Modern Data Pipeline Development
Enable teams to build modular, version-controlled data pipelines that transform raw data into analytics-ready datasets.
dbt
Data transformation and analytics engineering platform
By dbt Labs
Data Transformation & Analytics Engineering
Data Transformation
Analytics Engineering
SQL
Product Overview
dbt (data build tool) is a powerful data transformation tool that enables data analysts and engineers to transform raw data into actionable insights using SQL. It simplifies data workflows by modeling data in a modular, version-controlled approach and automating data transformations in the modern data stack.
dbt empowers teams to collaboratively develop and maintain data models using SQL, with built-in testing, documentation, and deployment capabilities. It integrates seamlessly with cloud data warehouses and supports automation to improve data accuracy and speed to insights. dbt fosters analytics engineering best practices, enabling reliable and scalable data pipelines.
Headquarters and Est. In
Philadelphia, United States — Est. 2016
No. of Employees
201-500
Customer Demography
Global
Customer Domains
Technology
Finance
Healthcare
Retail
SaaS
Use Case Deep Dive
Interactive analysis dashboard - explore detailed performance insights for key business scenarios
Data Quality Assurance and Monitoring
Continuously validate and monitor data transformations to ensure high data quality.
Cloud Data Warehouse Optimization
Optimize data transformation workflows to reduce cost and improve performance in cloud warehouses.
Analytics Engineering Collaboration
Facilitate collaboration between data engineers, analysts, and stakeholders using dbt's version control and documentation features.
Self-Service Analytics Enablement
Prepare clean, documented datasets for downstream BI tools enabling self-service analytics.
Continuous Integration & Deployment for Data
Implement modern CI/CD pipelines to deploy data transformations safely and automatically.
Multi-Tenant Data Environments
Support multiple business units or clients with isolated data pipelines and environments.
Complex Business Logic Implementation
Use extensible SQL and macros to implement sophisticated business logic in data transformations.
Data Governance and Compliance
Maintain governance, compliance, and auditability of data transformation activities.
Use Case Details
Integration Details
Feature Details
Key Features
Explore the core capabilities that make dbt stand out.
Modular SQL Data Modeling
Write modular SQL models to transform raw data into clean datasets.
Automated Data Testing
Define and run tests to validate data quality across transformation stages.
Incremental Model Builds
Build data models incrementally to handle large datasets efficiently.
Comprehensive Documentation Generation
Automatically generate documentation for data models and lineage.
Version Control with Git Integration
Manage dbt projects using Git for collaboration and change tracking.
Data Lineage Visualization
Visualize dependencies and relationships between data models.
Built-in Scheduler and Orchestration
Schedule dbt models to run automatically on specified intervals.
Integration with Cloud Data Warehouses
Supports seamless integration with major cloud data warehouses.
Support for Hooks and Macros
Customize and extend transformations with reusable hooks and macros.
Data Freshness and Testing Alerts
Monitor data recency and test results with alerting capabilities.
Command-line Interface (CLI)
Run dbt commands interactively or within automated scripts.
Documentation Versioning
Capture and manage documentation versions corresponding to code changes.
Multi-Environment Support
Configure dbt projects to deploy across development, staging, and production environments.
Seamless Integration with BI Tools
Prepare clean datasets optimized for business intelligence and analytics.
Integrated Metadata Management
Manage metadata such as descriptions, tags, and sources within dbt projects.
SQL Parser and Compiler
Parses and compiles SQL code for optimized execution in target warehouses.
Advanced Dependency Graph Management
Automatically manages dependency graphs for all models and tests.
Support for Custom Materializations
Define how models are materialized as tables, views, or incremental loads.
Multi-Cloud and Hybrid Environment Support
Deploy dbt across cloud platforms and on-premises data warehouses.
Robust Logging and Audit Trails
Maintain detailed logs of builds, tests, and runs for audit and troubleshooting.
Extensive Community and Marketplace
Access a rich ecosystem of packages, plugins, and community support.
Cloud IDE and Development Environment
Develop, run, and schedule dbt projects in a managed cloud environment.
Role-Based Access Control
Manage access permissions for users and teams within dbt Cloud.
Contextual Integrations
Not just "integrates with" – here's the specific value each integration delivers:
Snowflake
Delivers: Cloud data warehouse platform supporting high-performance data storage and querying.
Google BigQuery
Delivers: Serverless, highly scalable, and cost-effective multi-cloud data warehouse.
Amazon Redshift
Delivers: Fully managed petabyte-scale cloud data warehouse service.
dbt Cloud
Delivers: Managed cloud platform for developing, running, and orchestrating dbt projects.
GitHub
Delivers: Source code hosting platform for version control and collaboration.
GitLab
Delivers: DevOps platform for source code repositories, CI/CD, and collaboration.
Resources
Latest insights, guides, and templates to accelerate your decisions.
Downloads
Downloads coming soon
Resources and templates will be available soon
Case Studies
Fivetran + dbt: Accelerating Analytics Engineering
JetBlue: Scaling Data Teams with dbt
Platform Updates
dbt Cloud 2024 Product Updates
Videos
Watch dbt in action.
What is dbt? Introduction and Overview
Getting Started with dbt
Pricing & Plans
dbt Core
Free
dbt Cloud Developer
Paid - per user
dbt Cloud Team and Enterprise
Custom pricing
Frequently Asked Questions
Common questions about dbt:
dbt is used for transforming raw data into clean, consumable datasets using modular SQL transformations, supporting analytics workflows and data modeling.
Yes, dbt requires knowledge of SQL and an understanding of data modeling concepts, though it abstracts engineering complexity via modular design.
dbt supports many cloud data warehouses including Snowflake, BigQuery, Redshift, Databricks, and others via its adapters.
Yes, dbt Core is open-source and free to use. dbt Cloud offers additional managed features under paid plans.
Yes, dbt supports automated tests to validate data quality and integrity during transformations.
Scheduling and orchestration are available through dbt Cloud or can be integrated via external workflow tools.
Implementation Partners
Partners listed for dbt and trusted teams available for implementation support.
No implementation partners are listed for this profile yet.
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