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powerbiexpert22
Impactful Individual
Impactful Individual

snowflake schema use cases

what are those use cases in power bi where we must go with snowflake schema?

1 ACCEPTED SOLUTION
Ritaf1983
Super User
Super User

Hi @powerbiexpert22 

In Power BI, the best practice for data modeling is to use a star schema whenever possible. Star schemas are simpler and often perform better, making them the preferred choice for most scenarios. However, there are specific cases where using a snowflake schema is appropriate and beneficial.

When to Use a Snowflake Schema in Power BI

1. Normalized Data Structure

  • Scenario: When your source data is highly normalized.
  • Reason: A snowflake schema mirrors the normalized structure, making it easier to map and integrate with your data sources without extensive denormalization.

2. Complex Data Relationships

  • Scenario: When your data model requires complex relationships among dimensions.
  • Reason: Snowflake schemas allow you to handle intricate hierarchies and many-to-many relationships more effectively by breaking down dimensions into multiple related tables.

3. Efficient Storage and Data Integrity

  • Scenario: When storage efficiency and data integrity are critical.
  • Reason: Snowflake schemas reduce data redundancy by normalizing dimension tables, which helps maintain data integrity and reduces storage requirements.

4. Data Consistency and Maintenance

  • Scenario: When maintaining data consistency across reports is crucial.
  • Reason: Normalized tables in a snowflake schema make it easier to update and manage data, ensuring consistent information across various reports and dashboards.

5. Large and Complex Data Models

  • Scenario: When dealing with large and complex data models that need to scale.
  • Reason: Snowflake schemas can handle large datasets and complex relationships more efficiently, making them suitable for enterprise-scale data models.

6. Reducing Data Duplication

  • Scenario: When you need to minimize data duplication.
  • Reason: By normalizing dimension tables, snowflake schemas reduce redundant data storage, which can improve query performance and manageability.

7. Detailed Hierarchical Reporting

  • Scenario: When your reporting requires detailed hierarchical structures.
  • Reason: Snowflake schemas are well-suited for representing detailed hierarchical relationships, allowing for more granular and flexible reporting
    For more information please refer :
    My favorite video :
    https://www.youtube.com/watch?v=-vTakVyIDUU
    And another good explanation:
    https://www.youtube.com/watch?v=vZndrBBPiQc

    If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly.

     

Regards,
Rita Fainshtein | Microsoft MVP
https://www.linkedin.com/in/rita-fainshtein/
Blog : https://www.madeiradata.com/profile/ritaf/profile

View solution in original post

1 REPLY 1
Ritaf1983
Super User
Super User

Hi @powerbiexpert22 

In Power BI, the best practice for data modeling is to use a star schema whenever possible. Star schemas are simpler and often perform better, making them the preferred choice for most scenarios. However, there are specific cases where using a snowflake schema is appropriate and beneficial.

When to Use a Snowflake Schema in Power BI

1. Normalized Data Structure

  • Scenario: When your source data is highly normalized.
  • Reason: A snowflake schema mirrors the normalized structure, making it easier to map and integrate with your data sources without extensive denormalization.

2. Complex Data Relationships

  • Scenario: When your data model requires complex relationships among dimensions.
  • Reason: Snowflake schemas allow you to handle intricate hierarchies and many-to-many relationships more effectively by breaking down dimensions into multiple related tables.

3. Efficient Storage and Data Integrity

  • Scenario: When storage efficiency and data integrity are critical.
  • Reason: Snowflake schemas reduce data redundancy by normalizing dimension tables, which helps maintain data integrity and reduces storage requirements.

4. Data Consistency and Maintenance

  • Scenario: When maintaining data consistency across reports is crucial.
  • Reason: Normalized tables in a snowflake schema make it easier to update and manage data, ensuring consistent information across various reports and dashboards.

5. Large and Complex Data Models

  • Scenario: When dealing with large and complex data models that need to scale.
  • Reason: Snowflake schemas can handle large datasets and complex relationships more efficiently, making them suitable for enterprise-scale data models.

6. Reducing Data Duplication

  • Scenario: When you need to minimize data duplication.
  • Reason: By normalizing dimension tables, snowflake schemas reduce redundant data storage, which can improve query performance and manageability.

7. Detailed Hierarchical Reporting

  • Scenario: When your reporting requires detailed hierarchical structures.
  • Reason: Snowflake schemas are well-suited for representing detailed hierarchical relationships, allowing for more granular and flexible reporting
    For more information please refer :
    My favorite video :
    https://www.youtube.com/watch?v=-vTakVyIDUU
    And another good explanation:
    https://www.youtube.com/watch?v=vZndrBBPiQc

    If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly.

     

Regards,
Rita Fainshtein | Microsoft MVP
https://www.linkedin.com/in/rita-fainshtein/
Blog : https://www.madeiradata.com/profile/ritaf/profile

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