I need to read 12 millions records from a SQL table to store in another database after pivoting it. It works, but its taking lot time, any idea to improve the performance would be a great help.

Here is the table details:

The table structure of 12 million records table goes like this
mysourcetable (f_name varchar(100), l_name varchar(100), grade varchar(10), year varchar(10), age int, dob datetime())

Ex:
john abc, 2nd, 2020, 7,00-00-0000
rj bca, 5th, 2020, 10,00-00-0000

Here is the structure to target table – where the all the 12 million records will be saved
mytargettable(column_name varchar(100), column_value varchar(100), field_type varchar(100))

Ex:
f_name, john
l_name, abc
grade, 2nd
year, 2010
age, 7,
dob,00-00-000

f_name, rj
l_name, bca
grade, 5th
year, 2010
age, 10
dob,00-00-000

Here is the sample python code

for row in df.itertuples(index=True):
                      # Function to create an insert statement for each column in a row
                      #def create_insert_statements(row):
                      try:
                          counts += 1
                          # Iterate over each column in the row
                          for column, value in row._asdict().items():
                              # Create the insert statement
                              col_type = lookup_key(tbl_col_type, column)
 
                              statement = f"""INSERT INTO [mytargettable] ([Column_Name], [Column_Value], [Dictionary_Id]) 
                                  VALUES ('{column}', '{value}',{col_type}) rn """
                                     
                              cursor.execute(statement)
                              cursor.commit()
                         
                      except Exception as e:
                          print(f"Error inserting data ': {e}")

looking forward to get some feedback and solution