Dagster vs dbt Cloud – How they compare and work together
Which is the better tool for deploying dbt™ models? Is it dbt Cloud™, the dedicated dbt scheduling solution, or is it Dagster, the generalized data orchestration platform?
Understanding their roles in the modern data stack
End-to-end observability across your entire data platform
Dagster integrates dbt into a broader orchestration framework. It gives teams visibility into not just dbt assets, but upstream ingestion, downstream reporting, and everything in between. With a Python-native approach, it unifies SQL, Python, and more into a single, testable, and observable platform.
A focused scheduler built for the dbt ecosystem
Designed to execute and schedule dbt models, dbt Cloud™ offers a narrow but focused solution. It provides source freshness checks, exposure tracking, and a scheduling interface—but only within the dbt ecosystem.
Dagster vs dbt Cloud™ – What makes them different
Unify Your Stack
First-class data assets: Orchestration with context
Dagster lets you orchestrate everything in your data platform—not just dbt. You can run ingestion, transformation, and ML workflows in one place, with unified scheduling and observability. When something fails, you’ll know exactly where and why.
Task-based workflows: Simple to start, harder to scale
dbt Cloud™ only runs dbt jobs, often scheduled arbitrarily (e.g., “run 1 hour after ingestion”). If upstream tasks fail, dbt Cloud™ still runs—leading to stale or broken models and wasted runs.
Orchestrate More Than SQL
Highlighting observability and control
Dagster supports Python, bash, Java, R, and more. You can connect to any warehouse or tool using your own logic and packages. It’s built for engineers who want to orchestrate the full data lifecycle.
Limited visibility: dbt Cloud tracks tasks, not data
dbt Cloud only supports Python in specific data warehouse runtimes like Snowpark or GCP Dataproc. These are constrained, with limited packages and no external network access. You’re also limited to six supported data warehouses.
Partition Your dbt Models
Beyond time-based triggers: Dagster orchestrates with data signals
Dagster lets you partition dbt models by any dimension (like date or region) and re-run just the slice you need. You can scale in production or develop locally on a single partition.
Flexible scheduling without data guarantees
dbt Cloud doesn’t support true partitioning. It relies on incremental models, which add rows to existing tables but don’t allow full control or reprocessing of past time windows.
Dagster vs dbt Cloud Feature breakdown
| | |
|---|---|---|
Asset-aware | Yes | Yes |
Cron-based Scheduling | Yes | Yes |
Integrated IDE | No | Yes |
Full-featured orchestration (retries, debugging, logging, history) | Yes | No |
Flexible scheduling options | Yes | No |
Native asset observability | Yes | No |
Partitioned data support | Yes | Limited (incremental models) |
Dynamic alerting | Yes | No |
Cost management | Yes | No |
Data warehouse support | All databases | Limited (short list) |
Use Dagster with or without dbt Cloud
Teams don’t have to choose between Dagster and dbt Cloud™—they can start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Over time, teams that want more orchestration flexibility often choose to replace dbt Cloud™ entirely and manage everything inside Dagster.
Orchestrate more. Stress less.
Looking for unlimited deployments, advanced RBAC and SAML-based SSO, all on a SOC2 certified platform? Contact the Dagster Labs sales team today to discuss your requirements.