SQL Data Warehousing - SQL Tutorials

Designing Efficient Data Models

Tutorial 3 of 5

# Designing Efficient Data Models

## **1. Introduction**
This tutorial aims to guide you through the basic concepts of designing efficient data models, with a particular focus on star schema and snowflake schema. By the end of this tutorial, you should have a good understanding of these concepts and know when to use each. 

**Prerequisites:**
A basic understanding of databases and SQL is required for this tutorial.

## **2. Step-by-Step Guide**

### **Concepts**
- **Star Schema:** A star schema (also known as star-join schema, data cube, or multi-dimensional schema) is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts.
- **Snowflake Schema:** The snowflake schema is a more complex data warehouse model than a star schema, and it is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake.

**When to use each schema**
- **Star Schema:**
  - Use when you need fast aggregation and simpler queries, at the expense of data redundancy.
- **Snowflake Schema:**
  - Use when you want to eliminate data redundancy and improve data integrity, at the cost of more complex queries and slower performance.

## **3. Code Examples**

### **Example 1: Star Schema**

Here is an example of creating a star schema in SQL:

```sql
-- Fact Table
CREATE TABLE Sales (
    ProductID int,
    OrderDate date,
    SalesAmount decimal
);

-- Dimension Tables
CREATE TABLE Product (
    ProductID int,
    ProductName varchar(255),
    ProductCategory varchar(255)
);

CREATE TABLE Date (
    OrderDate date,
    Month varchar(255),
    Year int
);

In this example, the Sales table is the fact table, while Product and Date are the dimension tables. The fact table contains the actual data to be analyzed, while the dimension tables contain descriptive information about the facts.

Example 2: Snowflake Schema

Here is an example of creating a snowflake schema in SQL:

-- Fact Table
CREATE TABLE Sales (
    ProductID int,
    OrderDateID int,
    SalesAmount decimal
);

-- Dimension Tables
CREATE TABLE Product (
    ProductID int,
    ProductName varchar(255),
    ProductCategoryID int
);

CREATE TABLE Category (
    CategoryID int,
    CategoryName varchar(255)
);

CREATE TABLE Date (
    OrderDateID int,
    Day int,
    Month int,
    Year int
);

In this example, the Sales table is the fact table, while Product, Category, and Date are the dimension tables. The dimension tables are normalized, reducing data redundancy.

4. Summary

In this tutorial, we have covered the basics of designing efficient data models, focusing on star schema and snowflake schema. For further learning, you could explore other types of data models such as the galaxy schema.

5. Practice Exercises

Design a star schema for a movie rental system where the fact table records rental transactions.
Convert the star schema from the previous exercise into a snowflake schema.
What are the advantages and disadvantages of using a star schema versus a snowflake schema?

Solutions:
1. The fact table could include RentalID, CustomerID, MovieID, and RentalDate. The dimension tables could be Customer, Movie, and Date.
2. The Customer and Movie tables could be normalized into more tables. For example, the Movie table could be split into Movie and Genre.
3. The star schema allows for fast data retrieval and is easier to understand, but it involves data redundancy. The snowflake schema eliminates redundancy and saves storage space, but it involves more complex queries and slower data retrieval.

Remember, practice is key when learning new concepts. Keep practicing and you'll get there.
```