In today’s data-driven world, choosing the right schema to store data is equally important as collecting it. Schema design plays a crucial role in the performance, scalability, and usability of your data systems. Different data use cases require the selection of different schema designs. It can depend on various factors like the complexity of your data, storage limitations, and performance requirements. Star and Snowflake schemas are two data modeling approaches used for designing data warehouses. Both the schemas have their own set of benefits and limitations. In this article, we discuss the differences between star schema vs snowflake schema, its advantages and disadvantages, key schemas, and when to use each.
What is a Star Schema?
A star schema is a data modeling design that consists of a central fact table that can connect with multiple dimension tables. Before moving on let us understand what fact and dimension table.
- Fact table: The fact table consists of quantitative data on a business process, e.g., sales amount, revenue, etc.
- Dimension table: Dimension tables hold descriptive or additional information about the facts mentioned, e.g., product name and category or customer detailed information, etc.
Since this connection forms star star-like structure, it is called a star schema. It is the most popular and accepted design used in warehouses. Star schema is denormalized which helps in simplifying querying and enhancing performance for workloads. It is easy to understand and navigate which makes it popular for OLAP tools and reporting systems.
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Advantages of Star Schema
- Simple Querying: Since it is denormalized, it requires fewer joins. This helps in improving query performance and reducing complexity.
- User-Friendly Design: It is easy for non-technical users to understand and navigate the data model due to its intuitive nature.
- Optimized for BI Tools: It is compatible and effectively works with most OLAP tools for analytics and reporting.
- Fast Aggregations: Efficiently supports aggregate queries on large datasets.
You can take a look in detail how database schema design works step-by-step along with examples.
Disadvantages of Star schema
- Data Redundancy: While denormalization improves query performance. It may lead to data redundancy and increased storage requirements.
- Maintenance Challenges: Any updates and changes to the denormalized structure are complex.
- Not Ideal for Complex Relationships: In the case of a deeply nested hierarchy, it has a limited ability to handle such complex relationships.
- Scalability Issues: Querying with star schema may become less efficient with very large datasets and complex queries.
What is a Snowflake Schema?
Snowflake schema is star schema with further normalization. It is an extension of the former, where dimension tables are further broken down into sub-dimension tables. This structure creates a multi-level structure that is similar to a snowflake; hence, it was named the snowflake schema. It can be used where your data has complex relationships. Since it is further normalized, it can help reduce data redundancy and storage requirements but may end up increasing the complexity of queries.
Advantages of Snowflake Schema
- Reduced redundancy: Since it is a normalized structure it reduces data redundancy, thus saving storage space.
- Scalable design: In the case of complex datasets and large-scale analytics, it is a highly scalable design and suits better than star schema.
- Handle nested relationships: With sub-tables, it can handle multilevel and nested hierarchies efficiently.
- Consistency: Due to its normalized nature, you can ensure consistency and accuracy across data platforms.
Also, check out how the snowflake schema is used in creating the snowflake data model including the key features with an example.
Disadvantages of Snowflake Schema
- Complex Queries: While normalization has its own benefits it leads to complex queries, and more joins. This may lead to comparatively slow query performance.
- Higher Query Cost: Increased complexity of queries may require larger resources and may lead to longer query times. This may lead to higher costs.
- Challenging for Users: Since it is normalized, non-technical users may have a hard time understanding the relationships and structure of data.
- OLAP Tool Compatibility: It has comparatively limited compatibility with BI tools due to its normalized structure.
