Last updated on 2024年9月29日

How do you choose between a star and a snowflake schema for your data warehouse design?

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Data warehouses are centralized repositories of integrated data from multiple sources that support analytical queries and decision making. To design a data warehouse, you need to choose a suitable schema that organizes the data into tables and relationships. Two common schemas are star and snowflake, which differ in how they handle dimensions and hierarchies. In this article, you will learn what are star and snowflake schemas, how they compare in terms of advantages and disadvantages, and how to choose between them based on your data warehouse requirements and goals.

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1 What are star and snowflake schemas?

A star schema is a simple and intuitive schema that consists of a single fact table and multiple dimension tables. A fact table contains the measures or metrics of interest, such as sales, revenue, or profit. A dimension table contains the attributes or characteristics of the facts, such as product, customer, or time. Each dimension table is connected to the fact table by a single foreign key, forming a star-like shape.

A snowflake schema is a variation of the star schema that normalizes the dimension tables into multiple levels of hierarchy. For example, a product dimension table can be split into sub-tables for category, subcategory, and brand. Each sub-table is connected to its parent table by a foreign key, forming a snowflake-like shape.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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Star and snowflake schemas are database structural designs used in data warehousing. A star schema has a central fact table connected to dimension tables, directly linking attributes to facts, simplifying queries and enhancing performance. The snowflake schema, a variation of the star, further normalizes dimension tables into sub-dimension tables, reducing data redundancy but increasing complexity. Star schemas are preferred for their simplicity and query efficiency, while snowflake schemas are chosen for their data integrity and storage efficiency.

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Thang Nguyen

?no pain no gain?
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The Star Schema model consists of one main table (Fact) and multiple auxiliary tables (Dimension) directly connected to the main table, forming a star shape. The dimension tables are denormalized and store detailed data about the objects related to the fact table. The Snowflake Schema is an extended version of the Star Schema, where the dimension tables are normalized into smaller tables. The main dimension tables can be linked to auxiliary dimension tables, creating a structure resembling a snowflake.

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2 What are the advantages and disadvantages of star and snowflake schemas?

The main advantage of the star schema is its simplicity and performance. It is easy to understand and maintain, as it has fewer tables and joins. It also allows for faster queries, as it reduces the number of disk accesses and minimizes the data redundancy. The star schema is suitable for data warehouses that have stable and well-defined dimensions and facts, and that require high query efficiency and scalability.

The main advantage of the snowflake schema is its flexibility and normalization. It can accommodate complex and dynamic dimensions and hierarchies, as it allows for more granular and accurate data representation. It also reduces the storage space and avoids data inconsistency, as it eliminates the duplicated data across the dimension tables. The snowflake schema is suitable for data warehouses that have variable and evolving dimensions and facts, and that require high data quality and integrity.

The main disadvantage of the star schema is its denormalization and redundancy. It can result in large and wide dimension tables that consume more storage space and increase the risk of data inconsistency. It also limits the depth and breadth of the dimension hierarchies, as it flattens them into a single table. The star schema is not suitable for data warehouses that have complex and changing dimensions and facts, and that require high data normalization and accuracy.

The main disadvantage of the snowflake schema is its complexity and performance. It can result in many and small dimension tables that complicate the data warehouse design and maintenance, as it requires more tables and joins. It also slows down the queries, as it increases the number of disk accesses and joins. The snowflake schema is not suitable for data warehouses that have simple and stable dimensions and facts, and that require high query speed and scalability.

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Thang Nguyen

?no pain no gain?
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Star Schema: Advantages: - Simplicity: The design is easy to understand and implement. - Performance: Queries are faster due to fewer joins.performance. Disadvantages: - Data Redundancy: Since it is not normalized, data may be duplicated multiple times. - Maintenance Challenges: Updating and synchronizing data becomes more complicated. Snowflake Schema: Advantages: - Normalization: Reduces data redundancy, making maintenance and updates easier. - Data Integrity: Data is better organized, ensuring integrity. Disadvantages: - Complexity: The design is more complex, making it harder to understand and implement. - Performance: Queries are slower as more joins are required to retrieve complete data.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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Star schemas offer simplicity in design and faster query performance due to fewer joins, making them ideal for analytical querying and reporting. However, they can lead to data redundancy and larger database sizes. Snowflake schemas, by normalizing dimension data into sub-dimensions, reduce redundancy and save storage space but at the cost of query complexity and performance, as more joins are needed. While star schemas are user-friendly for business intelligence tools, snowflake schemas offer better data integrity and are more efficient in handling updates.

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3 How to choose between star and snowflake schemas?

Deciding which schema is best for your data warehouse design is contingent on various factors, such as your data characteristics, query needs, storage constraints, and performance expectations. Generally speaking, a star schema is recommended if you have a few dimensions with low cardinality and limited levels of hierarchy. Additionally, if you need fast and simple queries with aggregated data and have ample storage space to tolerate some data redundancy, then this schema is ideal. On the other hand, a snowflake schema is best suited when you have many dimensions with high cardinality and multiple levels of hierarchy. This type of schema is also helpful when you need complex and detailed queries with granular data and want to avoid data inconsistency. Moreover, a hybrid approach that combines both schemas can be used depending on the specific data warehouse scenarios and trade-offs. For instance, a star schema can be used for simpler and more stable dimensions while a snowflake schema can be used for more complex and dynamic ones. Finally, you can also use a constellation schema that has multiple fact tables sharing some common dimension tables to support different business processes or analytical needs.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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Choosing between star and snowflake schemas depends on specific requirements: opt for a star schema if simplicity, fast query performance, and ease of understanding are priorities, making it suitable for most business intelligence scenarios. Choose a snowflake schema if reducing data redundancy, saving storage space, and maintaining data integrity are crucial, despite potentially slower query times. Consider the technical skill level of the end-users and the nature of the data analytics tasks. Star schemas are generally preferred for their efficiency and ease of use in data warehousing and reporting applications.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Greg Anderson

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Don't get too hung up on which schema format you're following. Do what it best for your model, your data, and your performance requirements. You do want to pay attention to model methodologies when starting a new project. A 3NF relational model is good for a transactional database but definitely not analytics. Star, snowflake, and similar methods are good for analytic models where we accept some redundancy and overhead in order to improve analytic performance. In all cases, the most important considerations relate to your data and your requirements. Don't be afraid to color outside the lines, as long as your data is kept valid and accessible.

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How do you choose between a star and a snowflake schema for your data warehouse design?

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1 What are star and snowflake schemas?

2 What are the advantages and disadvantages of star and snowflake schemas?

3 How to choose between star and snowflake schemas?

4 Here’s what else to consider

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