Multidimensional schema is designed to build a data warehouse systems model.
The main purpose of these schemas is to address the needs of larger databases built for analytical purposes (OLAP).
This method is used to order data in the database with a good arrangement of the contents in a database. The schema allows customers to ask questions associated with business or market trends.
Furthermore, a multidimensional schema represents the data in the form of data cubes which enable viewing and modeling data from different perspectives and dimensions.
It’s of three types, but many confuse between star and Snowflake. Hence, it becomes difficult for them to choose the preferable model.
If you are one of them, let’s discuss the differences between the star and snowflake schema, starting with the definition and understanding their benefits, challenges, diagram, and characteristics.
What is a Multidimensional Schema?
Schema refers to the logical description of a complete database and data marts. It includes the name of records and their descriptions, including aggregates and associated data items.
A database generally uses a relational model to describe, whereas a data warehouse system uses a Schema model.
Multidimensional schema can be defined with Data Mining Query Language (DMQL).
To define the data marts and data warehouses, it uses two primitives – dimension definition and cube definition.
The multidimensional schema uses different types of schema models. They are:
Let’s discuss what star and Snowflake schemas are.
Star vs. Snowflake: What are They?
What is Star Schema?
A star schema is an architectural data warehousing and business intelligence model requiring a single fact table to store measured and transactional data. It also uses different smaller dimensional tables to hold attributes about business data.
It is named as per its structure. Like a star, the fact table takes its place at the center of the diagram, and small dimensional tables sit like branches to the center table to form a star-like structure.
Every star schema consists of a single fact table but multiple small dimensional tables. The fact tables include specific, measurable data that need to be analyzed, such as logged performance, financial data, or sales records. It may be a snap of historical data at a time or transactional.
Moreover, the Star schema is the simplest and most fundamental among the data warehouses and data mart schemas. It is efficient in handling basic queries. Star schema generally supports business intelligence, ad hoc queries, analytic application, and online analytical processing cubes.
Star schema also supports count, average, sum, and other aggregations of many records. Users can easily filter and group the aggregations by dimensions. For example, users generate queries like “find all the sales records in June” or “analyze total revenue from the XYZ office in 2022”.
What is Snowflake Schema?
A snowflake schema is a multidimensional data model which can also be known as the extension of the star schema. This is because dimension tables in the snowflake schema break down into subdimensions.
A schema is a snowflake if one and more dimension tables do not link directly to the fact table but rather connect through other dimension tables.
Snowflaking is a phenomenon that normalizes the dimension tables in a star schema. When you normalize all the dimension tables, the resulting structure resembles a snowflake containing a fact table in the middle of the structure.
In simple words, the snowflake schema consists of one fact table in the middle of the model, which is connected to dimension tables, which are again linked to other dimension tables. This schema is used to enhance the performance of the queries.
The model is created for quick, flexible querying across complex relationships and dimensions. It is helpful for one to many and many to many relationships among various dimensions levels.
Due to the tighter adherence to more normalization standards, you will get more storage efficiency. But, the data redundancy is negligible, and performance is low compared to denormalized data models like star schema.
Star vs. Snowflake: How Do They Work?
How does a Star Schema work?
The fact table in the middle of the star model stores two types of information – numeric and dimension attribute values. Let’s understand them with an example of a sales database.
Numeric values are unique to every row and data point. This doesn’t correlate to or relate to the data stored in another row. These are facts about a given transaction, such as total amount, order quantity, exact time, net profit, order ID, etc.
Dimensional attribute values are not storing any data directly rather, they store foreign key values for the row in a dimensional table. Different rows in the center table will reference this information, such as data value, sales employee ID, branch office ID, product ID, etc.
Dimension tables always store supporting information from the fact table. Every dimensional table relates to the column of a fact table along with a dimensional value and stores additional data about that value.
Example: The employee dimension table uses employee ID as the key value and also contains information, such as name, gender, address, and phone number. Similarly, a product dimension table stores information, including product name, color, first date to the market, manufacture cost, etc.
How does a Snowflake Schema Work?
Think of a snowflake design with a center box and different connections through that box to different dots. To maintain data marts and data warehouses, snowflake schema design comes into the picture.
It is similar to the star schema but with minute changes. Unlike star schema, snowflake schema extends its sub-dimension tables, which are linked to dimension tables.
The primary purpose of this model is to normalize the denormalized information of the star model. This way, it can solve common issues associated with a star schema.
At the core of the schema, you will find a fact table that links with the information contained in dimension tables. These tables again radiate outwards to sub-dimension tables that have detailed information describing the dimension table information.
Example: The snowflake schema contains a sales fact table and store location, line, family, product, and time dimension tables. The market dimensions consist of two dimension tables, with the store as a primary dimension table and the store’s location as the sub-dimension table. The product dimension has three sub-dimension tables mentioning a product, line, and family sub-dimension table.
Star vs. Snowflake: Characteristics
Characteristics of Star Schema
Star schema can filter data from normalized data to meet data warehousing needs. The unique key is generated from the associated information for each fact table to identify every row.
