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ahmedshalabyy12's avatar
ahmedshalabyy12
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3 days ago
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Materialized view vs delta tables in gold layer

Hello,

I asked ChatGPT this question, but I got a useless answer from the AI, and it changed its opinion twice during the session. So I decided to ask the experts: from your previous experience, which is better 
Materialized views or delta tables in the gold layer? Both store the physical data, and both can do incremental refresh. But when and why, that's my question.


Thank you 

data engineering
lakehouse
  • v-sathmakuri's avatar
    v-sathmakuri
    2 days ago

    Hi ahmedshalabyy12​ ,

    For example, if Silver has millions of sales records:

    Gold Delta table: Store the cleansed, business ready Sales data. This is your source of truth and can be reused by Power BI, notebooks and other pipelines.
    Materialized view: If Power BI frequently needs Sales by Region and Month, create an MV that pre-computes that aggregation for faster queries.

    So, the approach would be :  Silver -> Gold Delta table -> Materialized views for specific performance needs

    In short, use Delta tables for the core Gold data and materialized views when you need to optimize frequently used queries/aggregations.

    Thanks!!

4 Replies

  • ipkus's avatar
    ipkus
    Frequent Visitor
    2 days ago

    Materialized Views do physically persist data, and under the covers they are stored as Delta data as well. The key distinction is not the storage format, but the purpose and management of the object.

    A Gold Delta table is typically your business-ready, reusable source of truth that can support many different analytical use cases. A Materialized View, on the other hand, is usually created to optimize a specific query pattern, such as pre-aggregating Sales by Region and Month. The Materialized View is automatically maintained and refreshed from its underlying tables.

    So while both ultimately store data in Delta format, the recommended pattern is still:

    Silver → Gold Delta Tables (source of truth) → Materialized Views (performance optimization for specific workloads)

    This provides both reusability and performance without duplicating business logic across multiple datasets.

    Also, consider creating Business views as well. MLV might start to capacity overhead if done across the board.

  • v-sathmakuri's avatar
    v-sathmakuri
    Icon for Community Support rankCommunity Support
    3 days ago

    Hi ahmedshalabyy12​ ,

    Thank you for reaching out to fabric community.

    I would make Delta tables the default choice for the Gold layer, since they offer greater control over transformations, schema, incremental processing, history and reuse across workloads.

    Materialized views are the better fit when the main objective is query performance for example, when pre-aggregating data or streamlining frequently run queries.

    In short, Delta tables should hold the authoritative Gold data, while materialized views serve as performance-optimized derivatives built on top of it.

    Thanks!!

    • ahmedshalabyy12's avatar
      ahmedshalabyy12
      Icon for Helper V rankHelper V
      3 days ago

      Please, if you can, elaborate more with an example since I know delta tables and optimized by Databricks or Fabric 

      • v-sathmakuri's avatar
        v-sathmakuri
        Icon for Community Support rankCommunity Support
        2 days ago

        Hi ahmedshalabyy12​ ,

        For example, if Silver has millions of sales records:

        Gold Delta table: Store the cleansed, business ready Sales data. This is your source of truth and can be reused by Power BI, notebooks and other pipelines.
        Materialized view: If Power BI frequently needs Sales by Region and Month, create an MV that pre-computes that aggregation for faster queries.

        So, the approach would be :  Silver -> Gold Delta table -> Materialized views for specific performance needs

        In short, use Delta tables for the core Gold data and materialized views when you need to optimize frequently used queries/aggregations.

        Thanks!!