Quickstart: Connect to a SQL database in Fabric with the Microsoft Python Driver for SQL Server

In this quickstart, you use Streamlit to quickly create a report, allowing you to quickly gather user feedback to ensure you're on the right track. You use the mssql-python driver for Python to connect to your SQL database in Fabric and read the data loaded into your report.

The mssql-python driver doesn't require any external dependencies on Windows machines. The driver installs everything that it needs with a single pip install, allowing you to use the latest version of the driver for new scripts without breaking other scripts that you don't have time to upgrade and test.

mssql-python documentation | mssql-python source code | Package (PyPi) | UV

Prerequisites

• Load AdventureWorks sample data in your SQL database.

• Python 3

  • If you don't already have Python, install the Python runtime and Python Package Index (PyPI) package manager from python.org.

  • Prefer to not use your own environment? Open as a devcontainer using GitHub Codespaces.

    

• If you don't already have uv, install uv by following the instructions from https://docs.astral.sh/uv/getting-started/installation/.

• Install one-time operating system specific prerequisites.

  • Alpine
  • Debian/Ubuntu
  • RHEL
  • SUSE
  • openSUSE
  • macOS

  apk add libtool krb5-libs krb5-dev
  

Create the project and run the code

• Create a new project
• Add dependencies
• Update pyproject.toml
• Update main.py
• Save the connection string
• Use uv run to execute the script

Create a new project

1. Open a command prompt in your development directory. If you don't have one, create a new directory called python, scripts, etc. Avoid folders on your OneDrive, the synchronization can interfere with managing your virtual environment.

2. Create a new project with uv.

   uv init rapid-prototyping-qs
   cd rapid-prototyping-qs
   

Add dependencies

In the same directory, install the mssql-python, streamlit, and python-dotenv packages.

uv add mssql-python python-dotenv streamlit

Update pyproject.toml

1. The pyproject.toml contains the metadata for your project. Open the file in your favorite editor.

2. Update the description to be more descriptive.

   description = "A quick example of rapid prototyping using the mssql-python driver and Streamlit."
   

3. Save and close the file.

Update main.py

1. Open the file named main.py. It should be similar to this example.

   def main():
    print("Hello from rapid-protyping-qs!")
   
    if __name__ == "__main__":
      main()
   

2. At the top of the file, add the following imports above the line with def main().

   Tip

   If Visual Studio Code is having trouble resolving packages, you need to update the interpreter to use the virtual environment.

   from os import getenv
   from dotenv import load_dotenv
   from mssql_python import connect, Connection
   import pandas as pd
   import streamlit as st
   

3. Between the imports and the line with def main(), add the following code.

   def page_load() -> None:
      st.set_page_config(
          page_title="View Data",
          page_icon=":bar_chart:",
          layout="wide",
          initial_sidebar_state="expanded"
      )
   
      st.title("AdventureWorksLT Customer Order History")
   
      SQL_QUERY = """SELECT c.* FROM [SalesLT].[Customer] c inner join SalesLT.SalesOrderHeader soh on c.CustomerId = soh.CustomerId;"""
   
      df = load_data(SQL_QUERY)
   
      event = st.dataframe(
          df,
          width='stretch',
          hide_index=True,
          on_select="rerun",
          selection_mode="single-row"
      )
   
      customer = event.selection.rows
   
      if len(customer) == 0:
          SQL_QUERY = """select soh.OrderDate, SUM(sod.OrderQty), SUM(sod.OrderQty * sod.UnitPrice) as spend,  pc.Name as ProductCategory from SalesLT.SalesOrderDetail sod inner join SalesLt.SalesOrderHeader soh on sod.    salesorderid = soh.salesorderid inner join SalesLt.Product p on sod.productid = p.productid inner join SalesLT.ProductCategory pc on p.ProductCategoryID = pc.ProductCategoryID GROUP BY soh.OrderDate, pc.Name ORDER     BY soh.OrderDate, pc.Name;"""
      else:
          SQL_QUERY = f"""select soh.OrderDate, SUM(sod.OrderQty), SUM(sod.OrderQty * sod.UnitPrice) as spend,  pc.Name as ProductCategory from SalesLT.SalesOrderDetail sod inner join SalesLt.SalesOrderHeader soh on sod.    salesorderid = soh.salesorderid inner join SalesLt.Product p on sod.productid = p.productid inner join SalesLT.ProductCategory pc on p.ProductCategoryID = pc.ProductCategoryID where soh.CustomerID = {df.loc    [customer, 'CustomerID'].values[0]} GROUP BY soh.OrderDate, pc.Name ORDER BY soh.OrderDate, pc.Name;"""
   
      st.write("Here's a summary of spend by product category over time:")
      st.bar_chart(load_data(SQL_QUERY).set_index('ProductCategory')
                   ['spend'], use_container_width=True)
   
      if len(customer) > 0:
          st.write(
              f"Displaying orders for Customer ID: {df.loc[customer, 'CustomerID'].values[0]}")
          SQL_QUERY = f"""SELECT * FROM [SalesLT].[SalesOrderHeader] soh  WHERE soh.CustomerID = {df.loc[customer, 'CustomerID'].values[0]};"""
          st.dataframe(load_data(SQL_QUERY), hide_index=True, width='stretch')
          SQL_QUERY = f"""SELECT sod.* FROM [SalesLT].[SalesOrderHeader] soh INNER JOIN SalesLT.SalesOrderDetail sod on soh.SalesOrderId = sod.SalesOrderId WHERE CustomerID = {df.loc[customer, 'CustomerID'].values[0]};"""
          st.dataframe(load_data(SQL_QUERY), hide_index=True, width='stretch')
   

4. Between the imports and def page_load() -> None:, add this code.

   _connection = None
   
   def get_connection() -> Connection:
       global _connection
       if not _connection:
           load_dotenv()
           _connection = connect(getenv("SQL_CONNECTION_STRING"))
       return _connection
   
   @st.cache_data
   def load_data(SQL_QUERY) -> pd.DataFrame:
       data = pd.read_sql_query(SQL_QUERY, get_connection())
       return data
   

5. Find this code.

   def main():
       print("Hello from rapid-protyping-qs!")
   

6. Replace it with this code.

   def main() -> None:
       page_load()
       if _connection:
           _connection.close()
   

7. Save and close main.py.

Save the connection string

1. Open the .gitignore file and add an exclusion for .env files. Your file should be similar to this example. Be sure to save and close it when you're done.

   # Python-generated files
   __pycache__/
   *.py[oc]
   build/
   dist/
   wheels/
   *.egg-info
   
   # Virtual environments
   .venv
   
   # Connection strings and secrets
   .env
   

2. In the current directory, create a new file named .env.

3. Within the .env file, add an entry for your connection string named SQL_CONNECTION_STRING. Replace the example here with your actual connection string value.

   SQL_CONNECTION_STRING="Server=<server_name>;Database={<database_name>};Encrypt=yes;TrustServerCertificate=no;Authentication=ActiveDirectoryInteractive"
   

   Tip

   For SQL database in Fabric, use the ODBC connection string from the connection strings tab without the DRIVER information.

Use uv run to execute the script

1. In the terminal window from before, or a new terminal window open to the same directory, run the following command.

    uv run streamlit run main.py
   

2. Your report opens in a new tab in your web browser.

3. Try your report to see how it works. If you change anything, save main.py and use the reload option in the upper right corner of the browser window.

4. To share your prototype, copy all files except for the .venv folder to the other machine. The .venv folder is recreated with the first run.

Next step

Visit the mssql-python driver GitHub repository for more examples, to contribute ideas or report issues.