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Awesomedev's avatar
Awesomedev
New Member
7 months ago
Solved

Lakehouse Data Modeling Tips in Fabric

Hello Fabric Community,

I’m currently preparing for the DP-600 exam and working through Microsoft Learn modules. I’ve started creating a Lakehouse workspace in Microsoft Fabric and want to make sure my data models are optimized.

Could anyone share best practices for structuring tables, relationships, and measures in a Lakehouse for analytics efficiency? Any tips or common pitfalls to avoid would be greatly appreciated.

Thank you in advance for your guidance!

— Schenado

  • 1) Separate Layers

    Use a layered approach inside the Lakehouse:

    • Bronze → Raw ingestion (no business logic)

    • Silver → Cleaned & transformed tables

    • Gold → Analytics-ready tables (star schema)

    For reporting efficiency, Power BI should ideally connect to the Gold layer.

     

    2) Always Model as a Star Schema

    Even in Lakehouse.

    Use:

    • Fact tables (transactions, events, metrics)

    • Dimension tables (Date, Customer, Product, Region, etc.)

    Avoid:

    • Snowflake schema unless necessary

    • Fact-to-fact relationships

    • Many-to-many unless unavoidable

    • Bi-directional unless justified

     

    3) Use Surrogate Keys (Not Natural Keys)

    In your Gold layer:

    • Create integer surrogate keys for dimensions

    • Avoid joining on text columns

    • Avoid GUID joins if possible

    This reduces storage size and improves relationship performance.

     

    4) Watch Cardinality

    High-cardinality columns:

    • Transaction IDs

    • Timestamps at second-level

    • Free-text descriptions

    Keep them out of dimensions and visuals if not needed.

3 Replies

  • 1) Separate Layers

    Use a layered approach inside the Lakehouse:

    • Bronze → Raw ingestion (no business logic)

    • Silver → Cleaned & transformed tables

    • Gold → Analytics-ready tables (star schema)

    For reporting efficiency, Power BI should ideally connect to the Gold layer.

     

    2) Always Model as a Star Schema

    Even in Lakehouse.

    Use:

    • Fact tables (transactions, events, metrics)

    • Dimension tables (Date, Customer, Product, Region, etc.)

    Avoid:

    • Snowflake schema unless necessary

    • Fact-to-fact relationships

    • Many-to-many unless unavoidable

    • Bi-directional unless justified

     

    3) Use Surrogate Keys (Not Natural Keys)

    In your Gold layer:

    • Create integer surrogate keys for dimensions

    • Avoid joining on text columns

    • Avoid GUID joins if possible

    This reduces storage size and improves relationship performance.

     

    4) Watch Cardinality

    High-cardinality columns:

    • Transaction IDs

    • Timestamps at second-level

    • Free-text descriptions

    Keep them out of dimensions and visuals if not needed.

  • v-karpurapud's avatar
    v-karpurapud
    Icon for Community Support rankCommunity Support

    Hi Awesomedev 

    Thank you for submitting your question to the Microsoft Fabric Community Forum, and thanks to cengizhanarslan  for helpful suggestions.

    Could you let us know if the suggested solution resolved your issue? If not, please share any additional details so we can assist further.

    Best regards,
    Community Support Team.

    • v-aatheeque's avatar
      v-aatheeque
      Icon for Community Support rankCommunity Support

      Hi Awesomedev 

      Have you had a chance to look through the responses shared earlier? If anything is still unclear, we’ll be happy to provide additional support.