Problematic Data Relationships

Data Ingestion and Semantic Models with Microsoft Fabric

Alex Kuntz

Head of Cloud Curriculum, DataCamp

Understanding Many-to-Many Relationships

  • What Are Many-to-Many Relationships?
    • Multiple records in one table relate to multiple records in another.

Cardinality - Many To Many

  • Why Are They Problematic?
    • Leads to confusing or inaccurate data.
    • Makes it difficult to track specific connections.
Data Ingestion and Semantic Models with Microsoft Fabric

How to Resolve Many-to-Many Relationships

  • What are Bridge Tables?
    • Links tables via a one-to-many relationship.
    • Example - It links CustomerID from Customers to ProductID from Products.

Bridge Table Bridge Table for Customer and Products

Customers Table:

CustomerID CustomerName City
101 John Doe New York
102 Jane Smith Los Angeles

Products Table:

ProductID ProductName Category
1 Laptop Electronics
2 Smartphone Electronics
Data Ingestion and Semantic Models with Microsoft Fabric

Understanding Circular Relationships

  • What Are Circular Relationships?:

    • A loop occurs between tables (e.g., A -> B -> C -> A)

  • Why Are They Problematic?

    • Filters get confused and return wrong results
    • Model struggles with circular data flow, causing errors

  • How to Resolve:

    • Break the loop by removing/adjusting relationships
    • Use DAX Measures or set relationships as inactive

Circular Relationship

Data Ingestion and Semantic Models with Microsoft Fabric

Star vs Snowflake: What is the Difference?

  • Star Schema:
    • Denormalized structure
    • Central fact table, fewer tables
    • Fast queries, ideal for small to medium datasets

Star Schema

  • Snowflake Schema:
    • Normalized structure
    • Fact table with sub-dimensions
    • Slower queries, best for large complex datasets

Data Ingestion and Semantic Models with Microsoft Fabric

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Data Ingestion and Semantic Models with Microsoft Fabric

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