintainable and scalable way.
For our No-Code competitors - Our learning curve is higher. We require some training to get going. But we don't have any "brick walls" or "class ceilings.” This is a highly general purpose tool with multiple points of extension. There is always a way to solve a problem. You are not limited by a predefined idea of what you are trying to build.
SpiffWorkflow's answer
We are targeting people who are intent on solving complex problems. We would serve you well as a back-office solution to a grievous and repetitive set of automation tasks. Something that is worth taking the time to train a highly motivated, but not necessarily technical person, so they can create, maintain and monitor the process. We excel at complex approval processes within a mid-sized organization. If you are building your own application that must support 1000's of minor differences between your clients, we could be instrumental in making that possible.
SpiffWorkflow's answer
BPMN + Python. BPMN is an intuitive flow-chart like notation that business users can easily understand, but most BPMN tools require Java software engineers to use this notation for real automation tasks. Python is the most popular language in the world, and used far outside typical software development shops - by research students, data analysts, and business managers. We've built a framework that makes designing, automating, and monitoring workflows something anyone (with patience) can learn to do. We want to place BPMN and Python in the hands of everyone.
SpiffWorkflow's answer
SpiffWorkflow's answer
SpiffWorkflow's answer
Core Technologies
Extensions and Connections
Deployment
Internals
Based on our record, Apache Airflow seems to be more popular. It has been mentiond 75 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 2 months ago
Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 3 months ago
Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / 4 months ago
Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 4 months ago
This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / 5 months ago
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