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
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.
Primary Function. Scope of Operations. Learning Curve for Data Engineers. Community Size (GitHub Stars). ELT vs Orchestration Focus. Scheduling & Monitoring Built-in. Typical Use Case Maturity
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.
Dagster: Python 3.8 | dbt (data build tool): N/A
Dagster: 45-60 minutes | dbt (data build tool): N/A
Dagster: Automatic and built-in | dbt (data build tool): N/A
Dagster: Yes - Dagster asset checks | dbt (data build tool): N/A
Dagster: 12+ (K8s, Docker, ECS, serverless) | dbt (data build tool): N/A
Dagster: Free for self-hosted, $99/month for Dagster+ | dbt (data build tool): N/A
Dagster: 2019 | dbt (data build tool): N/A
Dagster: 8,400+ | dbt (data build tool): N/A
Time to First Working Pipeline (typical)
Dagster: 4-6 hours | dbt (data build tool): N/A
Dagster: $200-500 | dbt (data build tool): N/A
Dagster: Enterprise-grade built-in | dbt (data build tool): N/A
Dagster: 3.8+ | dbt (data build tool): N/A
Dagster: 6+ (K8s, Docker, serverless, hybrid) | dbt (data build tool): N/A
Dagster: 800+ | dbt (data build tool): N/A
Dagster: 20-30 minutes | dbt (data build tool): N/A
Dagster: ~200 lines | dbt (data build tool): N/A
Dagster: Native asset-level (100%) | dbt (data build tool): N/A
Dagster: 100% of features | dbt (data build tool): N/A
Dagster: 2 CPU / 4GB RAM minimum | dbt (data build tool): N/A
Dagster: ~15+ features (full compliance suite) | dbt (data build tool): N/A
Dagster: ~9.2k stars | dbt (data build tool): N/A
Dagster: 50+ | dbt (data build tool): N/A
Dagster: 3.9+ | dbt (data build tool): N/A
Dagster: Built-in Dagster Types with validation | dbt (data build tool): N/A
Dagster: 15-20 | dbt (data build tool): N/A
Dagster: Yes, native support | dbt (data build tool): N/A
Dagster: 99.9% | dbt (data build tool): N/A
Dagster: ~50 connectors | dbt (data build tool): N/A
Dagster: 8-24 hours | dbt (data build tool): N/A
Dagster: Enterprise-grade (dynamic, conditional) | dbt (data build tool): N/A
Dagster: Advanced (Python/software engineering) | dbt (data build tool): N/A
Dagster: 9/10 (advanced custom logic) | dbt (data build tool): Unlimited with Jinja2 templating & macros
Dagster: Usage-based (compute units) | dbt (data build tool): N/A
Dagster: Native, built-in with asset graph | dbt (data build tool): N/A
Dagster: Yes - native DAG, scheduling, dynamic branching | dbt (data build tool): No - requires external orchestrator
Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week) | dbt (data build tool): $100-400/month per developer seat
Dagster: End-to-end pipeline orchestration and execution | dbt (data build tool): Data Transformation (T)
Dagster: Cloud-agnostic; works with 15+ systems | dbt (data build tool): 10+ with deep native support (Snowflake, BigQuery, Redshift, Postgres, etc.)
Dagster: 40-60 hours (requires Python learning) | dbt (data build tool): 8-15 hours (SQL-only approach)
Dagster: 9,800+ stars | dbt (data build tool): 22,100+ stars
Dagster: 2,000+ (estimated from public case studies) | dbt (data build tool): 5,000+ (verified public customers)
Dagster: 9,200+ | dbt (data build tool): N/A
Dagster: 5 years (2019) | dbt (data build tool): N/A
Dagster: 150+ | dbt (data build tool): N/A
Dagster: Native assertions & sensors | dbt (data build tool): N/A
Dagster: Asset-centric with lineage tracking | dbt (data build tool): N/A
Estimated Learning Curve (Hours to Productivity)
Dagster: 40-60 hours | dbt (data build tool): N/A
Dagster: 40+ | dbt (data build tool): N/A
Dagster: 500+ | dbt (data build tool): N/A
Dagster: ~50 | dbt (data build tool): N/A
Dagster: Strong (compile-time + runtime) | dbt (data build tool): N/A
Dagster: 2,500+ | dbt (data build tool): N/A
Dagster: 2018 | dbt (data build tool): N/A
Dagster: Full asset-level lineage | dbt (data build tool): N/A
Dagster: First-class type definitions | dbt (data build tool): N/A
Dagster: 3-4 weeks to proficiency | dbt (data build tool): N/A
Dagster: Dagster Cloud (official) | dbt (data build tool): N/A
Dagster: 100K+ (typical deployments) | dbt (data build tool): N/A
Dagster: 50+ connectors | dbt (data build tool): 0 (not applicable)
Dagster: 7/10 (Python required) | dbt (data build tool): N/A
Dagster: 24-48 hours (development needed) | dbt (data build tool): N/A
Dagster: Full native integration | dbt (data build tool): N/A
Dagster: 5-10 days | dbt (data build tool): N/A
Dagster: 9,200+ stars | dbt (data build tool): N/A
Dagster: 100+ (Python, Spark, K8s, APIs, databases, cloud) | dbt (data build tool): 12+ data warehouses (Snowflake, BigQuery, Redshift, Postgres, DuckDB, etc.)
