Data Modeling for Analytics Engineers: Dimensional Modeling & dbt
Master data modeling techniques with dimensional modeling and dbt to build efficient and scalable data warehouses for analytics.
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Dimensional Modeling Fundamentals
Unit 1: Introduction to Dimensional Modeling
What is DM?
Facts, Dimensions, Measures
Star Schema
Unit 2: Designing Dimensional Models
Snowflake Schema
Grain Definition
Dimension Table Design
Fact Table Types
Unit 3: Practical Considerations
Conformed Dimensions
Junk Dimensions
Role-Playing Dimensions
Advanced Data Modeling Techniques
Unit 1: Slowly Changing Dimensions (SCDs)
Intro to SCDs
SCD Type 0: Retained Data
SCD Type 1: Overwriting
SCD Type 2: Add New Row
SCD Type 3: Add New Column
Unit 2: Advanced SCD Types and Data Vault
SCD Type 4: History Table
SCD Type 6: Combination
Intro to Data Vault
Data Vault vs. Star Schema
When to use Data Vault?
dbt for Data Modeling
Unit 1: dbt Fundamentals
dbt: The Big Picture
Setting Up Your dbt Project
dbt Project Structure
Unit 2: Building Data Models with dbt
Your First dbt Model
dbt's Jinja Templating
Macros: DRY Code in dbt
Ref and Source Functions
Unit 3: Testing and Documentation
Testing Your dbt Models
dbt Docs: Documenting Models
Snapshots: Capturing Changes
Data Model Optimization and Performance
Unit 1: Fundamentals of Data Model Optimization
Intro to Optimization
Cloud Warehouse Options
Query Execution Plans
Unit 2: Indexing and Partitioning
Indexing Strategies
Partitioning Techniques
Clustering Keys
Unit 3: Materialized Views and Optimization Best Practices
Materialized Views
Data Distribution
Query Optimization Tips
Monitoring & Tuning