What is DBT?

DBT stands for Data Build Tool. Founded in 2016 it is a tool that allows users to transform raw data into analytics ready datasets within warehouses. It is capable of allowing users to automate pipelines, have detailed version control and integrates with data warehouses such as snowflake. DBT was revolutionary because it improved accessibility to analysts, allowing them to write and test SQL without relying heavily on engineers. This overall improved reliability of pipelines, while also simplifying the deployment process.

DBT changed the classic flow of extract, transform and load (ETL), to extract, load then transform (ELT). So instead DBT extracts the data and loads it into a data warehouse. Once it is in the warehouse all transformation occurs within said warehouse. This method improves flow efficiency as heavy lifting is done within the data warehouse, meaning less reliance on external systems for power.

Where does DBT fit into the data pipelines?

The chart below represents the flow of data from raw source to transformed and ready for analysis within BI tools

How does DBT work?

DBT uses SQL select queries to define transformations. These are called models, and they turn raw data into analysis ready tables or views. Models support testing and documentation which aids in more efficient collaboration and higher data quality.

DBT can be written in DBT core or DBT cloud. DBT core is open source and allows users to run SQL transformations locally, however it required lots of manual setup. Alternatively, DBT cloud is a hosted version of DBT with a user friendly interface that can manage workflows, schedule jobs and allows cloud based collaboration. It has simplified set up in comparison to DBT core.

How does DBT fit into different data roles?

DBT allows for a flow between three main people; Data engineers, analytics engineers and data analysts. For data engineers DBT will allow them to manage overall pipelines and maintain the data platform. DBT allows Analytics engineers to do tasks such as providing clean transformed data ready for analysis and maintain documentation and definitions. Data analysts will perform tasks such as building dashboards, forecasting or working with business users to understand the data, acting more indirectly with DBT, but the functionality overall improves the quality and efficiency of requested datasets. These roles are not cut and dry, there can be blurring between the roles.

If you would like to learn more about DBT you can take courses directly through the DBT website for free! I have pasted a link to the fundamentals course below to get you started.

https://learn.getdbt.com/courses/dbt-fundamentals

Author:
Bethan Donovan
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