DBT for Data Engineers Full Course 2026 | Basics to Advanced

DBT has become the standard transformation layer in modern data engineering — but most tutorials only show you how to write a few models and call it a day.

This DBT for Data Engineers Full Course 2026 | Basics to Advanced is built differently.

This is a complete, end-to-end deep dive into DBT — from foundational concepts to production-grade architecture used in real analytics engineering teams.

Everything is explained clearly, demonstrated practically, and structured the way real data engineers think about transformation systems.

No fluff. No theory overload. Just dbt done properly.

🚀 What You’ll Learn in This DBT Masterclass

In this full course, you’ll learn:
• What DBT actually does inside a modern data stack
• Understanding the DAG and model lineage
• Sources, staging layers, marts, and transformation architecture
• Materializations (view, table, incremental, ephemeral)
• Testing (generic + custom tests)
• Macros and Jinja for reusable logic
• Snapshots and slowly changing dimensions (SCDs)
• Seeds and reference data management
• Environment separation (dev / prod)
• Branching, versioning, and CI/CD workflows
• dbt project structure for large teams
• Real-world transformation design patterns

Every concept is demonstrated with practical examples — not toy queries.

You’ll see how to build a transformation layer the way professional analytics engineers and data engineers do in production environments.

🧱 Course Structure

This course moves step-by-step:
dbt Foundations
Building Models & Understanding the DAG
Testing & Documentation
Incremental Models & Performance
Macros & Reusability
Snapshots & Data History
Production Architecture & CI/CD

Each section builds on the previous one, so by the end you’ll understand dbt as a system — not just a tool.

🧰 Tech Stack & Tools Used

• dbt (Core concepts apply to both Core and Cloud)
• SQL (warehouse-agnostic patterns)
• Git & Branching workflows
• CI/CD concepts
• Modern cloud data warehouse concepts
• VS Code
• Docker

🎯 Who This Course Is For

• Data engineers
• Analytics engineers
• SQL developers moving into modern stacks
• BI engineers
• Anyone preparing for dbt-focused interviews
• Anyone building a transformation layer in production

📂 Resources & Code

GitHub Repository:
https://github.com/Jay61616/dbt-masterclass

Study Material:
https://1drv.ms/w/c/528673ccd24bac3c/IQBhyScZrPVASpEmatFap_x_AatXFwWuNlXhy2duKQHKhu8?e=XujhRh

🕒 Timestamps

0:26 Course Intro
2:58 What is DBT?

Phase 1 — Production Setup & Runtime

6:39 1. Production Architecture & Runtime Topology
38:43 2. Project Initialization & Connectivity
1:19:34 3. Git Foundation for DBT

Phase 2 — Core Modeling Engine

1:28:47 4. DBT Project Structure & Compilation Model
2:13:01 5. Dependency Graph & DAG Mechanics
2:51:34 6. Materializations & Incremental Strategy
3:15:36 7. Jinja & Macro System
3:49:24 8. Sources, Seeds & Snapshots

Phase 3 — Testing & Quality Enforcement

4:34:59 9. Schema & Custom Testing
4:57:20 10. Test Execution Strategy & CI Enforcement

Phase 4 — Documentation & Observability

5:25:03 11. YAML Specs & Documentation Site
5:50:40 12. Lineage & Impact Analysis

Phase 5 — Performance Engineering

5:59:26 13. Incremental Optimization & Performance Strategy
6:06:46 14. PostgreSQL Optimization

Phase 6 — Environment & Production Strategy

6:17:47 15. Dev vs Prod Architecture
6:26:28 16. Packaging & Modularity

📣 Let’s Connect

LinkedIn: https://linkedin.com/in/jayachandrakadiveti
GitHub: https://github.com/Jay61616
Website: https://jay.unaux.com

🎯 Hashtags

#dbt
#databuildtool
#dbtFullCourse
#DataEngineering
#AnalyticsEngineering
#ModernDataStack
#SQLTransformation
#DataWarehouse
#ETL
#ELT
#DataPipelines

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