LACC Data Warehouse for Restaurants ABC Questions

Design the data warehouse for Restaurants ABC

Restaurants ABC restaurant company supplies equipment to 55 different restaurants in 20 cities across US. They have 2 product lines: front of house equipment and kitchen equipment. For front of house equipment, they have products such as tables, chairs, tablecloths, napkin holders, cutlery and so on. For kitchen equipment, they have products such as saucepans, knives and chef clothing.

An operational database is used to support day-to-day operation. In the operational database, they record the following data:

For each customer, they record data such as Customer Name, Contact No, City and State.

For each product, they record data such as Product Name, Product Line. A product line contains many products.

For each transaction, they record data such as Order Date, Customer Name, Product Names, Sales, Cost, Profit, Salesperson Name. A transaction may contain many products.

For each Salesperson, they record data such as Salesperson Name, YearsWithABC, Department. A department has many salespersons.

Now they wish to construct a data warehouse and BI solution to help them analyze the performance in terms of sales, cost and profit. The data will be extracted from the operational database, transformed and loaded to the data warehouse. Your task is to design the structure of the data warehouse to meet the following requirements.

They wish to analyze the sales, cost, and profit by product and customer. They also would like to analyze the sales, cost and profit ratio by product line and time period (day, month, year). Last, they also wish to analyze the sales, cost and profit ratio by city and state. They also need to analyze the sales, cost, and profit by salesperson.

Questions:

What are the facts/measures in this case? How many dimension tables do we need in the data warehouse?

Design the fact table and dimension tables. Use a star schema.

1. What is the grain of the fact table?
2. Convert the star schema to a snowflake schema by normalizing the product table.
3. Your Turn:
4. Based on the current snowflake schema, further normalize the salesperson table.