Business Intelligence Project
Project Members: Tufang (Coco) Xu, Akeel Qazi, Tommy Huynh
Year: 2022
Dimension Data Model ERD
Developing ELT and Staging in Snowflake
For this portion, I created a new Database in Snowflake and created staging tables. I then loaded data from the Azure Blob Storage into the staging tables.
The following are screenshots of the SELECT query for 3 of the 12 tables:
Dimension Loads in Snowflake
This query creates the necessary dimension tables and loads them from the staging table that I previously created.
Creating Fact Tables and Loading Data
This query creates the necessary fact tables and loads them from the staging tables.
Pass Through Views
This query creates the SQL "pass-through" views of each dimension and fact table. SQL "pass-through" views are exact replicas of your dimension and fact tables. They serve as the simplest form of a data access layer, protecting the data warehouse from direct queries and shielding downstream objects from any modifications made to the warehouse.
Visualization Views
SQL views are crucial for supporting the data visualizations in the next step. This is where visualization views will handle data complexities such as grouping, filtering, specialized calculations that are not present in the fact tables, intricate joins, etc. This part of the data access layer is more advanced, as it is needed to compensate for Tableau's limitations in aggregating and grouping view information.
Visualizations
