You have several options for executing a stored procedure in Synapse Analytics, depending on your needs and workflow:
The simplest and most direct way is to call the stored procedure using a T-SQL statement within another stored procedure, script, or directly in the SQL Server Management Studio query window.
```sql -- Call the stored procedure with input parameters EXEC sp_UpdateHistoricPrices 'my_delta_lake_table', 'my_historic_table'; ```If you're using Azure Data Factory for orchestrating your data pipelines, you can include the stored procedure call within a "Execute T-SQL Script" activity in your pipeline.
[Image of Azure Data Factory pipeline executing stored procedure](www.sqlshack.com)Consider using Azure Functions for serverless execution of your stored procedures. Trigger the function through various events (e.g., scheduled execution, blob storage changes) to automate the procedure execution based on your needs.
[Image of Azure Function triggering Synapse Analytics stored procedure](learn.microsoft.com)If you're working with .NET applications, you can use libraries like System.Data.SqlClient to connect to your Synapse Analytics database and execute the stored procedure programmatically.
This allows integration with your existing C# or VB.NET applications.
For Python-based scripts and applications, consider using libraries like pyodbc or pymssql to connect to Synapse Analytics and call the stored procedures.
This option offers integration with your Python data science or DevOps workflows.
Choosing the best option depends on several factors:
Ultimately, the flexibility of Synapse Analytics allows you to choose the most suitable approach for your specific scenario. Don't hesitate to ask if you need further guidance on selecting the best execution method for your stored procedure within your specific data workflow.