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Data Warehouse for Retail

Overview

This project aims to track the sale performance and inventory levels of products by branch over a year. The data warehouse utilizes a dimensional modeling approach and OLAP design for efficient querying.

Data Model

Fact Table

FACT: Sales, Inventory
Measurements: Sum(product), Sum(branchInventory)

Dimensions

Who?
    Customer(custID, jwID, cust_name)

What?
    Product(procID, jwID, name, description)

Where?
    Branch(branchID, jwID, Street, City, State)

When?
    Date(dateID, day, month, year)

Class Diagram

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Dimensional Modeling

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OLAP Database

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Dimension Tables

Dim_Product
Dim_Date
Dim_Branch
Dim_Customer

Fact Tables

Fact_Inventory
Fact_Sales

Data Warehouse Process

Loading Dimensional Tables

Dim_Customer
    Generate Rows
    Table Output
    Table Input
    Combination Lookup/Update
    


Dim_Date
    Table Input (Orders)
    Calculator Step (Separate into day, month, year)
    Table Output

Example: Load Customer Process

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Example: Load Date Process

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Loading Fact Tables

Fact_Inventory
    Table Input (SQL Query)
    Lookup Steps (Dimension Tables)
    Load Fact Table Step

Fact_Sales
    Similar process as Fact_Inventory

Example: Fact Inventory SQL

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Loading into Tableau

Establish MySQL Connection
Visualize data with appropriate joins

Example: Tableau setup

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Tableau Dashboard

Branch Location Map
Product Sales Bar Chart
Inventory Level by Month
Total Sales by Month

Selecting filters in the first two sections dynamically updates the bottom visualizations.

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