Data Warehousing
Data Warehousing — Architecture, Modelling and Delivery
From dimensional modelling to cloud data platforms — the discipline behind every modern analytics programme.
Data Warehousing covers the architectural backbone of analytics: dimensional modelling (Kimball), normalised modelling (Inmon), slowly changing dimensions, and modern cloud warehouse implementations on Snowflake, BigQuery, and Redshift. Students complete an end-to-end mini-project from source extract through dimensional model to a published dashboard.
What makes this programme worth your time.
Cloud Warehouse Lab
Hands-on instruction across Snowflake, BigQuery, and Redshift trial environments.
End-to-End Mini Project
Each student delivers a source-to-dashboard mini-project as a portfolio artefact.
Modelling Discipline
Both Kimball and Inmon traditions — practitioners learn when each is appropriate.
A careful progression, in four modules.
Module I · Foundations
- —Warehouse vs. lake vs. lakehouse
- —Dimensional modelling principles
- —Star and snowflake schemas
Module II · Modelling Patterns
- —Slowly changing dimensions
- —Conformed dimensions
- —Bridges and factless facts
Module III · Cloud Implementation
- —Snowflake architecture
- —BigQuery patterns
- —Redshift fundamentals
Module IV · Delivery & Quality
- —Pipeline orchestration
- —Testing and reconciliation
- —Documentation and stewardship
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