What is Star and Snowflake schema in Data Warehousing.
The process to collect data from various sources to be analyzed and to be used for insights is data warehousing. It is a combination of technologies and components that support the strategic use of data. It is an electronic storage of large amounts of information designed for query and analysis rather than transaction processing. It’s the process of transforming data into information and making it available to users in a timely manner that makes a difference.
As huge amount of data is generated everyday and stored electronically it needs to be organized in schemas, which will help to navigate through as well avoid duplication of data. Schemas commonly use visual representations to communicate the architecture of the database, becoming the foundation for an organization’s data management discipline. Database schema design helps to organize data into separate entities, making it easier to share a single schema within another database. Administrators can also control access through database permissions, adding another layer of security for more proprietary data.
Star schema
A star schema is a type of database model used in data warehouses. It is comprised of a central fact table surrounded by several dimension tables. The fact table is the primary data store, containing numerical measurements, metrics, or other aggregated data. The surrounding dimension tables store the context for each data point in the fact table, and contain information such as product categories, time, or customer demographics.
The star schema is an effective and efficient model for data warehousing because it facilitates the rapid retrieval of data, which is especially important when working with large datasets. Joining the fact table to the dimension tables provides users with the ability to query the data across multiple dimensions, allowing them to quickly and easily find answers to complex business questions.
The star schema has become a popular choice for data warehousing because it is simple and straightforward to implement. Unlike other models, it does not require the use of complex joins and other queries to connect tables, making it a great choice for beginners. Additionally, many modern data warehouse tools already support the star schema, allowing users to quickly and easily implement their data warehouse.
Overall, the star schema is an efficient and effective data model for data warehousing. It allows users to quickly and easily query large datasets across multiple dimensions and facilitates the rapid retrieval of data. Additionally, it is simple to implement, making it a great choice for beginners.
Snowflake Schema
A snowflake schema is a type of data warehouse architecture that organizes data into multiple, hierarchical layers of tables. The name comes from the fact that the data structure resembles a snowflake, with multiple points radiating outward from a single center. The center of the snowflake is the fact table, which stores the main data that the warehouse stores. The other points represent the dimensional tables, which contain related attributes that help define the fact table’s data. The dimension tables are further divided into their own sub-tables, making the snowflake architecture more complex and efficient than other data warehouse models.
In comparison to the star schema, the snowflake schema has more tables that are more interconnected and have more levels of hierarchy. This architecture allows for more flexibility in terms of querying, and provides a better way to store and manage large amounts of data. It also has the ability to join tables that are related, making the data retrieval process easier and more efficient.
The snowflake schema can be advantageous in a number of ways. It can help reduce the amount of storage space needed for the data, as the tables are more normalized. It can also provide more granular data access, allowing for better analytical queries and more accurate reports. Finally, it makes it easier to maintain and update the data, as all related tables are linked together.
In short, the snowflake schema is an efficient way to store data in a data warehouse. Its hierarchical structure makes it easier to store, manage, and retrieve large amounts of data. It also allows for greater flexibility and granular data access, making it an ideal choice for organizations with complex data requirements.
The star and snowflake schemas are composed of a central table connected to several other tables. In the star schema, the central table is referred to as the “fact table” and it is connected to several “dimension tables”. In the snowflake schema, the central table is also known as a “fact table” and is connected to multiple “dimension tables” as well as “subdimension tables”.
The primary difference between the star and snowflake schemas lies in the structure of the connected tables. In the star schema, all dimension tables are directly connected to the fact table. This makes the structure of the star schema flat and allows for faster queries. In the snowflake schema, the dimension tables are connected to other subdimension tables. This hierarchical structure allows for more complex queries but also slows query times. Additionally, the star schema is more appropriate for simpler data sets and single-subject queries, while the snowflake schema is better for larger, more complex data sets and multi-subject queries.
Overall, the star and snowflake schemas are two different types of database designs used to organize information. While they share similar characteristics, they also have important distinctions such as structure, query speed, and data complexity.