Study Notes 4.1.2: What is dbt?

1. Introduction to dbt

• dbt (Data Build Tool) is a transformation workflow used for data engineering.
• It allows writing and deploying analytical code using SQL or Python.
• The primary function is to transform raw data from multiple sources into meaningful formats for analysis.

2. Data Sources and Integration

• Companies have multiple data sources, such as:
  • Backend systems (e.g., apps, databases)
  • Frontend usage data (e.g., user interactions, web analytics)
  • Third-party providers (e.g., weather data, external APIs)
• This data is loaded into a data warehouse for further processing.
• Common data warehouses include:
  • BigQuery
  • Snowflake
  • Databricks
  • Fabric

3. Purpose and Benefits of dbt

• Sits on top of the data warehouse to transform data.
• Converts raw data into business-ready insights.
• Supports integration with BI tools (e.g., Tableau, Looker) and machine learning workflows.
• Ensures data quality through testing, documentation, and version control.

4. How dbt Works

• dbt facilitates SQL/Python-based data transformation.

• It automates complex data operations (DDL and DML generation):

  Database Operations in dbt

  In the context of dbt (Data Build Tool), DDL and DML refer to database operations that are used to manipulate and transform data.

  • DDL (Data Definition Language): DDL is used to define the structure of a database, including creating, modifying, and dropping tables, indexes, and other database objects. In dbt, DDL is used to create the underlying database schema for your data models.
  • DML (Data Manipulation Language): DML is used to manipulate the data within a database, including inserting, updating, and deleting data. In dbt, DML is used to perform transformations on your data, such as filtering, aggregating, and joining data from different sources.

  dbt automates the generation of DDL and DML statements, allowing you to focus on writing the logic for your data transformations without worrying about the underlying database operations.

• The transformation process follows data modeling techniques.

• Steps in the dbt workflow:

  1. Writing SQL/Python transformation scripts.
  2. Running dbt to compile and execute transformations.
  3. Storing transformed data as views or tables in the data warehouse.

5. dbt’s Software Engineering Practices

• dbt incorporates modern software development best practices, including:
  • Version control using Git.
  • Modularity for reusable components.
  • CI/CD (Continuous Integration/Continuous Deployment).
  • DRY (Don't Repeat Yourself) principles.
  • Development environments (e.g., sandbox environments for safe testing).
  • Testing and documentation framework.

6. dbt Core vs. dbt Cloud

• dbt Core:
  • Open-source, free to use.
  • Command-line tool for running dbt scripts.
  • Requires manual setup and integration.
• dbt Cloud:
  • A SaaS version of dbt Core with additional features.
  • Provides a web-based IDE for development.
  • Supports cloud-based orchestration of dbt projects.
  • Includes features like logging, hosted documentation, metadata APIs, and a semantic layer.

7. Setting Up dbt for a Project

• Two options for running dbt:
  1. Using dbt Cloud with BigQuery
     • No local installation required.
     • Free developer plan available.
  2. Using dbt Core with PostgreSQL
     • Local installation required (e.g., using VS Code).
     • Separate orchestration setup needed.

8. Course Project Overview

• The course involves working with taxi trip data.
• Steps in the project:
  1. Load raw data into Google Cloud Storage (GCP Buckets).
  2. Store data in BigQuery tables.
  3. Use dbt to transform the data.
  4. Enhance data with master data tables.
  5. Create dashboards for business insights.

9. Summary

• dbt is a powerful tool for data transformation in modern data engineering.
• Helps organize, test, and deploy analytical models efficiently.
• Supports best practices from software engineering to improve analytics workflows.
• dbt Core is free and open-source, while dbt Cloud provides additional cloud-based features.
• The course project demonstrates how dbt integrates with BigQuery and enables data transformation for business applications.

By following this course, learners will gain hands-on experience in dbt workflows, data transformation, and integration with data warehouses.