Dagster vs Prefect – Which orchestration tool fits your team?
Dagster and Prefect are both modern orchestrators, but they differ significantly in their approach, depth of abstraction, and focus. Dagster is a data platform orchestrator with a data-first architecture and opinionated best practices. Prefect is a general workflow orchestrator with a flexible execution model.
Understanding
Dagster and Prefect
An asset-centric data orchestrator for modern data engineers
Dagster is a data orchestrator purpose-built for data engineers who want to define, build, and manage data assets in code. Unlike traditional task-based orchestrators, Dagster treats data assets—such as tables, models, and reports—as first-class citizens.
This asset-centric model gives engineers a clear, maintainable representation of their data platform and its dependencies.
Manual control, less structure: Prefect’s approach to orchestration
Prefect provides a manual approach to orchestration, which becomes difficult to manage, especially as workflows grow in complexity. Prefect excels in lightweight, Python-first workflows but doesn’t natively model data assets or provide built-in data lineage, which can limit visibility and governance for larger, more interdependent data platforms.
Build data pipelines around assets, not just tasks
By building pipelines around the assets they produce rather than the tasks they run, teams can gain richer observability, enforce data quality standards, and trace lineage across their entire platform with ease. Whether deploying new models, scheduling updates, or responding to upstream changes, Dagster provides a consistent framework that abstracts operational complexity while aligning closely with modern software engineering best practices.
An imperative model: Freedom with more responsibility
Prefect is a Python-native orchestrator. Prefect’s task-based model allows users to wrap any Python function as an orchestrated task. While Prefect offers freedom in how workflows are constructed, its model is imperative rather than declarative—developers define the exact sequence of steps to execute, rather than describing desired end states.
Comparing Dagster and Prefect
Choosing innovation or stagnation?
First-class data assets: Orchestration with context
Dagster was the first to introduce data assets as first-class primitives—think tables, reports, or ML outputs—making lineage, dependencies, and state management central to orchestration.
Task-based workflows: Simple to start, harder to scale
Prefect is task-oriented: workflows are defined via Python functions and DAGs, which makes it flexible but decoupled from data semantics.
Only recently did Prefect add assets as a limited feature.
Accelerating innovation
Dagster continues to push the boundaries of what an orchestrator can do, with launch of Dagster Components and the updated platform health monitoring and pipeline observability features on homepage.
Don't take our word for it, the proof is in our open-source commits.
Declining development momentum
Prefect’s momentum has waned: weekly commits have steadily declined since mid-2021, reflecting a slowdown in core feature development and community engagement. The drop in contribution activity suggests less frequent enhancements and fewer new capabilities over time.
Why it matters
Dagster’s asset-centric approach gives you deeper visibility and data-aware orchestration out of the box. Our investment in the product means that you continue to receive benefits of our development without worrying about getting left behind.
Observability built in
Highlighting observability and control
Dagster has a deeply integrated UI and metadata model that exposes the state of every asset, including run history, dependencies, and cost/performance metrics. It supports granular alerting, retries, lineage tracking, and partitioned materializations.
Limited visibility: Prefect tracks tasks, not data
Prefect centralizes logs and statuses per task, but lacks native data lineage or visibility across workflows without custom instrumentation.
Why it matters
With Dagster, you see your data platform—not just whether a task succeeded or not.
Governance, scheduling, and SLAs
Beyond time-based triggers: Dagster orchestrates with data signals
Dagster enables data-aware scheduling—triggering assets based on upstream freshness, events, or partitions. Built-in support for asset sensors, policies, retries, and quality checks.
Flexible scheduling without data guarantees
Prefect supports dynamic scheduling, sensors, and flows, but lacks integrated data SLAs or asset resolution tracking.
Why it matters
Dagster treats your data platform as a system of record with defined state and governance.
Dagster vs. Prefect Feature breakdown
| | |
|---|---|---|
Primary Use Case | Asset-based orchestration across the modern data stack | Python-native task orchestration for flexible workflows |
Developer Experience | Declarative, modular, testable; supports branch deployments and CI/CD | Imperative, Python-function based |
Observability | Built-in asset-level lineage, run history, metrics, and alerts | Centralized logs and task-level status Custom observability via plugins |
Data Awareness | First-class support for data assets, lineage, partitions, and metadata | Task-focused; no native concept of data assets or end-state awareness |
Tooling Integration | Deep integrations with dbt, Airbyte, Snowflake, Databricks, AWS, and more | Flexible via Python functions
Community blocks for tool support |
Orchestrate more. Stress less.
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