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# System prepended metadata

title: What is dbt ?

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# What is dbt ?


dbt (data build tool) is a tool that enables data analysts and analytic engineers to transform data in their warehouses more effectively. Created by [dbt Labs](https://www.getdbt.com/), dbt is now used by over 8,000 organizations who have modernized into the data cloud.

dbt is the T in ELT. dbt doesn’t extract or load data, but Transforms data that’s already loaded into your data cloud. This approach is known as (Extract, Load, Transform).

ELT has become commonplace because of the power of modern data clouds like [Snowflake](https://signup.snowflake.com/?owner=SPN-PID-126628), [BigQuery](https://cloud.google.com/bigquery) and [Firebolt](https://www.firebolt.io/). Data clouds like Snowflake are highly performant and emmensely scalable such that most data transformation use cases can be much more effectively handled in-database rather than in some external processing layer. Adding to this the separation of compute and storage and there are fewer reasons to want to execute your data transformation jobs elsewhere.

dbt’s only function is to take code, compile it to SQL, and then run against your database. If you are comfortable with writing SQL you will be comfortable building with dbt. dbt makes it a breeze to write SQL-based data transformation logic against a data cloud.

[DataLakeHouse](https://datalakehouse.io) leverages dbt for transformations within Snowflake and we absolutely love the tool! If you decide to extend the dbt models yourself you too will see how powerful and easy dbt is to use. 

dbt is key to building a [Data Vault](https://patrickcuba.medium.com/data-vault-pitch-904511875b34) allowing you to quickly pivot as your organization changes without a large data warehouse project. 

Below depicts where dbt fits into the data stack:
![](https://hackmd.io/_uploads/BJqZomekc.png)
