Dagster is a general-purpose orchestration platform for entire data pipelines with scheduling and monitoring, while dbt is a specialized transformation tool focused solely on SQL-based data modeling and transformation. Dagster handles orchestration across tools; dbt excels at ELT transformations within data warehouses.
Dagster vs dbt — Side-by-side comparison on A Versus B
Choose Dagster if you need to orchestrate complex, multi-tool data pipelines with Python workflows, require native scheduling and monitoring, or work with diverse data sources. Choose dbt if you're focused on SQL-based transformations, need rapid development of data models, want strong community support for analytics engineering, or work primarily within a single data warehouse.
Compare Dagster vs dbt (data build tool)
Modern asset-oriented data orchestration platform with first-class data lineage and testability.
SQL-first transformation framework for analytics engineering that enables version control, testing, and documentation of data models.
Structured comparison data for Dagster vs dbt with 151 attributes.
Time to First Working Pipeline (typical)
Estimated Learning Curve (Hours to Productivity)
Maximum Concurrent Runs (single instance)
Initial Setup Cost (First Year, Single User)
Dagster vs dbt — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Dagster vs dbt.
Yes, absolutely. This is a best-practice pattern called the 'dbt + Dagster stack'. Dagster orchestrates the entire pipeline and can invoke dbt runs as a step, leveraging dbt's SQL transformation strength within a Dagster-managed workflow. Dagster provides the scheduling, monitoring, and orchestration layer while dbt handles data modeling and transformation.
Is dbt a replacement for Apache Airflow?
No, dbt and Airflow serve different purposes. Airflow is an orchestration tool (like Dagster) that schedules and monitors workflows. dbt is a transformation tool. However, dbt can work with Airflow - Airflow orchestrates dbt runs. Dagster is closer to Airflow functionally but with better data asset tracking.
Partially. Dagster can execute SQL transformations and handle data modeling, but it's not optimized for analytics engineering workflows the way dbt is. dbt offers superior documentation generation, testing frameworks, and lineage tracking specifically for SQL models. Teams often use both for complementary strengths.
Which is better for a startup data team?
For most startups, dbt is the faster choice - SQL-focused teams can be productive in days. Dagster requires Python expertise and is better suited for organizations with complex multi-tool pipelines or ML workflows. If your startup is data-warehouse-centric with SQL analysts, start with dbt and add Dagster later for orchestration.
What's the total cost of ownership difference?
dbt Cloud's managed service ranges $300-1,200/month depending on tier. Dagster Cloud is similarly priced ($500+/month). However, Dagster typically requires additional infrastructure (Kubernetes, compute resources) for production, while dbt leverages your existing data warehouse. Open-source versions of both are free but require self-hosting and operational overhead.