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-- Stop the Extended Events session if it is running
IF EXISTS (
    SELECT 1
    FROM sys.server_event_sessions
    WHERE name = 'MonitorTableQueries'
)
BEGIN
    ALTER EVENT SESSION [MonitorTableQueries] ON SERVER STATE = STOP;
    PRINT 'MonitorTableQueries session stopped.';

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-- Create Extended Events session
CREATE EVENT SESSION [MonitorTableQueries]
ON SERVER
ADD EVENT sqlserver.sql_statement_completed
(
    ACTION (
        sqlserver.sql_text,
        sqlserver.database_name,
        sqlserver.username

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IF  EXISTS (SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'[dbo].[help_change_feed]') AND type in (N'U')) DROP TABLE [dbo].[help_change_feed]

create table  help_change_feed 
(
	table_group_id uniqueidentifier ,
	table_group_name nvarchar(140) ,
	destination_location nvarchar(512) ,
	destination_credential nvarchar(247) ,
	destination_type nvarchar(247) ,
	workspace_id nvarchar(247) ,

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%%pyspark

import sempy.fabric as fabric
import struct
import sqlalchemy
import pyodbc
import pandas as pd
from notebookutils import mssparkutils

#Function to Return sqlalchemt ODBC Engine, given a connection string and using Integrated AAD Auth to Fabric

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@json('
"translator": {
	"type": "TabularTranslator",
	"mappings": [
		{
			"source": {
				"name": "t",
				"type": "DateTime",
				"physicalType": "datetime"
			},

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import requests
from notebookutils import mssparkutils
import sempy.fabric as fabric 
from sempy.fabric.exceptions import FabricHTTPException, WorkspaceNotFoundException 
import json
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType
from datetime import datetime

# Get token

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import pandas as pd
import struct
import sqlalchemy
import pyodbc
import notebookutils
import sempy.fabric as fabric
from sqlalchemy import text
import time

# -----------------------------------------------------------------------------

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"""
View Migration Script: SQL Server ➜ Fabric Warehouse/Lakehouse
--------------------------------------------------------------
This script copies one or more view definitions from a source SQL Server database 
into a Fabric Warehouse or Lakehouse SQL endpoint.

Key Features:
- Connects to SQL Server using SQLAlchemy/pyodbc.
- Connects to Fabric using MSI authentication (access token).
- Reads view definitions from sys.views/sys.sql_modules in the source.

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/*
This is very rough, it doesn't work for ints/bits/etc only varchars

This does not work if there are schema bound views

*/
select 'alter table ' + s.name + '.' + object_name(c.object_id) + ' ALTER COLUMN ' + c.name + ' ' + ttt.name + '(' + convert(varchar,c.max_length) + ');',
c.max_length , c.*, tt.*, s.* from sys.columns  c
inner join sys.tables tt on tt.object_id = c.object_id 
inner join sys.schemas s on s.schema_id = tt.schema_id

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