Week 9 - SQL for Analytics

Introduction to SQL for Analytics

Joins, CTEs, and Aggregations

3. Data Validation Queries

5. Data Modeling Concepts

6. Building Views in Azure PostgreSQL

7. Gotchas & Pitfalls

5. Data Modeling Concepts

Data Modeling Concepts

Content coming soon...

Suggested Topics

The three-layer model: raw (landing), staging (cleaned), marts (business-ready)
What grain means and why it is the most important modeling decision
Primary keys, surrogate keys, and natural keys
Facts: measurable events with numeric metrics (taxi trips with fare amounts, distances, durations)
Dimensions: descriptive context for facts (zones with borough and neighborhood names)
Star schema basics: one fact table surrounded by dimension tables (trips surrounded by zones and a date dimension)
The relationship between grain and joins: how wrong grain causes duplicated metrics
Naming conventions and documentation for models
Date dimension as a standard first dimension: date keys, fiscal periods, and time intelligence
How these concepts map to the views you will build in Azure PostgreSQL

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