dbt with Snowflake Training

Duration: 10–12 Hours (Real-time Interactive Session)
Format: Live, Hands-on Labs
Target Audience: Data Engineers, Analytics Engineers, Data Analysts
Prerequisites: Basic SQL knowledge & familiarity with data warehousing concepts

Modules Covered

Module 1: Introduction to Modern Data Stack
  • Evolution from ETL to ELT
  • dbt fundamentals
  • Snowflake overview & architecture
Module 2: Environment Setup & Configuration
  • Snowflake account & roles
  • dbt Cloud setup
  • Project initialization
Module 3: dbt Models & Sources
  • SQL models & materializations
  • Sources & freshness testing
  • Staging models
Module 4: Advanced dbt Features
  • Macros & Jinja
  • Incremental models
  • Snapshots & seeds
Module 5: Testing & Documentation
  • Data tests
  • Documentation & lineage
  • Data quality best practices
Module 6: Production Deployment & Orchestration
  • Environments
  • Job scheduling
  • CI/CD
Module 7: Snowflake-Specific Features
  • Functions & semi-structured data
  • Time travel & optimization
  • Integrations
Module 8: Real-world Project Implementation
  • End-to-end pipeline project
  • Optimization & debugging
  • Scaling & collaboration
Module 9: Best Practices & Advanced Topics
  • Style guide
  • Performance tuning
  • Enterprise considerations
Module 10: Q&A and Wrap-up
  • Challenges discussion
  • Certification paths
  • Next steps
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