What is star schema and explain with example

Star Schema: A Fundamental Data Warehouse Design

A star schema is a common data warehouse design pattern where a central fact table is surrounded by multiple dimension tables. This structure facilitates efficient data analysis and reporting by organizing data into a hierarchical format. 

Key Components:

• Fact Table: Stores quantitative measurements (facts) related to a business process.

• Dimension Tables: Provide context and descriptive information about the facts, such as time, products, customers, or locations.

• Relationships: The fact table is connected to the dimension tables through foreign keys, creating a star-like structure.

Example: A Retail Sales Data Warehouse

Fact Table: Sales

SalesID	ProductID	CustomerID	DateID	Quantity	Price	Total
1	101	1001	20231001	2	29.99	59.98
2	102	1002	20231002	1	19.95	19.95
...	...	...	...	...	...	...

Dimension Tables:

• Products | ProductID | ProductName | Category | Price | |---|---|---|---| | 101 | Laptop | Electronics | 29.99 | | 102 | Book | Books | 19.95 | | ... | ... | ... | ... |

• Customers | CustomerID | CustomerName | City | State | |---|---|---|---| | 1001 | John Doe | New York | NY | | 1002 | Jane Smith | Los Angeles | CA | | ... | ... | ... | ... |

• Time | DateID | Date | Year | Quarter | Month | Day | |---|---|---|---|---|---| | 20231001 | 2023-10-01 | 2023 | Q3 | 10 | 1 | | 20231002 | 2023-10-02 | 2023 | Q3 | 10 | 2 | | ... | ... | ... | ... | ... | ... |

Visual Representation:

+--------------------+| Sales |+--------------------+| SalesID | ProductID | CustomerID | DateID | Quantity | Price | Total ||---|---|---|---|---|---|---|| ... | ... | ... | ... | ... | ... | ... |+--------------------+ | |+-------+ +-------+ +-------+| Products | | Customers | | Time |+-------+ +-------+ +-------+| ProductID | ProductName | CustomerID | CustomerName | DateID | Date | ... ||---|---|---|---|---|---|---|| ... | ... | ... | ... | ... | ... | ... |+-------+ +-------+ +-------+

Benefits of Star Schema:

• Simplicity: Easy to understand and implement.

• Performance: Optimized for analytical queries, especially when using OLAP cubes.

• Scalability: Can handle large datasets and complex analyses.

• Flexibility: Allows for adding or removing dimensions as business requirements change.

Star schemas are a widely used and effective data warehouse design pattern, providing a solid foundation for data analysis and reporting.

Sample Data for a Sales Fact Table

Note: These are sample values. You can customize them based on your specific business requirements and data.

SalesID	ProductID	CustomerID	DateID	Quantity	Price	Total
1001	101	1001	20231001	2	29.99	59.98
1002	102	1002	20231002	1	19.95	19.95
1003	101	1003	20231003	3	29.99	89.97
1004	103	1001	20231004	2	15.99	31.98
1005	102	1003	20231005	4	19.95	79.80
1006	104	1002	20231006	3	24.99	74.97
1007	101	1004	20231007	1	29.99	29.99
1008	103	1003	20231008	5	15.99	79.95
1009	102	1004	20231009	2	19.95	39.90
1010	105	1001	20231010	3	34.99	104.97
1011	103	1002	20231011	2	15.99	31.98
1012	104	1003	20231012	4	24.99	99.96
1013	101	1004	20231013	1	29.99	29.99
1014	105	1002	20231014	3	34.99	104.97
1015	102	1001	20231015	2	19.95	39.90
1016	103	1004	20231016	5	15.99	79.95
1017	104	1002	20231017	3	24.99	74.97
1018	101	1003	20231018	1	29.99	29.99
1019	105	1001	20231019	3	34.99	104.97
1020	102	1004	20231020	2	19.95	39.90

Sample Data for the Products Dimension Table

Note: The specific columns and data in a Products dimension table will depend on your business requirements. Here's a basic example:

ProductID	ProductName	Category	Subcategory	Price
101	Laptop	Electronics	Computers	29.99
102	Book	Books	Fiction	19.95
103	Smartphone	Electronics	Mobile Devices	499.99
104	T-Shirt	Clothing	Casual	19.99
105	Jeans	Clothing	Denim	39.99
106	Coffee Maker	Appliances	Kitchen	99.99
107	Headphones	Electronics	Audio	79.99
108	Backpack	Accessories	Bags	24.99
109	Watch	Accessories	Watches	149.99
110	Shoes	Footwear	Sneakers	59.99
111	Toy Car	Toys	Vehicles	12.99
112	Board Game	Games	Board Games	24.99
113	Blender	Appliances	Kitchen	59.99
114	Camera	Electronics	Photography	399.99
115	Dress	Clothing	Formal	79.99
116	Gaming Console	Electronics	Gaming	299.99
117	Cookware Set	Appliances	Kitchen	129.99
118	Belt	Accessories	Belts	19.99
119	Sunglasses	Accessories	Eyewear	29.99
120	Mug	Kitchenware	Drinkware	9.99

Explanation:

• ProductID: A unique identifier for each product.

• ProductName: The name of the product.

• Category: The broader category to which the product belongs.

• Subcategory: A more specific category within the main category.

• Price: The unit price of the product.

You can customize these columns and add or remove dimensions based on your specific business requirements. For example, you might include additional dimensions like brand, color, or size.

Sample Data for the Customers Dimension Table

CustomerID	CustomerName	City	State	Country
1001	John Doe	New York	NY	USA
1002	Jane Smith	Los Angeles	CA	USA
1003	David Lee	Chicago	IL	USA
1004	Emily Brown	Houston	TX	USA
1005	Michael Johnson	Phoenix	AZ	USA
1006	Sarah Miller	Dallas	TX	USA
1007	Thomas Wilson	Atlanta	GA	USA
1008	Olivia Taylor	Seattle	WA	USA
1009	James Clark	Boston	MA	USA
1010	Jennifer Davis	San Francisco	CA	USA
1011	William Hill	Detroit	MI	USA
1012	Victoria Harris	Philadelphia	PA	USA
1013	Daniel Carter	San Antonio	TX	USA
1014	Emma Jones	Indianapolis	IN	USA
1015	Christopher Lee	Jacksonville	FL	USA
1016	Ashley Williams	San Diego	CA	USA
1017	Benjamin Scott	Austin	TX	USA
1018	Abigail Baker	Columbus	OH	USA
1019	Ethan Hall	Las Vegas	NV	USA
1020	Sophia King	Memphis	TN	USA

Note: You can customize this table to include additional customer attributes as needed, such as email address, phone number, age, gender, and purchase history.

Sample Data for the Time Dimension Table

Note: This sample data includes common time attributes. You can customize it based on your specific reporting requirements.

DateID	Date	Year	Quarter	Month	DayOfWeek	WeekOfYear
20231001	2023-10-01	2023	4	10	Sunday	40
20231002	2023-10-02	2023	4	10	Monday	40
20231003	2023-10-03	2023	4	10	Tuesday	40
20231004	2023-10-04	2023	4	10	Wednesday	40
20231005	2023-10-05	2023	4	10	Thursday	40
20231006	2023-10-06	2023	4	10	Friday	40
20231007	2023-10-07	2023	4	10	Saturday	40
20231008	2023-10-08	2023	4	10	Sunday	41
20231009	2023-10-09	2023	4	10	Monday	41
20231010	2023-10-10	2023	4	10	Tuesday	41
20231011	2023-10-11	2023	4	10	Wednesday	41
20231012	2023-10-12	2023	4	10	Thursday	41
20231013	2023-10-13	2023	4	10	Friday	41
20231014	2023-10-14	2023	4	10	Saturday	41
20231015	2023-10-15	2023	4	10	Sunday	42
20231016	2023-10-16	2023	4	10	Monday	42
20231017	2023-10-17	2023	4	10	Tuesday	42
20231018	2023-10-18	2023	4	10	Wednesday	42
20231019	2023-10-19	2023	4	10	Thursday	42
20231020	2023-10-20	2023	4	10	Friday	42

Explanation:

• DateID: A unique identifier for the date.

• Date: The actual date in YYYY-MM-DD format.

• Year: The year of the date.

• Quarter: The quarter of the year (1, 2, 3, or 4).

• Month: The month of the year (1-12).

• DayOfWeek: The day of the week (Sunday, Monday, etc.).

• WeekOfYear: The week of the year.

This sample data provides various time-related attributes that can be used for analysis and reporting. You can customize the dimensions and levels of detail based on your specific requirements.