It provides fast calculations and aggregations, such as the revenue of income gained and total items sold at the end of every month. These details can be filtered according to the needs by framing suitable queries.
It is the measurement of events that includes finite number values consisting of the foreign key. These keys are related to the dimensional tables. There are various types of fact tables that are framed with values at an atomic level.
The transaction fact table contains data on specific events, such as sales and holidays.
Recording facts include given periods like account information at the end of the year or every quarter.
The dimensional table gives detailed data on attributes or records found in the center table.
The user is capable of own design a table according to the needs.
You can use star schema to accumulate snapshot tables.
Characteristics of Snowflake Schema
The snowflake schema needs small disk space.
This model is easy to implement due to its separate and main dimension tables.
The dimension tables contain at least two attributes to define information at multiple grains.
Due to multiple tables, the performance is low as compared to the star schema.
The snowflake schema has the highest data integrity level and low redundancies due to normalization.
Star vs. Snowflake: Advantages
Advantages of Star Schema
Star schema is the simplest way among the data mart schemas.
It has a simple reporting logic. This logic is implied dynamically.
It’s designed using feeding cubes applied through the Online Transaction Process to make cubes work efficiently and effectively.
Star schema is formed with simple logic and queries that are easy to extract from the transactional process.
It offers enhanced performance for reporting applications.
It’s deployed to control the quick recovery of data.
The filtered and selected information can be applied easily in different cases.
Advantages of Snowflake Schema
Star schema is used to develop query performance due to fewer disk storage requirements.
It offers greater scalability in the relationships between components and dimension levels.
It’s easier to maintain.
Star schema offers fast data retrieval.
It’s a common and simple data schema for data warehousing.
It helps enhance data quality.
The structured data reduces the issue of data integrity.
Star vs. Snowflake: Limitations
Limitations of Star Schema
It has a high denormalized and integrity state. The entire process will collapse if the user fails to update the data. The security and protections are also limited. In addition, the star schema is not as flexible as the analytical model. It doesn’t offer efficient support to various relationships.
Limitations of Snowflake Schema
The main limitation you will find with Snowflake is the additional maintenance efforts due to the increasing number of small dimension tables. Many complex queries make it challenging to find the required data. In addition, the implementation time of the question is high due to higher tables. This model is also rigid and requires higher maintenance costs.
Star vs. Snowflake: Differences
Star and Snowflake are types of multidimensional schema but have different structures and properties. The former is like a star, and the latter resembles a snowflake, defining their names.
In the star schema, only a single join build a relationship between the central fact table and side dimension tables. On the other hand, in the snowflake schema, multiple joins are needed to link to dimension tables.
Star schema is generally used when you have less number of rows in the dimension table, whereas snowflake schema is used when a dimension table is relatively big.
The diagram below differentiates the two models and how the dimension tables and the fact table are linked in different schemas.
Star schema uses more disk space.
Snowflake schema uses less disk space.
It has high data redundancy.
It has low data redundancy.
The dimension tables are denormalized, which means repeating the same value within the table.
The dimension tables are fully normalized.
It takes minimum time to execute the queries, resulting in better performance.
It takes more time than the star schema for the query execution, making it less performing than the star schema.
The query complexity is low.
The query complexity is higher than the star schema.
Due to high data redundancy, maintaining star schema is a bit difficult.
Due to low data redundancy, it is easy to maintain and change the snowflake schema.
Data integrity is high because data is stored redundantly where multiple copies exist in the dimension tables.
Data integrity is low as it completely normalizes the dimension tables.
Hierarchies for the dimension tables in the star schema are stored in the dimension table.
Hierarchies are divided into separate dimension tables.
It has a simple DB design.
It has a very complex DB design.
Multiple dimension tables surround a fact table.
The fact table is surrounded by dimension tables which are also surrounded by sub-dimension tables.
Star schema is easy to design and set up as direct relationships represent them.
On the other hand, the snowflake schema is a bit complex to set up.
Cube processing is faster.
Due to complex join, cube processing is a little slow.
It has a minimum number of foreign keys.
It has the maximum number of foreign keys.
Both Star and Snowflake schemas are useful in different sectors. So, deciding which is better among them is based on their requirements.
The snowflake schema is the extension of the star schema, where it normalizes the dimension tables in the star schema.
The star schema is simple in design, runs queries faster, and setup is easy. On the other hand, the snowflake schema is easier to maintain, takes less disk space, and is less prone to data integrity problems.
So, a star schema could be the better option if you need a simple design, fewer foreign keys, and faster cube processing. But, if you need less disk space, low data integrity, and low maintenance, the snowflake schema can be more suitable.
Durga Prasad Acharya is a Freelance Technical Writer who loves writing on emerging technologies, such as AI & ML, Cybersecurity, Web Hosting, SaaS, Cloud Computing, and more. Besides writing, he’s a web designer and is passionate about… read more
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