Dagster: 40-80 hours (requires Python, orchestration concepts) | dbt (data build tool): 10-20 hours (SQL knowledge sufficient)
Dagster: Yes - built-in with Dagster Daemon | dbt (data build tool): No - requires external tool (Airflow, Prefect, cron)
Dagster: Yes - built-in monitoring, alerting, and error tracking | dbt (data build tool): Limited - requires dbt Cloud or external tools for comprehensive monitoring
Dagster: Python, SQL, Spark, Go, shell scripts, APIs | dbt (data build tool): SQL only (with some Jinja2 templating)
Dagster: 15-25 hours (setup + learning) | dbt (data build tool): 2-4 hours (familiar SQL, quick setup)
Dagster: Not applicable | dbt (data build tool): 5,000+ packages on dbt Hub
Dagster: 40-90 minutes | dbt (data build tool): N/A
Dagster: Manual tracking via assets; no built-in staging workflow | dbt (data build tool): N/A
Dagster: Native asset graphs with automatic lineage, impact analysis, and upstream/downstream visibility | dbt (data build tool): N/A
Dagster: 9,500+ | dbt (data build tool): 21,000+ stars
Dagster: Framework-agnostic via Python ops; requires custom integration | dbt (data build tool): N/A
Dagster: Python 3.8+ | dbt (data build tool): N/A
Maximum Concurrent Runs (single instance)
Dagster: 500+ (optimized for high-volume asset materialization) | dbt (data build tool): N/A
Dagster: Built-in Python type hints with automatic validation and schema enforcement | dbt (data build tool): N/A
Dagster: 2,000+ | dbt (data build tool): N/A
Dagster: Limited (community growing) | dbt (data build tool): N/A
Dagster: Yes (first-class asset lineage) | dbt (data build tool): N/A
Dagster: Yes (comprehensive in-process testing) | dbt (data build tool): Native tests, assertions, freshness checks, dbt expectations
Dagster: Strong (enforced type hints) | dbt (data build tool): N/A
Dagster: Moderate-to-High (asset-oriented paradigm) | dbt (data build tool): N/A
Dagster: N/A | dbt (data build tool): 1.2M
Dagster: N/A | dbt (data build tool): 2-4 hours
Dagster: N/A | dbt (data build tool): 15+
Dagster: N/A | dbt (data build tool): Beginner (SQL-only option)
Dagster: N/A | dbt (data build tool): 400+
Dagster: N/A | dbt (data build tool): T only (transformation layer)
Dagster: N/A | dbt (data build tool): Low (CLI tool, runs locally or on warehouse)
Dagster: N/A | dbt (data build tool): 250+ adapters
Dagster: N/A | dbt (data build tool): $0 (1 developer seat)
Dagster: N/A | dbt (data build tool): 300 seconds (5 minutes via Cloud)
Dagster: N/A | dbt (data build tool): 4-8 hours (SQL-based)
Dagster: N/A | dbt (data build tool): Auto-generated lineage, column docs, test summaries, dbt docs site
Dagster: N/A | dbt (data build tool): Limited—ref() and source() are static dependencies
Dagster: N/A | dbt (data build tool): 29,000
Dagster: N/A | dbt (data build tool): ~15,000+ companies
Dagster: N/A | dbt (data build tool): SQL + Jinja2
Dagster: N/A | dbt (data build tool): 14 official + 50+ community
Dagster: N/A | dbt (data build tool): 5-10 minutes
Dagster: N/A | dbt (data build tool): $0 (dbt Cloud Developer plan)
Dagster: N/A | dbt (data build tool): $0-$500
Dagster: N/A | dbt (data build tool): 2-8 minutes
Dagster: N/A | dbt (data build tool): 2-8 hours
Dagster: N/A | dbt (data build tool): Snowflake, BigQuery, Redshift, Postgres, Databricks, Spark SQL, DuckDB, Trino (8+ platforms)
Dagster: N/A | dbt (data build tool): 4+ (assertions, tests, data quality checks, schema validation)
Dagster: N/A | dbt (data build tool): 1+ GB
Dagster: N/A | dbt (data build tool): 22.8K stars
Initial Setup Cost (First Year, Single User)
Dagster: N/A | dbt (data build tool): $0 (open-source) or $1,000 (dbt Cloud)
Dagster: N/A | dbt (data build tool): No (requires external tools)
