topic Re: Pros and cons of using SQL in notebook to write data to warehouse vs. stored procedures in Data Engineering

Pros and cons of using SQL in notebook to write data to warehouse vs. stored procedures

AlexanderPowBI — Tue, 11 Feb 2025 10:35:45 GMT

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

Re: Pros and cons of using SQL in notebook to write data to warehouse vs. stored procedures

nilendraFabric — Tue, 11 Feb 2025 13:46:27 GMT

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.

Feature	SQL in Notebooks	Stored Procedures
Interactivity	Highly interactive; great for exploration and debugging.	Less interactive; designed for predefined batch execution.
Performance	May incur overhead; not as optimized for large-scale operations.	Precompiled; optimized for high-performance transformations.
Flexibility	Supports integration with other languages (e.g., Python).	Limited to T-SQL; less flexible for multi-language workflows.
Maintainability	Can become fragmented in production setups.	Modular and reusable; easier to manage in production pipelines.
Security	Relies on workspace-level security; less granular control.	Strong RBAC and object-level security controls available.

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Re: Pros and cons of using SQL in notebook to write data to warehouse vs. stored procedures

ArwaAldoud — Tue, 11 Feb 2025 15:26:13 GMT

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.