Goal: Get comfortable with Snowflake basics, SQL, and core data engineering concepts.

• Introduction to Snowflake cloud data platform
   
• Account setup, roles, and access management
   
• Snowflake architecture: storage, compute (warehouses), services layer
   
• Loading and unloading data (stages, COPY INTO, file formats, Snowpipe)
   
• Time Travel & Fail-safe
   

Hands-on labs:

• Create a Snowflake database and schema
   
• Load CSV/JSON/Parquet into Snowflake
   
• Query semi-structured data using VARIANT
   

• Advanced SQL in Snowflake: CTEs, window functions, clustering, materialized views
   
• Normalization vs. denormalization
   
• Star schema, Snowflake schema, and Data Vault modeling
   
• Partitioning, clustering, and performance tuning basics
   
• Intro to data governance: masking policies, RBAC
   

Hands-on labs:

• Create dimension & fact tables in Snowflake
   
• Optimize queries with clustering keys
   
• Implement row-level security
   

Goal: Build data transformations with dbt, integrate with version control, and apply testing.

• What is dbt? Role in the modern data stack
   
• Installing dbt Core & connecting to Snowflake
   
• dbt project structure (models, seeds, snapshots, macros)
   
• Writing modular SQL models in dbt
   
• Jinja templating basics in dbt
   

Hands-on labs:

• Set up dbt project with Snowflake connection
   
• Build staging and marts layers in dbt
   
• Implement snapshots for slowly changing dimensions (SCD Type 2)
   

• Testing & documentation in dbt
   
• dbt sources, exposures, and lineage
   
• dbt packages (dbt-utils, dbt-expectations)
   
• Macros, hooks, and operations
   
• dbt Cloud vs dbt Core: orchestration & deployment options
   

Hands-on labs:

• Implement unit tests on transformations
   
• Generate dbt docs and lineage graph
   
• Use dbt packages to extend transformations
   

Goal: Build, orchestrate, and deploy a full production-grade data pipeline.

• Scheduling dbt runs with dbt Cloud or Airflow
   
• CI/CD with GitHub/GitLab for dbt models
   
• Implementing dbt in a modern data workflow (CI/CD pipelines, pull request reviews)
   
• Logging, monitoring, and observability with dbt + Snowflake
   

Hands-on labs:

• Build CI/CD pipeline for dbt project
   
• Automate nightly dbt runs on Snowflake warehouse
   
• Integrate alerts for failed jobs
   

• Project Objective:
   
  • Ingest raw data into Snowflake (CSV/JSON/Parquet via Snowpipe or COPY).
     
  • Transform data with dbt into staging → core → analytics marts.
     
  • Apply testing, documentation, and CI/CD pipeline.
     
  • Build a dashboard (e.g., with Tableau or Power BI) connected to Snowflake marts.
     
• Deliverables:
   
  • Documented dbt project repo (GitHub/GitLab)
     
  • Snowflake schema with marts ready for BI
     
  • Final project presentation/report
     

By the end of this course, you will be able to:

1. Design data models and schemas in Snowflake.
    
2. Ingest & Transform data with dbt.
    
3. Test, Document, and Deploy data pipelines using CI/CD best practices.
    
4. Implement Governance and security features in Snowflake.
    
5. Build a Real Project showcasing your skills for portfolios or interviews.