Key Differences between Star Schema vs Snowflake Schema
As we have already discussed both schemas, let us take a look at the difference.
| Feature | Star Schema | Snowflake Schema |
| Structure | Flat, Deformalized structure | Hierarchical, normalized, and multilevel architecture |
| Query Complexity | Fewer joins | May require more joins |
| Performance | Fast querying experience. | Low due to joins and complex nature |
| Storage | May require more space due to redundancy | Comparatively low due to removed redundancy |
| Maintenance | Challenging due to normalized nature | Easy to update and maintain |
| User Friendliness | Easy to navigate due to flat architecture | Challenging due to multi-level structure |
| BI Tools support | Highly compatible | Low compatibility |
| Supported Use Cases | Best for simple data models | It can be used for complex hierarchies |
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Structure
Star schema has a flat structure with denormalized tables. It has central tables connected to dimension tables. Snowflake schema, on the other hand, has a multi-layer structure where dimension tables are further normalized and broken down into sub-tables
Query Complexity
Queries in star schemas can be made with fewer joins, compared to snowflake queries, which require multiple joins to retrieve data. This makes queries in star schemas less complex and faster than snowflakes.
Performance
Star schemas are highly performant for analytics, especially with aggregation. Snowflake schemas, on the other hand, tradeoff performance for storage optimization, which makes them slow as compared to the former.
Storage
Due to denormalization star schemas may have redundant data, hence may use more storage. On the other snowflake, schemas reduce redundancy, optimizing storage.
Maintenance
A denormalized structure does not allow flexibility in updation and modification. Hence, snowflake schemas offer easier maintenance than star schemas.
Supported Use Cases
Star schemas are best for simple analytics and reporting, whereas snowflake schemas can be used for complex datasets and hierarchies.
End-User Friendliness
Star schemas are easy to navigate through due to their flat structure, especially for non-technical users. On the other hand, one may require some technical understanding to understand the multi-layered structure of snowflake schemas.
BI Tool Support
Star schemas are supported by most of the BI tools, while snowflake schemas are supported by comparatively fewer tools due to the complexity of the structure.
Choosing Between Star Schema vs Snowflake Schema
Here are some key pointers that should be taken into consideration before selecting the schema that would best suit your use case.
- Data Complexity: The nature and structure of your data are the main factors in deciding which schema to use. If your data use case has a simple relationship, then you should select star schema. On the other hand, if your data has a complex relationship and requires higher normalization then snowflakes should be considered.
- Data Volume: If your use case has a higher data volume and a large number of transactions, then you should consider using Snowflake schema to save storage and reduce data redundancy. On the other hand, you could use star schema in case of less complex and low-volume data. For example, in the case of real-time IOT data analytics, you could consider using Snowflake schema whereas in the case of a use case like an e-commerce site, you could consider using star schema.
- Resource Allocation and Limitation: Star schemas require less computational power as compared to snowflake schemas. On the other hand, in the case of storage, it is vice versa. Hence, you may need to consider your budget and resource constraints before choosing the schema.
- Platform compatibility: Star schemas are highly compatible with OLAP Tools and BI platforms, whereas snowflake schema may require advanced BI tools to handle its complexity. Therefore, compatibility should be taken into consideration before choosing a schema.
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Conclusion
Star and Snowflake schemas are two popular and widely accepted schemas for data warehousing. While both of them ship with their own sets of features and limitations, it is important to understand their difference and when to choose which. By understanding your analytics needs and aligning your schemas towards them, you can unlock data’s full potential. Hevo is a no-code data platform tool that can help simplify the integration of your data into both star and snowflake schemas as per your data analytics goals.
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FAQs
1. Why is star schema preferred?
Star schema is preferred for its high performance and less complex nature. It is also compatible with most of the BI Tools.
2. Is a star schema normalized or denormalized?
Star schema has a denormalized structure.
3. Is snowflake schema slower than star schema?
Yes, the snowflake schema tends to be slower than the star schema for querying due to its normalized design and complexity.
Neha has extensive experience in freelance consulting, encompassing strategic thinking, integrated marketing, and customer acquisition. She has driven growth for startups and established brands through comprehensive marketing communications, and digital strategies. She loves to share the knowledge acquired through her hand-on exposure with B2B SaaS products for more than a decade by creating impactful content.
