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dbt

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description dbt Overview

Data teams use dbt to define transformations that run inside supported data platforms. SQL models describe how source data becomes analysis-ready tables or views, while dependency handling determines how related models are built. Testing and documentation help teams make those transformations understandable and check whether important data assumptions hold. The tool supports workflows based on version control and collaborative review, making it useful for consistent business intelligence models and shared analytics definitions.

dbt is principally concerned with transformation after data has been loaded, so it commonly fits an ELT workflow rather than handling every stage of extraction and loading itself. Open-source dbt Core and hosted offerings support different operating arrangements. Its distinction is bringing software development practices to data modeling, helping teams maintain reusable logic instead of scattered queries.

help dbt FAQ

Is dbt an ETL or ELT tool?

dbt (data build tool) is strictly a data transformation tool, meaning it handles the "T" in an ELT (Extract, Load, Transform) process. The data must first be extracted and loaded into a data warehouse like Snowflake or BigQuery before dbt applies SQL transformations.

What language do you write dbt models in?

Data engineers write dbt models using standard SQL (Structured Query Language). dbt then compiles these SQL files and orchestrates their execution in the underlying data warehouse.

Does dbt offer a free version?

Yes, dbt offers dbt Core, which is a free and open-source command-line interface (CLI) tool. For teams that need a managed cloud environment with a visual interface, they offer paid tiered plans under dbt Cloud.

What is a dbt macro?

A macro in dbt is a piece of reusable Jinja code that allows developers to write SQL functions dynamically. Macros are heavily used to reduce repetition and keep complex data pipelines clean and maintainable.

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