Live Online Data Build Tool Course for Data Engineering

Live Course Module: DBT Course for Data Engineering

Total Duration: 16 Hours (4 Weeks)

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Week 1: Introduction to dbt and Core Concepts (4 hours)

1. Introduction to Modern Data Engineering (30 mins)

   • Overview of ELT vs ETL

   • The role of dbt in modern data stacks

   • Common dbt use cases in Data Engineering

2. Understanding dbt Architecture (45 mins)

   • dbt workflow and ecosystem

   • Integration with warehouses (Snowflake, BigQuery, Redshift, Databricks, etc.)

   • dbt CLI vs dbt Cloud

3. Setting up the Environment (1 hour)

   • Installing dbt (CLI & Cloud setup)

   • Connecting to data warehouses

   • Creating your first dbt project

4. dbt Project Structure Deep Dive (1 hour 45 mins)

   • Anatomy of a dbt project

   • Models, Seeds, Tests, Macros, and Snapshots overview

   • File structure and configuration files (dbt_project.yml, profiles.yml)

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Week 2: Building and Managing Models (4 hours)

1. dbt Models Explained (1 hour)

   • Creating and materializing models (view, table, incremental)

   • Configuring materializations in dbt_project.yml

   • Ref() function and dependency management

2. Working with Jinja and SQL in dbt (1 hour)

   • Jinja templating basics

   • Variables, loops, and macros

   • Dynamic SQL generation

3. Testing and Documentation (1 hour)

   • Data quality testing with dbt tests

   • Writing custom tests

   • Generating and viewing dbt documentation

4. Version Control & Collaboration (1 hour)

   • Managing dbt projects with Git

   • CI/CD pipeline integration basics

   • Working in teams (branching, pull requests)

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Week 3: Advanced dbt Features (4 hours)

1. dbt Sources & Seeds (1 hour)

   • Defining and using sources

   • Managing seed data

   • Source freshness checks

2. Macros, Hooks, and Packages (1 hour)

   • Creating and using macros

   • Using pre- and post-hooks

   • Installing and managing dbt packages

3. dbt Snapshots (1 hour)

   • Introduction to slowly changing dimensions (SCD)

   • Implementing snapshots

   • Snapshot performance considerations

4. dbt Cloud Advanced Features (1 hour)

   • Scheduling and running jobs

   • Using the IDE and job history

   • Setting up alerts and notifications

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Week 4: Deployment, Optimization & Real-World Project (4 hours)

1. Performance Optimization (1 hour)

   • Optimizing SQL and dbt models

   • Using incremental models efficiently

   • Model dependencies and parallelization

2. Orchestration and Automation (1 hour)

   • Orchestrating dbt with Airflow, Prefect, or Dagster

   • Integration with CI/CD tools (GitHub Actions, GitLab CI)

3. Real-World dbt Project (1.5 hours)

   • Hands-on building of an end-to-end dbt project

   • Connecting source data → transforming → testing → deploying

4. Final Review & Q&A (30 mins)

   • Recap of core dbt concepts

   • Discussion of best practices and industry standards

   • Resources for continued learning

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🧩 Optional Add-Ons / Capstone

• Capstone Project (2 hours – optional):
  Build a complete dbt project using sample data (e.g., from Snowflake or BigQuery public datasets).

• Assessment & Certification Quiz

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🧰 Tools & Technologies

• dbt CLI / dbt Cloud

• Git / GitHub

• SQL

• Cloud Warehouse (Snowflake / BigQuery / Redshift / Databricks)

• Jinja Templating