Life beyond dbt™:
Look at all of your organization’s data
dbt Cloud is a dedicated tool for orchestrating dbt models. It provides a focused but limited understanding based on source freshness and exposures, and it cannot take action or understand how other parts of your organization function.
Dagster creates a single pane of glass to view all of your organization’s data in one platform. You can observe the relationships between your application database, various data sources, your data warehouse, BI reporting, and your machine learning models.
Migration made easy
We provide a straightforward migration path from dbt Cloud to Dagster, no code changes required.
Run your dbt models in Dagster Cloud in under half an hour and enjoy the power of a fully-featured modern control plane for your data pipelines, end-to-end.
Unify your stack
Dagster orchestrates all of your data processes, including that of other tools, into one coherent asset-focused framework. Dagster unifies all of your tools under one control plane, with intelligent schedules that run your entire data pipeline, all at once. With Dagster you understand where things fail, no matter where the error occurs.
dbt Cloud only schedules dbt models. Many users schedule a dbt job at some arbitrary time after their data ingestion (i.e., 1 hour after they think it’ll be done). However, this is unreliable as the data ingestion may have failed. Nonetheless, dbt Cloud will run and charge you for the models built with stale data.
Orchestrate your Python, SQL, and more - anywhere.
Dagster is a Python framework that empowers you to execute any workloads you may need. Use any package available to the Python community. Execute bash scripts, Java, R, C#, Rust workloads, and more by tapping into the Python ecosystem. Dagster can connect to any database or warehouse using the same Python SDKs you’d use in regular scripts.
dbt Cloud has limited Python support. It uses your data warehouse’s runtime to materialize Python models. Therefore, you’re constrained by their limitations. For example, you’re only able to use Python packages approved by Snowpark or GCP Dataproc, and you may not have outside network access when your Python model runs. dbt Cloud restricts users to only the 6 data warehouses that they support, which is a significantly shorter list than dbt-core’s list of adapters.
Partition your dbt models
With Dagster, you can partition your data assets created from dbt models by date range or any other dimension. You can add on or rebuild your dbt models one partition at a time, with complete control. You can develop with an individual partition and scale in production.
dbt Cloud does not support partitioning, but requires incremental models to add to existing tables.
Use a fully-featured orchestrator
Dagster was built from the ground up as an orchestrator. It supports the standard expectations of an orchestrator: structured logging, automatic retries on failure, and observability over metadata such as tests passing or model performance over time. Dagster also continues to expand the category of what orchestrators should do with many new features in development.
dbt Cloud is a lightweight scheduler made strictly for dbt. Logs are difficult to parse through and must be downloaded to be searched. Retrying failures is limited and doesn’t allow for exponential back-off or executing different logic on repeated failures. dbt Cloud sometimes must be complemented by other products in order to track historical metadata.
To summarize the main differences between dbt Cloud and Dagster:
 |   | |
| Asset-aware |  | |
| Cron-based Scheduling | | |
| Integrated IDE | |  |
| Full-featured orchestration (retries, debugging, logging, history) | | |
| Flexible scheduling options | | |
| Native asset observability | | |
| Partitioned data support | Limited (incremental models) | |
| Dynamic alerting | | |
| Cost management | | |
| Data warehouse support | Limited (short list) | All databases |
Community
Dagster has a growing community of forward-thinking engineers who see the value of our differentiated approach. The Dagster engineering team is directly involved in supporting both open-source and Dagster Cloud users.
Interested in getting an objective 3rd party perspective? Join the Dagster Slack and interact with current users.