In this dbt Crash Course, I will walk you through how to use dbt Core to run your data transformation workflow . This is going to be a crash course meant to be covering the basics for you to get started. For more advanced topics, I will be covering them in separate videos.

dbt stands for Data Build Tool, is an open-source data transformation and data warehousing tool created by dbt lab, designed to handle the complexity of modern data pipelines. It provides a framework for defining, testing, and deploying SQL transformations and helps automate the process of building, maintaining and updating a data warehouse. dbt enables data analysts, data engineers, and data scientists to build, version, and maintain a library of reusable data transformations.

📑 More on dbt (data build tool): [ Ссылка ]

📄 Profiles reference: [ Ссылка ]

📑 Supported databases: [ Ссылка ]

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00:00 - Intro
01:04 - Prerequisites
01:54 - Agenda
02:25 - Create dbt project Python virtual environment
03:44 - Install dbt Core CLI & database adapter
05:26 - Init dbt project
05:42 - Set up database connection (Google BigQuery)
11:08 - dbt dry run
14:13 - Push file to a GitHub repo
16:36 - Build dbt models
20:06 - Configure dbt model materialization
26:16 - Build dbt models on top of other models
30:43 - Testing
33:49 - Generate documentation

#dbt #databuildtool #dbtlab #dataengineering #dataanalytics

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