dbt
SQL-native transformation framework that turns the warehouse into a tested, versioned analytics codebase
What is dbt?
dbt is a SQL-native transformation framework that lets analytics engineers model, test, and document data inside the warehouse. The new Fusion engine adds real-time validation, lineage via the dbt Catalog, and an Insights layer for cost-aware orchestration. It never stores data itself, running entirely on top of Snowflake, Databricks, BigQuery, and Redshift.
Data warehouses, analytics, business intelligence, product analytics, and AI data workflow tools.
See the full Data & Analytics guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Modeling raw Stripe and Salesforce data into clean revenue marts in Snowflake
Adding column-level tests and freshness checks before exposing tables to BI
Generating auto-published lineage docs that replace stale Confluence pages
Cutting warehouse spend by routing model runs through dbt Insights cost optimization
Fit to evaluate
Analytics engineers at Series B+ companies with a cloud warehouse
Data platform teams standardizing transformation across multiple business units
Consultancies delivering productionized warehouse modeling for clients
Enterprises adopting a data mesh with multiple federated dbt projects
Business fit
Right for you if your team writes SQL daily, owns a cloud warehouse, and is tired of brittle stored procs and undocumented dashboards. Skip if you have no dedicated analytics engineer, your data lives in spreadsheets, or your transformation needs are simple enough for a single Looker derived table. Mid-market and enterprise data teams get the most value from Catalog, Mesh, and Semantic Layer features. Solo analysts can prototype free on the Developer plan before paying.
How to evaluate dbt
Use this category when leaders need faster, clearer answers from business data.
Confirm the exact workflow
Map dbt to one concrete workflow first, such as modeling raw stripe and salesforce data into clean revenue marts in snowflake. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare data connectors, modeling, dashboarding, governance, and AI query features.
Compare practical alternatives
Shortlist dbt against Fivetran, Airbyte, Census so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Developer free (1 seat, 3,000 models/mo); Starter $100/user/mo (5 seats, 15,000 models); Enterprise and Enterprise+ custom annual with 100,000 models/mo and Mesh, Catalog Advanced, Cost Optimization. Also confirm implementation time, support needs, and whether the medium setup matches your team.
Compare dbt with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Modeling raw Stripe and Salesforce data into clean revenue marts in Snowflake, Adding column-level tests and freshness checks before exposing tables to BI |
|---|---|
| Best-fit team | Analytics engineers at Series B+ companies with a cloud warehouse, Data platform teams standardizing transformation across multiple business units |
| Implementation effort | Medium setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | FivetranAirbyteCensusHightouch |
dbt pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | Developer free (1 seat, 3,000 models/mo); Starter $100/user/mo (5 seats, 15,000 models); Enterprise and Enterprise+ custom annual with 100,000 models/mo and Mesh, Catalog Advanced, Cost Optimization. |
| Checked |
Common questions about dbt
What is dbt?
dbt is a SQL-native transformation framework that lets analytics engineers model, test, and document data inside the warehouse. The new Fusion engine adds real-time validation, lineage via the dbt Catalog, and an Insights layer for cost-aware orchestration. It never stores data itself, running entirely on top of Snowflake, Databricks, BigQuery, and Redshift.
What is dbt used for?
Common use cases: Modeling raw Stripe and Salesforce data into clean revenue marts in Snowflake; Adding column-level tests and freshness checks before exposing tables to BI; Generating auto-published lineage docs that replace stale Confluence pages; Cutting warehouse spend by routing model runs through dbt Insights cost optimization.
How much does dbt cost?
Developer free (1 seat, 3,000 models/mo); Starter $100/user/mo (5 seats, 15,000 models); Enterprise and Enterprise+ custom annual with 100,000 models/mo and Mesh, Catalog Advanced, Cost Optimization.
Who is dbt best for?
dbt fits Analytics engineers at Series B+ companies with a cloud warehouse, Data platform teams standardizing transformation across multiple business units, Consultancies delivering productionized warehouse modeling for clients, Enterprises adopting a data mesh with multiple federated dbt projects. Right for you if your team writes SQL daily, owns a cloud warehouse, and is tired of brittle stored procs and undocumented dashboards. Skip if you have no dedicated analytics engineer, your data lives in spreadsheets, or your transformation needs are simple enough for a single Looker derived table. Mid-market and enterprise data teams get the most value from Catalog, Mesh, and Semantic Layer features. Solo analysts can prototype free on the Developer plan before paying.
What are alternatives to dbt?
Common alternatives to dbt include Fivetran, Airbyte, Census, Hightouch, Segment, Mixpanel.