Description
✅ What You Will Learn
📌 Module 1 – Architecture Introduction
-
Data Architecture Overview
-
Warehousing Fundamentals
-
Modern Data Stack
-
DBT Overview: Market Share, Competitors, and Architecture
📌 Module 2 – Getting Started with DBT
-
Installation & Environment Setup (DBT, Python, Snowflake)
-
DBT Interface Walkthrough
-
DBT Concepts: Models, Project Setup, and Materializations
-
SQL for DBT (DQL Commands)
-
Creating Low-Code SQL ETL Pipelines
📌 Module 3 – Advanced Concepts
-
DBT Snapshots & Hooks
-
Data Loading Techniques: Full Load vs Incremental Load
-
Jinja Templating in DBT
-
Implementing Row-Level Security
-
Orchestrating Pipelines & CI/CD with GitHub
-
DBT Best Practices
📌 Module 4 – Versioning, Documentation & Deployment
-
DBT Version Control & Governance
-
Testing Data Models
-
Auto-Generating Documentation
📌 Module 5 – Portfolio Building & Industrial Projects
Project 1 – E-Commerce Company Data Pipeline
📍 Setup Snowflake & Load Data
📍 Configure DBT
📍 Build Low-Code SQL Pipelines
📍 Create Facts & Data Marts
📍 Test Pipelines
📍 Deploy with Airflow
Project 2 – EdTech Platform ELT Modernization
📍 Configure DBT & Build Transformation Pipelines
📍 Implement CI/CD with GitHub
📍 Document Data Models using YAML
📍 Unit Testing with Generic & Custom Tests
📍 Deploy & Optimize Pipelines
📌 Module 6 – Resume, LinkedIn & Interview Strategies
-
Resume Building for Data Engineering Roles
-
Technical Interview Strategies
-
LinkedIn Profile Optimization
-
Networking & Job Search Techniques
👤 Who Is This Course For?
-
Data Engineers & Analysts looking to master DBT
-
Professionals moving from ETL to ELT pipelines
-
Anyone building a portfolio of real-world data projects
-
Job seekers preparing for data engineering interviews
Reviews
There are no reviews yet.