To interact with MS SQL Server, you'll need to install the `pyodbc` package. Open your terminal or command prompt and run:
```bash
pip install pyodbc
```

Ensure that you have the Microsoft ODBC Driver for SQL Server installed on your system. You can download it from the Microsoft website. This driver is needed for `pyodbc` to establish a connection to SQL Server.

Use `pyodbc` to connect to your SQL Server database. You'll need to know your server name, database name, and authentication details. Here’s a sample connection string:
```python
import pyodbc

conn = pyodbc.connect(
'DRIVER={ODBC Driver 17 for SQL Server};'
'SERVER=your_server_name;'
'DATABASE=your_database_name;'
'UID=your_username;'
'PWD=your_password'
)
cursor = conn.cursor()
```

Format your iterable data into a suitable structure for SQL insertion. For instance, if you have a list of dictionaries, ensure each dictionary represents a row, with keys as column names:
```python
data = [
{'column1': 'value1', 'column2': 'value2'},
{'column1': 'value3', 'column2': 'value4'}
]
```

Create SQL queries to insert data into your database. You can use Python’s string formatting to dynamically insert values. Ensure you handle data types and SQL injection risks appropriately:
```python
for row in data:
query = f"INSERT INTO your_table_name (column1, column2) VALUES (?, ?)"
values = (row['column1'], row['column2'])
cursor.execute(query, values)
```

After constructing your queries, execute them using the cursor object and commit the transaction to save changes:
```python
conn.commit()
```

Finally, ensure you close the database connection to free up resources:
```python
cursor.close()
conn.close()
```

By following these steps, you can efficiently move data from an iterable structure directly into an MS SQL Server database without relying on third-party integrations. Adjust the data formatting and SQL queries based on your specific dataset and database schema.