Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
dbt (data build tool) is an open-source command-line tool that lets analysts and engineers transform data inside a cloud data warehouse using SQL, with version control, automated testing, documentation, and metric definitions built in.
dbt has become the de facto standard for data transformation in the modern data stack. Where ETL tools historically transformed data on intermediate servers before loading it into a warehouse, dbt enables ELT: load raw data first, then transform inside the warehouse with versioned SQL models.
The dbt project structure (models/, macros/, tests/, docs/) plus dbt Cloud or dbt Core gives data teams the same engineering rigour they had been missing — Git-versioned transformations, automated tests, documentation that auto-generates, and a metric layer (the dbt Semantic Layer) that exposes governed metrics to BI tools and AI agents.
A dbt project consists of:
Modern BI tools like Analytify integrate natively with dbt, reading dbt models directly without needing a translation layer.
A data team writes a dbt model models/marts/active_users.sql that defines active_users as users with at least one event in the past 30 days. They add a test that asserts active_users is never null and never higher than total_users. They document each column. dbt compiles the SQL for Snowflake, runs the tests, materialises the table, and generates docs. Every BI tool, dashboard, and AI agent now queries active_users from the dbt-managed table.
dbt ecosystem in 2026:
The open-source CLI. Free. Run from your laptop, GitHub Actions, or any orchestrator.
The paid managed service from dbt Labs. IDE, scheduling, hosted docs, semantic layer.
Metric layer exposing dbt-defined metrics for BI tool consumption.
Open-source BI tool that reads dbt models directly as the semantic layer.
Open-source GenBI platform with dbt-compatible semantic layer for SaaS embedded analytics.
Headless BI / semantic layer that complements dbt for API access to metrics.
dbt stands for “data build tool.” It is also the name of the company (dbt Labs) that maintains the open-source project and offers a paid cloud version.
dbt Core is open-source and free. dbt Cloud (the managed service) has a free Developer tier and paid Team / Enterprise tiers starting around $100/user/month.
dbt Core is a transformation tool. The dbt Semantic Layer (formerly MetricFlow) is a separate feature that exposes dbt metrics to BI tools and AI agents.
Airflow is a general-purpose workflow orchestrator. dbt is a SQL transformation tool. They are complementary: Airflow can schedule and orchestrate dbt runs alongside other tasks (extracts, loads, ML training).
Modern BI tools like Analytify, Lightdash, Mode, Hex, and Preset read dbt models directly. Older BI tools typically need the dbt Semantic Layer or a separate adapter.
ETL transforms data before loading it into the warehouse, often on a separate server. dbt-style ELT loads raw data first and transforms inside the warehouse using SQL. dbt is the dominant tool for the T in modern ELT.
Yes. Most modern data teams replace warehouse stored procedures with dbt models because dbt provides version control, testing, and documentation that stored procedures lack.