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AlexanderPowBI's avatar
AlexanderPowBI
Icon for Resolver I rankResolver I
1 year ago
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Pros and cons of using SQL in notebook to write data to warehouse vs. stored procedures

Hi, When writing data from a lakehouse to a warehouse (and doing some transformations), I am wondering about theese two options:   1. Create stored procedure in warehouse and trigger it from my pi...
data engineering
data pipeline
lakehouse
notebook
  • nilendraFabric's avatar
    nilendraFabric
    1 year ago

    Hello AlexanderPowBI 

     

    • If your workload involves frequent experimentation or requires integration with tools like Spark or Power BI, notebooks might be a better fit.
    • For standardized, high-performance ETL tasks that need to be reused across multiple pipelines or projects, stored procedures are more suitable.
    • In Microsoft Fabric specifically, stored procedures often outperform notebooks for large-scale transformations due to their tight integration with the warehouse engine.




    FeatureSQL in NotebooksStored Procedures
    InteractivityHighly interactive; great for exploration and debugging.Less interactive; designed for predefined batch execution.
    PerformanceMay incur overhead; not as optimized for large-scale operations.Precompiled; optimized for high-performance transformations.
    FlexibilitySupports integration with other languages (e.g., Python).Limited to T-SQL; less flexible for multi-language workflows.
    MaintainabilityCan become fragmented in production setups.Modular and reusable; easier to manage in production pipelines.
    SecurityRelies on workspace-level security; less granular control.Strong RBAC and object-level security controls available.

     

     

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ArwaAldoud's avatar
ArwaAldoud
Icon for Super User rankSuper User
1 year ago

Both options work, but the best choice depends on your needs.

I prefer using a notebook because it's more flexible you can mix SQL with Python/PySpark for advanced transformations. It's also easier to debug and test.

  • Heavy transformations are usually done in the lakehouse (using PySpark/Spark SQL).
  • Final transformations in the warehouse are often handled via stored procedures for efficiency.
  • Notebooks are great for quick prototyping or handling complex logic.