Master dbt for Data Transformation
Learn to build scalable, maintainable data transformation pipelines with dbt, designed for data analysts and engineers.
This Course Includes
- 30 Hours of Hands-on Training
- Tools: dbt Core, dbt Cloud, SQL
- Online Practical Labs
- Learn Data Modeling Skills
- Real-World Projects and Exercises
- Data Pipeline Development and Testing
Things You'll Learn
- dbt fundamentals and architecture
- SQL-based data transformation
- Building modular data models
- Testing and documentation in dbt
- Integrating dbt with data warehouses
Course Content
Introduction to dbt
- Overview of dbt and its role in analytics engineering.
- Key concepts: Models, Materializations, and Jinja.
- Comparing dbt Core vs. dbt Cloud.
- Hands-on exercise: Setting up a dbt project locally.
- Understanding the dbt workflow: Build, Test, Document.
- Real-world use case: Transforming raw sales data.
- Introduction to SQL for dbt transformations.
- Basic dbt commands: run, test, and docs.
Data Modeling with dbt
- Building modular and reusable SQL models.
- Using materializations: Table, View, Incremental, Ephemeral.
- Hands-on lab: Creating a staging model from raw data.
- Leveraging Jinja for dynamic SQL generation.
- Hands-on exercise: Building a fact table with aggregations.
- Best practices for naming conventions and file structure.
- Real-world scenario: Modeling customer analytics data.
- Refactoring legacy SQL into dbt models.
Testing and Documentation
- Writing data tests: Unique, Not Null, Relationships.
- Hands-on lab: Adding tests to a dbt project.
- Creating custom tests with macros.
- Generating and hosting dbt documentation.
- Hands-on exercise: Documenting a data pipeline.
- Ensuring data quality and reliability.
- Real-world case study: Testing a financial dataset.
- Automating tests in CI/CD pipelines.
Advanced dbt Features
- Using snapshots for tracking data changes over time.
- Hands-on lab: Implementing a snapshot for audit tracking.
- Advanced Jinja: Loops, conditionals, and macros.
- Managing dbt packages for reusable code.
- Hands-on exercise: Installing and using a dbt package.
- Optimizing performance with incremental models.
- Real-world example: Optimizing a marketing analytics pipeline.
- Version control with dbt and Git.
Integrating dbt with Data Warehouses
- Connecting dbt to Snowflake, BigQuery, or Redshift.
- Hands-on lab: Setting up dbt with a cloud data warehouse.
- Integrating dbt with orchestration tools like Airflow.
- Hands-on exercise: Scheduling dbt runs with Airflow.
- Managing credentials and profiles in dbt.
- Real-world scenario: Building an end-to-end ETL pipeline.
- Deploying dbt in production environments.
- Best practices for collaboration and deployment.
Why Choose This Course?
- Led by dbt experts with analytics experience
- Hands-on labs with real-world datasets
- Flexible online format for your schedule
- Practical projects to showcase your skills
- Prepares you for dbt certification