Dagster: N/A | dbt (data build tool): 15,000+
Dagster: N/A | dbt (data build tool): DAG visualization in dbt Cloud UI
Dagster: N/A | dbt (data build tool): Yes (dbt Core open-source + limited Cloud tier)
Dagster: N/A | dbt (data build tool): Requires external Git + manual CI/CD setup
Dagster: N/A | dbt (data build tool): Free (dbt Core)
Dagster: N/A | dbt (data build tool): $300/month
Dagster: N/A | dbt (data build tool): 1000+ dbt packages
Dagster: N/A | dbt (data build tool): 30-60 (requires source data)
Dagster: N/A | dbt (data build tool): Advanced (4/5)
Dagster: N/A | dbt (data build tool): 20,000+
Dagster: N/A | dbt (data build tool): ~75 (official) + community
Dagster: N/A | dbt (data build tool): Advanced (5+ years experience)
Dagster: N/A | dbt (data build tool): ~$500-2,000 platform fee
Dagster: N/A | dbt (data build tool): 10-20 days
Dagster: N/A | dbt (data build tool): 20+ (Snowflake, BigQuery, Redshift, Postgres, Databricks, etc.)
Dagster: N/A | dbt (data build tool): 30,000+
Dagster: N/A | dbt (data build tool): Self-hosted, dbt Cloud (SaaS)
Dagster: N/A | dbt (data build tool): 60%+
Dagster: N/A | dbt (data build tool): 10,000+
Dagster: N/A | dbt (data build tool): 20+
Dagster: N/A | dbt (data build tool): 280-350 MB
Dagster: N/A | dbt (data build tool): 12-18 seconds
Dagster: N/A | dbt (data build tool): 15-20 minutes
Dagster: N/A | dbt (data build tool): 200+
Dagster: N/A | dbt (data build tool): 7+ years (founded 2019)
Dagster: N/A | dbt (data build tool): 20,000+ members
Dagster: N/A | dbt (data build tool): 300+ verified packages
Dagster: N/A | dbt (data build tool): 50,000+ companies
Dagster: N/A | dbt (data build tool): 15-30 minutes
Dagster: N/A | dbt (data build tool): Jinja2 templating
Dagster: N/A | dbt (data build tool): 3,000+ public packages
Dagster: N/A | dbt (data build tool): $0 (open-source) / $100 (dbt Cloud)
Dagster: N/A | dbt (data build tool): 4-8 hours (Git, CI/CD, warehouse connection)
Dagster: N/A | dbt (data build tool): 25,000+
Dagster: N/A | dbt (data build tool): 60%+ of enterprise data teams
How to choose between Dagster and dbt (data build tool)
Compare Dagster vs dbt (data build tool)
Dagster vs dbt — Structured Comparison Data
Machine-readable comparison dataset for Dagster vs dbt. Contains 151 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between Dagster and dbt (data build tool)
Primary Function: Dagster has End-to-end pipeline orchestration and workflow management, while dbt (data build tool) has SQL-based data transformation and modeling.
Scope of Operations: Dagster has Can orchestrate Python, SQL, Spark, APIs, and 100+ external tools, while dbt (data build tool) has Limited to SQL transformations in data warehouses (Snowflake, BigQuery, Redshift, Postgres).
Consider: Learning Curve for Data Engineers
Learning Curve for Data Engineers: Dagster has Steeper - requires Python programming knowledge, while dbt (data build tool) has Gentler - SQL-first approach accessible to analytics engineers.
Community Size (GitHub Stars): Dagster has 8,900+ stars (as of 2026), while dbt (data build tool) has 21,000+ stars (as of 2026).
ELT vs Orchestration Focus: Dagster has Data orchestration and workflow automation platform, while dbt (data build tool) has Extract-Load-Transform (ELT) best practices and modeling framework.
Consider: Scheduling & Monitoring Built-in
Scheduling & Monitoring Built-in: Dagster has Native scheduling, monitoring, alerting, and retry logic included, while dbt (data build tool) has Requires external schedulers (Airflow, Cron, cloud-native tools).
Typical Use Case Maturity: Dagster has Complex multi-tool pipelines with 15+ dependencies, while dbt (data build tool) has Data warehouse transformations with 5-50 SQL models.