Development Analytics & Data Automation & Workflows

dbt

Analytics engineers and data teams reach for dbt when warehouse transformations have outgrown scattered SQL scripts. It turns SQL models into a versioned, testable dependency graph that fits Git-based development.

Runs on Web Windows macOS Linux Self-Hosted
Available on Web
Built for Startups Small Business Enterprise Developers
Overview

What is dbt?

How it changes analytics work

dbt puts transformation logic in version-controlled SQL, then runs that logic inside your warehouse. Analysts can work with software-engineering habits: code review, reusable models, dependency management, automated checks, and deployment environments. It is strongest after data has already been loaded into platforms such as Snowflake, BigQuery, Redshift, or Databricks; it is not an ingestion tool or a database.

Choosing Core or Cloud

dbt Core is the free command-line project and remains practical for teams willing to own scheduling, hosting, and CI. dbt Cloud adds a browser IDE, managed jobs, credentials, governance controls, and vendor support. The free Cloud tier suits individual development; paid seats and enterprise features raise costs as a team grows.

  • Core: Maximum control, more operational work.
  • Cloud: Faster setup, centralized administration, recurring seat fees.
Capabilities

Key features

Build Modular Models

Refactor SQL into reusable models with explicit upstream references.

Data Quality Tests

Add schema, relationship, accepted-value, and custom SQL checks.

Generate Documentation

Publish model descriptions, lineage, columns, and test status.

Git-Native Workflow

Develop through branches, pull requests, and environment-aware deployments.

Run Incremental Models

Process only new or changed warehouse records.

Cloud Job Orchestration

Schedule production runs and inspect failures in dbt Cloud.

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