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.
Here are the main differences between dbt Cloud and Dagster:
|Full-featured orchestration (retries, debugging, logging, history)
|Flexible scheduling options
|Native asset observability
|Partitioned data support
|Limited (incremental models)
|Data warehouse support
|Limited (short list)
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.
With Dagster, you can execute any workloads you may need using Python. Execute bash scripts, Java, R, C#, Rust workloads, and more. 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.