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Query SQL Server DB from Spark Notebook in Azure Synapse Analytics

Updated on Jan 09,2024

Query SQL Server DB from Spark Notebook in Azure Synapse Analytics

Table of Contents

  1. Introduction
  2. Connecting to SQL Server Database
    1. Installing the Pi ODBC Library
    2. Importing the Pi ODBC Library
    3. Finding the Driver Name
    4. Creating the Connection STRING
  3. Executing Queries
  4. Retrieving Data
  5. Performing Inserts
  6. Committing Changes
  7. Closing the Cursor and Connection
  8. Conclusion
  9. FAQ
  10. Additional Resources

Introduction

Welcome to this tutorial on how to query SQL Server database tables from the Spark notebook in Azure SYNAPSE Analytics. In this tutorial, we will discuss the steps to connect to a SQL Server database, execute queries, retrieve data, perform inserts, commit changes, and close the cursor and connection. We will be using the Pi ODBC library in Python to Interact with the SQL database.

Connecting to SQL Server Database

To connect to the SQL Server database from the Spark notebook, we need to install and import the Pi ODBC library. The library provides the necessary tools to establish a connection between the notebook and the database. We also need to find the driver name and Create a connection string for the database.

Installing the Pi ODBC Library

Before we can use the Pi ODBC library, we need to ensure that it is installed. Luckily, the Spark pool already includes the library by default, so we don't need to worry about installation.

Importing the Pi ODBC Library

Once the library is installed, we can import it into our notebook by using the import pyodbc command. This allows us to access the library's functions and classes.

Finding the Driver Name

To establish a connection with the SQL Server database, we need to know the driver name. We can find the driver name by using the pyodbc.drivers() function, which returns a list of available drivers. We select the appropriate driver and make note of its name.

Creating the Connection String

The connection string is a key component in establishing a connection with the SQL Server database. It contains information such as the server name, database name, username, and password. We create variables to store these values and construct the connection string using the pyodbc.connect() function.

Executing Queries

Once the connection is established, we can use the cursor object to execute queries. We pass the SQL query as a string to the cursor.execute() function. This allows us to execute select queries, insert queries, or stored procedures on the SQL Server database.

Retrieving Data

After executing a select query, we can retrieve the data from the cursor object. We can loop through the cursor using a for loop and print each row of data. This allows us to see the results of our query.

Performing Inserts

In addition to executing select queries, we can also perform inserts on the SQL Server database. We use the cursor.execute() function to execute the insert query, providing the necessary values for the insert operation. After executing the insert query, we need to commit the changes to ensure they are applied to the database.

Committing Changes

To commit the changes made to the SQL Server database, we use the cursor.commit() function. This ensures that any modifications, such as inserts, updates, or deletes, are permanently saved.

Closing the Cursor and Connection

Once We Are done with our queries and modifications, it is important to close the cursor and connection to free up system resources. We use the cursor.close() function to close the cursor object and the connection.close() function to close the connection object.

Conclusion

In this tutorial, we discussed how to connect to an SQL Server database from the Spark notebook in Azure Synapse Analytics. We covered the steps to install and import the Pi ODBC library, find the driver name, create the connection string, execute queries, retrieve data, perform inserts, commit changes, and close the cursor and connection. By following these steps, You can effectively interact with SQL databases using the Pi ODBC library in Python.

FAQ

Q: Can I connect to any SQL Server database using this method? A: Yes, you can connect to any SQL Server database, including Azure SQL databases, using the Pi ODBC library in Azure Synapse Analytics.

Q: Do I need to install any additional drivers? A: In most cases, the necessary ODBC drivers are already installed by default in the Azure Synapse Analytics Spark pool. However, it is important to ensure that the appropriate driver is available for the specific SQL Server database you want to connect to.

Q: Can I execute stored procedures using this method? A: Yes, you can execute stored procedures by passing the stored procedure call as a string to the cursor.execute() function.

Q: What happens if I forget to commit my changes? A: If you forget to commit your changes, they will not be permanently saved to the database. It is important to always commit changes to ensure data integrity.

Q: Can I use this method in other programming languages besides Python? A: While this tutorial focuses on using the Pi ODBC library in Python, you can also use similar ODBC libraries in other programming languages to interact with SQL Server databases. The steps may vary slightly depending on the programming language used.

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