Organizations are generating massive amounts of data, so systems must be put in place to gather and process it all for analysis. The kind of data storage a business uses will determine how well it can leverage the data it gathers. There are two basic approaches: star schema, which is the simplest and involves a fact table in the middle that connects to dimension tables surrounding it to form the shape of a star; and snowflake, which is a more complex method that involves connecting fact tables, dimension tables, and sub-dimension tables through foreign keys to store data. Data architects must consider speed, cost, security, and dependability, among others about the schema they intend to use for storage.
Star schema is the simplest technique of structuring the data in the data warehouse. It includes the fact table in the middle linking to the dimension tables around it. The fact table collects data on metrics and the dimensions tables contain data on attributes. These are fact data about an organization, and the descriptive data linked to it, which are the two categories in the schema. Deformalized data is always present in star schema where extra data is inserted into the relational database at the cost of write speed to serve better read speed. Some examples of an attribute fact table could be an employee’s name, the sales price, the quantity of items sold, and distance measures. Now, dimension tables that would be linked with such tables are John, Bob, and other specific names for the employees and other numerical values for the other parameters in the fact table.
Snowflake schema consists of three types of tables: The enterprise’s data structure is carried out by fact tables, dimension tables, and sub-dimension tables. The fact table is the key table in the schema which contains data about the activity of a business. Specific details of the facts are recorded in the dimension table. Sub-dimension tables are created based on the fact that dimension tables of the snowflake schema are already normalized.
For instance, if a school has developed a database for enrollment of students and grading of the students, then. The data architect might want to make “Enrollment” the key fact table. Some dimension tables linked to the fact table could be the “Students” table which contains data relating to students, the “Courses” table which holds data about courses on offer, and the ‘Teachers’ table with data based on teachers.
There could be sub-dimension tables named “Students: Parental education,” “Family background,” or “Career objectives” under a primary “Students” dimension table similarly, “Courses: Language,” “Science,” “Commerce,” “Teachers: Doctorate,” “Sports,” “Physical education”.
All three tables are related based on foreign keys. A foreign key is a column(s) that requires the values to closely match the values of a column in the other related table. For instance, if there is a restaurant database consisting of the orders table and the customer’s table. If, in this case, the algorithm generates a column order. Customer. id referencing the customers. Id primary key, any value of orders that is inserted or updated. Customer. It must exactly match the numeral in the customer’s table. Id.
Emphasizing a point, it can be said that an objective of the snowflake schema is to normalize the star schema’s denormalized data. Normalization is the process through which data is arranged. For the time being until full most normalization is achieved, the dimension tables are nymphed into multiple sub-tables. Lastly, the snowflake schema is a variation of the star schema. The components are complex, well-defined, and intertwining with each other and that is why it is called snowflake schema.
| No. | Star Schema | Snowflake Schema |
| Structure | Consists of a centralized fact table connected to dimension tables in a star-like structure | Consists of a centralized fact table connected to multiple-dimension tables in a hierarchical manner |
| Normalization | Partially denormalized design | Highly normalized design |
| Query Performance | Better for simple queries and aggregations | Excellent for complex queries and aggregations |
| Storage Efficiency | Less efficient due to denormalization | Highly efficient for storing data |
| Scalability | Limited scalability due to denormalization | Highly scalable due to the separation of data |
| Data Integrity | Lower data integrity due to denormalization | Ensures high data integrity |
| Complexity | Simpler to design and maintain | More complex to design and maintain |
| Flexibility | Less flexible for changes in the data model | More flexible for changes in the data model |
| Usage | Suitable for small to medium-sized data warehouses | Suitable for large, complex data warehouses |
| Storage Overhead | Requires more storage space | Requires less storage space |
| Foreign Keys | It has less number of foreign keys. | While it has a larger number of foreign keys. |
| Redundancy | It has high data redundancy. | While it has low data redundancy. |
In conclusion, the schema problem in DW and BI pertains to how a data warehouse should be properly organized per the needs of the company. The star schema and snowflake schema are two of the most commonly implemented approaches, though they are not identical. Due to its ability to assist decision-makers find information easily and easily implementing cloud data warehousing, a star schema could be the most appropriate for anyone looking for a simple solution. On the other hand, a snowflake schema may be advantageous where the need to fit the growing data demands may suit the structure.