Dagster is the 'Software-Defined Assets' platform focused on data lineage and testing. Prefect is the 'just decorate your Python' orchestrator focused on simplicity and developer velocity.
Both Dagster and Prefect are modern orchestrators that emerged as better-than-Airflow alternatives, and I've used both in production. The comparison between them isn't about "old vs new" — it's about how opinionated you want your orchestrator to be. Dagster is an opinionated data platform: assets, IO managers, resources, types, tests — a full data-engineering framework. Prefect is a light orchestration layer: decorate Python functions, schedule them, observe them. Both are excellent at what they do; they're optimized for different jobs.
On a dbt-heavy analytics engineering team with 500+ models, we picked Dagster. The asset-centric model mapped directly onto how the team already thought (each dbt model is an asset), and dagster-dbt surfaced every model as a first-class Dagster asset with automatic lineage, partition-aware materialization, and Schedule/Sensor integration. The learning curve was a week; the payoff was visible within a month — partial refreshes, cross-asset alerts, and upstream-aware backfills all became natural.
On a different team running dynamic data processing — a constantly-changing set of customer data pipelines where each customer triggered a slightly different Python workflow — we picked Prefect. Dagster's structured partition model was overkill for dynamic workflows; Prefect's @flow-decorated Python functions with dynamic subflow spawning mapped perfectly to the problem. Each customer's pipeline was a flow run, runtime-determined. Dagster would have forced us to fit square pegs into round holes.
Choose Dagster if:
python my_flow.py just worksLook at your pipelines. If they're analytics-engineering shaped (dbt models, scheduled transformations on warehouses, partition-aware incremental processing), Dagster's asset model will pay for itself. If they're dynamic Python shaped (customer-specific workflows, event-driven processing, ML training pipelines, data science experiments), Prefect's flexibility is a better match. For mixed-shape platforms, it's legitimate to run both — Dagster for the dbt/analytics side and Prefect for the Python-heavy dynamic side — though the operational cost of two orchestrators is real. Neither tool is objectively better; the right answer depends on the shape of your workloads.
If your new project is analytics-engineering shaped — dbt models, scheduled warehouse transformations, partition-aware incremental processing — Dagster is the better fit. Its Software-Defined Assets model maps directly onto dbt-style thinking. If your new project is more Python-heavy and dynamic — runtime-determined workflows, customer-specific pipelines, ML training jobs, event-driven processing — Prefect is the better fit. For mixed projects, lean toward Dagster if lineage matters and Prefect if flexibility matters.
Dagster, significantly. The dagster-dbt package treats every dbt model as a first-class Software-Defined Asset, providing automatic lineage, partition-aware materialization, asset group metadata, and unified observability across dbt models and non-dbt assets. Prefect's dbt integration (prefect-dbt) runs dbt commands as Prefect tasks with logging and retries — functional but not asset-aware. For analytics engineering teams, Dagster's dbt integration is genuinely the gold standard today.
Prefect, noticeably. Adding @flow and @task decorators to existing Python functions is immediate — most teams ship a working Prefect flow within a day. Dagster requires understanding Software-Defined Assets, IO Managers, Resources, Partitions, and the asset graph; expect a week of learning before productivity. Once past the curve, Dagster's structure often pays back that investment, especially for data-centric teams. Prefect's "easy to start" advantage is a real onboarding benefit for less-experienced teams.
Dagster has Schedules (cron-based triggering of jobs/assets), Sensors (event-driven triggers that poll external systems), and Freshness policies (automatic triggers when an asset goes stale). Prefect has schedules (cron or interval-based), deployments (configurable flow runs with parameters), and automations (event-driven actions on state changes). Both support the common patterns well; Dagster's freshness-based triggering is genuinely useful for analytics workloads and doesn't have a direct Prefect equivalent.
Both are excellent and modern. Dagster's UI is asset-graph-centric — the lineage graph is front and center, showing assets, their partitions, materialization history, and upstream/downstream relationships. Prefect's UI is flow-run-centric — each flow run shows its task graph, logs, states, and durations. Dagster's UI is better for understanding your data platform as a whole; Prefect's is better for debugging individual workflow executions. Neither is objectively better; they reflect their different mental models.
Technically yes, though most teams pick one. You could run Dagster for your dbt/analytics workloads and Prefect for your dynamic Python pipelines, with one calling the other via HTTP API if needed. The operational cost of running both (two services, two sets of observability, two sets of team knowledge) is real — consider whether your workload genuinely splits cleanly between the two models before committing to a dual-tool strategy.
Both work well. Dagster has dagster-snowflake providing Snowflake assets, resources, and IO managers — you can write @asset functions that materialize tables in Snowflake with lineage tracked automatically. Prefect has prefect-snowflake providing SnowflakeConnector and query tasks. For orchestrating complex Snowflake workloads (dbt + Snowpark + stored procedures + dynamic tables), Dagster's asset model gives you better visibility across the whole pipeline; Prefect is lighter-weight for simpler SQL-job orchestration.