![]() ![]() It’s only when things go wrong that workflow management is critical. One of Prefect’s fundamental insights is that if you could guarantee your code would run as intended, you wouldn’t need a workflow system at all. Additionally, because of the constraints that Airflow places on what workflows can and cannot do (expanded upon in later sections), writing Airflow DAGs feels like writing Airflow code. However, Airflow’s API is fully imperative and class-based. Airflow was the first tool to take this to heart, and actually implement its API in Python. Given its popularity and omnipresence in the data stack, Python is a natural choice for the language of workflows. For this reason, it is important that your workflow system be as simple and expressive as it can possibly be. Production workflows are a special creature - they typically involve multiple stakeholders across the technical spectrum, and are usually business critical. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. It has culminated in an incredibly user-friendly, lightweight API backed by a powerful set of abstractions that fit most data-related use cases. Our research, spanning hundreds of users and companies, has allowed us to discover the hidden pain points that current tools fail to address. Prefect is the result of years of experience working on Airflow and related projects. This makes upgrading difficult and dramatically increases the maintenance burden when anything breaks. For this reason, almost every medium-to-large company using Airflow ends up writing a custom DSL or maintaining significant proprietary plugins to support its internal needs. If your use case resembles any of these, you will need to work around Airflow’s abstractions rather than with them. A sampling of examples that Airflow can not satisfy in a first-class way includes:ĭAGs which need to be run off-schedule or with no schedule at allĭAGs that run concurrently with the same start time Users often get into trouble by forcing their use cases to fit into Airflow’s model. However, because of the types of workflows it was designed to handle, Airflow exposes a limited “vocabulary” for defining workflow behavior, especially by modern standards. It proved that workflows could be built without resorting to config files or obtuse DAG definitions. Airflow was also the first successful implementation of workflows-as-code, a useful and flexible paradigm. We have tried to be balanced and limit discussion of anything not currently available in our open-source repo, and we hope this serves as a helpful overview for the community.Īirflow was designed to run static, slow-moving workflows on a fixed schedule, and it is a great tool for that purpose. This post is not intended to be an exhaustive tour of Prefect’s features, but rather a guide for users familiar with Airflow that explains Prefect’s analogous approach. We prepared this document to highlight common Airflow issues that the Prefect engine takes specific steps to address. We know that questions about how Prefect compares to Airflow are paramount to our users, especially given Prefect’s lineage. We open sourced the Prefect engine a few weeks ago as the first step toward introducing a modern data platform, and we’re extremely encouraged by the early response! Disappointingly, those observations remain valid today. The seed that would grow into Prefect was first planted all the way back in 2016, in a series of discussions about how Airflow would need to change to support what were rapidly becoming standard data practices. It simply does not have the requisite vocabulary to describe many of those activities. Airflow got many things right, but its core assumptions never anticipated the rich variety of data applications that has emerged. Processes are fast, dynamic, and unpredictable. ![]() Compute and storage are cheap, so friction is low and experimentation prevails. Today, many data engineers are working more directly with their analytical counterparts. However, Airflow’s applicability is limited by its legacy as a monolithic batch scheduler aimed at data engineers principally concerned with orchestrating third-party systems employed by others in their organizations. ![]() It introduced the ability to combine a strict Directed Acyclic Graph (DAG) model with Pythonic flexibility in a way that made it appropriate for a wide variety of use cases. Airflow is a historically important tool in the data engineering ecosystem, and we have spent a great deal of time working on it.
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