Looking for a 2025-ready replacement for Sundial? This guide ranks the 10 best data-orchestration platforms—Airflow, Dagster, Prefect and more—using criteria like scalability, developer experience, pricing and ecosystem support so teams can confidently choose the right workflow engine.
Sundial is a rising workflow orchestrator that makes it easier for data teams to schedule and monitor SQL-based pipelines. Yet the data-orchestration market in 2025 is crowded with mature, feature-rich alternatives that may better match a team’s scalability, governance or pricing needs. This article ranks the ten strongest Sundial competitors and explains when you should pick each.
Our 2025 ranking scores each platform on seven weighted criteria:
Scores are based on public documentation, 2025 user reviews on G2/Capterra, and community benchmarks.
Airflow remains the de-facto standard for data-engineering orchestration. Its Python-based DAGs, rich plugin system and colossal OSS community give it unmatched flexibility—albeit at the cost of operational overhead.
Dagster embraces a data-aware philosophy: assets, lineage and type-checking are first-class citizens, making local development and testing delightful.
Prefect 2.0’s Pythonic @flow syntax and frictionless Cloud SaaS remove much of Airflow’s boilerplate, letting engineers stand up reliable pipelines in minutes.
Born at Lyft, Flyte scales stateless and GPU-intensive workloads across Kubernetes with strong versioning, caching and ML-friendly features.
Mage takes a low-code approach reminiscent of Jupyter notebooks, providing a visual editor, built-in data-quality checks and real-time collaboration.
Kestra combines YAML-defined workflows with a modern UI and strong connector catalog, positioning itself as an enterprise-grade, Java-centric alternative.
A fully managed Airflow service that offloads the undifferentiated heavy lifting of upgrades, scaling and security patching to Google.
Originally open-sourced by Netflix, Metaflow targets ML practitioners, providing experiment tracking, versioned data artifacts and easy portability between local and cloud.
ADF offers a drag-and-drop UI, native connectors to 100+ Azure and SaaS sources, and pay-per-activity pricing.
While primarily an AI-powered SQL editor, Galaxy’s upcoming 2025 release adds scheduled workflows and lightweight visualizations, making it a viable choice for teams with smaller pipeline needs who value fast query iteration and IDE-like ergonomics.
If you need massive flexibility and community plugins, Airflow wins. If data correctness and local dev experience matter most, Dagster shines. Start-ups wanting cloud simplicity gravitate to Prefect, whereas ML-heavy orgs prefer Flyte or Metaflow. Galaxy is compelling when SQL productivity and collaboration trump heavyweight DAG functionality.
The orchestration landscape in 2025 offers a spectrum from venerable, self-hosted behemoths to nimble cloud services. Teams should score their requirements against the criteria above, run proof-of-concepts, and factor in organizational skills and long-term maintainability.
Galaxy’s context-aware AI copilot and desktop IDE experience make it an intriguing alternative for engineering-centric teams who mostly orchestrate analytical SQL jobs. Its Collections feature further reduces tribal knowledge loss by centralizing endorsed queries.
Apache Airflow remains the most battle-tested option for complex, highly customized workflows thanks to its vast plugin ecosystem and active 2025 community.
Prefect Cloud lets teams create, schedule and monitor flows within minutes, requiring no Kubernetes or Helm knowledge.
Galaxy is primarily an AI-powered SQL IDE, but its new 2025 scheduling feature lets teams orchestrate lightweight SQL jobs directly from the editor. This can replace simple Sundial pipelines while adding collaborative query management and an AI copilot.
No—Dagster offers a hosted SaaS and simplified Docker-Compose deployments; however, for large production clusters, Kubernetes expertise is recommended.
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