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AlexanderPowBI
Resolver I
Resolver I

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 pipeline

2. Write the SQL in a notebook and trigger it from my pipeline

 

Is there any major drawbacks of using any of the two approaches? 

 

//Alexander

1 ACCEPTED SOLUTION
nilendraFabric
Super User
Super User

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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2 REPLIES 2
ArwaAldoud
Super User
Super User

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.
nilendraFabric
Super User
Super User

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.

 

 

If this is helpful , please accept the answer and give kudos

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