VKB Solution

Data Build Tool — Modern Data Transformation

DBT Training

DBT (Data Build Tool) has become the backbone of the modern data stack, transforming how data teams build, test, and document their data pipelines. Unlike traditional ETL tools, DBT works entirely in SQL and brings software engineering best practices — version control, testing, documentation, and CI/CD — to data transformation. If you're working with Snowflake, BigQuery, Redshift, or any modern cloud data warehouse, DBT is the tool you need to know.

4 WeeksIntermediateOnline / Offline
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Duration4 Weeks
LevelIntermediate
ModeOnline / Offline

What You'll Learn

Build modular, reusable SQL transformation models
Implement automated data testing with a single command
Auto-generate beautiful data documentation and lineage graphs
Use Jinja templating for dynamic, DRY SQL
Deploy DBT projects with CI/CD pipelines
Work with Snowflake, BigQuery, and Redshift

Course Curriculum

A structured, progressive curriculum built around real-world application.

1

Module 1

The Modern Data Stack & DBT Intro

  • What is the modern data stack?
  • Where DBT fits: ELT vs ETL
  • DBT Core vs DBT Cloud
  • Setting up your first DBT project
2

Module 2

Models, Sources & Seeds

  • Writing your first DBT model
  • Model materialization: table, view, incremental, ephemeral
  • Defining sources and source freshness tests
  • Seeds for static reference data
3

Module 3

Refs, Dependencies & DAG

  • The ref() function and model dependencies
  • Understanding the DAG (Directed Acyclic Graph)
  • Staging, intermediate, and mart layers
  • Best practices for project structure
4

Module 4

Tests, Docs & Jinja

  • Built-in tests: unique, not_null, accepted_values, relationships
  • Custom generic and singular tests
  • Generating and serving documentation
  • Jinja templating and macros for DRY SQL
5

Module 5

DBT Cloud, CI/CD & Capstone

  • DBT Cloud IDE and job scheduling
  • Connecting to GitHub for version control
  • CI/CD with slim builds and deferred execution
  • Capstone: full analytics engineering project

Career Outcomes

Build a well-structured DBT project from scratch
Write modular, tested, and documented SQL models
Use Jinja and macros to write DRY, maintainable SQL
Implement automated data quality tests
Deploy DBT with CI/CD pipelines
Work confidently as an Analytics Engineer

Tools You'll Use

DBT CoreDBT CloudSnowflake / BigQuery / RedshiftGit & GitHubVS Code

Who Is This For?

1Data analysts who want to level up to analytics engineering
2Data engineers working with modern cloud data warehouses
3SQL-proficient professionals entering the modern data stack
4Teams looking to bring software engineering practices to their data pipelines

Frequently Asked Questions

Everything you need to know before enrolling.

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