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What is Star Schema?
Star Schema is a type of database structure typically used in data warehouses. It is composed of a single, central table known as the fact table which stores all the quantitative information, and several other tables known as dimension tables which provide context to that data and describe different aspects of the same item being measured.
Star Schemas are well suited to measure certain types of data, such as sales order information or sums across multiple categories. Each connection between a dimension table and the fact table is called a star, hence the name Star Schema. Star Schemas allow easy storage and retrieval of multi-dimensional data while keeping complexity at a minimum. As complex queries become easier to perform, Star Schema acts as an important tool for analyzing large volumes of data quickly and accurately.
What is Snowflake Schema?
Snowflake Schema is a term used to describe a star schema-structured database, where the tabular data is stored in a set of normalized files (or tables) that are connected by defined foreign key relationships. Snowflake schemas are a popular design choice for organizations to store and query large datasets, as widespread normalization leads to greater query performance.
Snowflake schemas allow for faster access when retrieving data from multiple levels of complexity in relation to their Star schema counterpart, which requires only one join operation. Snowflake schemas also include additional flexibility – changes or updates made to tables at the lower level don’t affect higher levels of the structure, allowing for smarter use of resources over time.
Difference between Star and Snowflake Schema
Star and Snowflake Schema are two popular Database models used for data warehousing purposes, but there is a fundamental difference between the two. Star Schema has a single fact table, which is connected to other dimension tables all around it. This makes the structure appear in a radial or star-like shape.
On the other hand, Snowflake Schema has multiple dimension tables that fan out from the fact table. With centralized warehouses and higher degrees of normalization, complex queries can be served better with this schema model. It is important to understand these two models and know when to use which type so as to effectively build a dimensional data warehouse structure.
Star and snowflake schemas are two different ways of organizing data. The star schema is more popular because it’s easier to understand and use. However, the snowflake schema can be useful in certain cases. If you have a lot of data that needs to be organized in a specific way, the snowflake schema may be the best option for you.