Study Notes 4.1.2: What is dbt?

dbt (Data Build Tool) is a transformation workflow used for data engineering, allowing users to write and deploy analytical code using SQL or Python. It transforms raw data from multiple sources into meaningful formats for analysis. Companies have multiple data sources, including backend systems, frontend usage data, and third-party providers, which are loaded into a data warehouse for further processing. dbt sits on top of the data warehouse, converting raw data into business-ready insights and supporting integration with BI tools and machine learning workflows. dbt facilitates SQL/Python-based data transformation, automating complex data operations and ensuring data quality through testing, documentation, and version control. The transformation process follows data modeling techniques, involving writing SQL/Python transformation scripts, running dbt to compile and execute transformations, and storing transformed data as views or tables in the data warehouse. dbt incorporates modern software development best practices, including version control, modularity, CI/CD, DRY principles, development environments, and testing and documentation frameworks. dbt Core is an open-source, free-to-use command-line tool, while dbt Cloud is a SaaS version with additional features, including a web-based IDE and cloud-based orchestration. To set up dbt for a project, users can choose between using dbt Cloud with BigQuery or dbt Core with PostgreSQL. The course project demonstrates how dbt integrates with BigQuery and enables data transformation for business applications.