## Introduction
[dbt](https://www.getdbt.com/) (data build tool) is an open-source analytics engineering tool that enables data teams to transform, model, and document data in the warehouse using SQL and software engineering best practices. It empowers analysts to build scalable and maintainable data transformation workflows with version control, testing, and modular development.
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![[Tooling dbt.png]]
*dbt model of the staging layer for the Demonstration dataset*
## Features
- **SQL-Based Transformations**: Enables analysts to define data transformations using SQL.
- **Version Control**: Integrates with [[GitHub]] for collaboration and change tracking.
- **Automated Testing**: Ensures data quality with built-in assertions and testing capabilities.
- **Modular & Reusable Code**: Uses macros and Jinja templating for code reuse.
- **Dependency Management**: Organizes transformation workflows with directed acyclic graphs (DAGs).
- **Seamless Integration**: Works with Snowflake, BigQuery, Redshift, Databricks, and other modern warehouses.
- **Data Documentation**: Generates clear lineage, documentation, and cataloging for datasets.
## Applications
- **Analytics Engineering**: Bridges the gap between data engineering and analytics.
- **Data Transformation & Modeling**: Converts raw data into structured, analytics-ready datasets.
- **Data Quality Assurance**: Implements automated testing for consistent and reliable data.
- **Versioned Data Pipelines**: Enables collaborative and iterative development of data workflows.
- **Orchestration & Workflow Management**: Integrates with Airflow, Prefect, and other orchestration tools.
## Best Practices
- **Follow a Layered Approach**: Structure models into staging, intermediate, and final layers.
- **Use Incremental Models**: Optimize performance by processing only new or updated data.
- **Leverage Testing & Documentation**: Ensure data integrity with automated tests and clear documentation.
- **Implement CI/CD for Data Pipelines**: Automate deployments with continuous integration workflows.
- **Adopt Code Reusability**: Use macros and Jinja templates to standardize transformations.
## Pricing
dbt (data build tool) offers [pricing plans](https://www.getdbt.com/pricing) to suit various team sizes and needs:
1. **Developer Plan**:
- **Cost**: Free
- **Features**:
- One developer seat
- Up to 3,000 successful model builds per month
- Browser-based IDE, job scheduling, logging, alerting, data documentation, source freshness reporting, continuous integration, and native support for GitHub and GitLab
- **Note**: Exceeding 3,000 models in a month will pause subsequent runs until the next month or an upgrade to a paid plan.
2. **Team Plan**:
- **Cost**: $100 per developer seat per month
- **Features**:
- Up to 8 developer seats
- 15,000 successful model builds per month
- Includes all Developer Plan features, plus five read-only seats, unlimited concurrent running jobs, API access, outbound webhooks, and dbt Semantic Layer powered by MetricFlow
- **Additional Usage**: Beyond 15,000 models, additional builds are billed at $0.01 per successful model.
3. **Enterprise Plan**:
- **Cost**: Custom pricing
- **Features**:
- Custom number of developer seats
- Custom pricing per successful model built
- Includes all Team Plan features, plus unlimited projects, Single Sign-On (SSO), multiple deployment regions, Service Level Agreements (SLAs), professional services, role-based access controls, fine-grained Git permissions, audit logging, native support for GitHub, GitLab, and Azure DevOps, and advanced continuous integration
- **Note**: Pricing is tailored to organizational requirements and negotiated with dbt Labs.
## Usage
We use the dbt in each of our data architectures that we are reponsible for. Of all the tools we are accustomed to use, this one tool needs to be present, since it makes the transformation proces significantly more scalable and easier to maintain.