Prerequisites
- Proficient in SQL (SELECT, JOINs, CTEs, aggregations)
- Familiar with relational databases (PostgreSQL, Snowflake, BigQuery, etc.)
- Understanding of data warehousing concepts (ETL/ELT, star/snowflake schemas)
- Basic data modeling knowledge (fact and dimension tables)
- Comfortable with command-line tools
- Basic version control knowledge (Git)
- Optional: Python for advanced macros, BI tools experience, cloud data platform familiarity
Who Should Enroll?
- Data Analysts
- Data Engineers
- BI Developers
- Analytics Engineers
- Data Scientists
- Anyone working with SQL and data warehouses
What You Will Learn
- How to set up and configure dbt projects
- Writing modular, maintainable SQL models
- Implementing data transformations using dbt
- Creating staging, intermediate, and final models
- Understanding and using dbt macros and Jinja templating
- Managing dependencies between models
- Testing and validating data quality
- Documenting data models and generating dbt documentation
- Using version control (Git) with dbt projects
- Deploying dbt models to cloud data warehouses
- Optimizing performance and maintainability of data pipelines
Benefits of Taking dbt Course
- Build clean, reliable, and reusable data models
- Improve SQL and data transformation skills
- Ensure data quality with testing and validation
- Streamline collaboration using version control (Git)
- Enhance understanding of modern data warehousing workflows
- Document and maintain data pipelines efficiently
- Prepare data for analytics, BI, and data science projects
- Boost career opportunities in data engineering and analytics roles
Career Opportunities After Completing dbt Training
- Analytics Engineer
- Data Engineer
- BI Developer / BI Analyst
- Data Analyst
- Data Scientist
- ETL/ELT Specialist
- Cloud Data Platform Specialist
