Build AI applications with Horizon. 2 days ago · 2 — Prefect Prefect is a modern orchestration engine that emphasizes “negative engineering”—the idea that the tool should handle all the things that could go wrong so you don’t have to. Curious to hear the community's experience with Prefect, specifically 2. Airflow: Which Workflow Orchestrator Should Data Engineers Choose? By Deepti Bahel In the evolving data engineering landscape, orchestrating complex workflows reliably is critical. Setting the Stage: Basic Implementations Airflow’s “Hello World” Symphony from airflow import DAG Airflow vs. Airflow. While Apache Airflow has been the industry standard for years, Prefect stands out as a strong Jan 18, 2025 · Discover the ultimate 2025 comparison of Prefect vs. 0 and Prefect 3. Jul 23, 2021 · I currently use Airflow and use Prefect's core product within Airflow tasks (so Airflow does the scheduling, Prefect does the execution) on our computationally heavy tasks. If your goal is to build a data platform that is built on data engineering best practices and is primarily focused on building and maintaining data sets, then Dagster is a much stronger choice. 2 days ago · Apache Airflow vs Prefect Top 10 Data Pipeline Orchestration Tools: Features, Pros, Cons & Comparison January 21, 2026 cotocus 0 Jul 22, 2025 · Prefect vs. CI/CD and with Prefect you can pretty much just write Python rather than "Airflow"-code or "orchestrator"-code, which makes company-wide adoption Mar 30, 2023 · I have little experience with Airflow, but I am looking for new orchestration tool for personal project. Generally, Prefect is better designed, everything is API-first and extensible, easy to get started and you can have a painless and reliable infra story incl. That’s your data pipeline without proper orchestration. My initial thought is to set up managed airflow server on GCP because I have worked with airflow for few months in one of my client engagements. May 2, 2024 · Explore Airflow vs Prefect, two data orchestration tools, and how they may be utilized to improve data workflow management. Explore their features, strengths, weaknesses, and ideal use cases to select the best workflow orchestration tool for your needs. Airflow is a good choice as a generalized orchestrator, multi-purpose, and large adoption. Jan 18, 2022 · I'd argue not to use Airflow if you start fresh and use either: Prefect if you need a fast and dynamic modern orchestration with a straightforward way to scale out. Prefect: Like Airflow but easy to manage. Open-source foundations, production-ready platforms. However reading more about orchestration tools, it seems that Dagster might be a better choice in terms of usability and that it tackles the . It is designed to be highly intuitive and developer-friendly. Mar 24, 2023 · Airflow is the king of the ecosystem still. 60-70% cost savings, Python-first simplicity, and hybrid architecture that keeps your data in your infrastructure. Jan 7, 2026 · Prefect and Apache Airflow are two of the most popular options for managing data pipelines. Jun 6, 2023 · We're currently evaluating Airflow, Prefect, Dagster Airflow: Easy to build pipelines, Flexible, good integrations. 0 with a new second-generation orchestration engine called Orion. Dagster when you Aug 12, 2022 · Airflow is definitely the oldest and most adopted of the three, but Prefect is strictly better in several ways. See side-by-side examples of how to write a simple "Hello World" workflow in both platforms. I've heard it can be difficult to manage and is slightly outdated and bloated. Nov 9, 2021 · Airflow or Prefect? I'm trying to decide on a workflow automation engine for my data engineering project - what would you recommend? You should judge based on your use case. Compare Airflow and Prefect, two popular orchestration tools for data engineering, based on their architecture, user interface, error handling, and extensibility. x. It has several abstractions that make it a swiss army knife for general task management. Jul 27, 2023 · This article provides in-depth comparison of Argo, Airflow, and Prefect, allowing to find the optimal tool for your task orchestration needs. My personal gripes are they still haven't given up on the radar chart which I need to zoom out so far that the text is unreadable just to see all the weird routed edges Airflow and Prefect aren't, while Dagster was super easy to install and test, pip install dagster dagit et voilà, I have everything needed to test locally as if I was in production, including a great UI (dagit). What’s the difference between Apache Airflow, Control-M, and Prefect? Compare Apache Airflow vs. 0 I've generally been an ardent proponent, but lately I've started to see more and more cracks in the system. Control-M vs. Learn why engineering teams are choosing Prefect over Airflow.

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