Connect Redshift using Python Pyodbc – ODBC Driver Example

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  • Post last modified:April 19, 2023
  • Post category:Redshift
  • Reading time:9 mins read

There are many ways to connect to the Redshift data warehouse appliance from Python or any other programming language. Most of the applications use either odbc or jdbc driver to connect to Amazon Redshift. In my other earlier post, we have discussed how to connect Redshift data warehouse from Python using jdbc driver. In this article, we will check method on connect Redshift using Python pyodbc and odbc driver with a working example.

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Introduction

Amazon Redshift is a powerful cloud data warehousing solution that enables users to store, manage, and analyze large amounts of data. Python is a popular programming language that is widely used for data analysis and management. By connecting Redshift to Python using ODBC driver and the pyodbc library, users can leverage the strengths of both tools to perform sophisticated data analysis, visualization, and reporting.

Setup Redshift ODBC Driver

The Redshift data warehouse comes with odbc support. Before trying to connect to Redshift from either Windows or Linux, you must install respective odbc driver. The pyodbc python module will not work without a proper odbc driver installed.

Configure Redshift ODBC Driver on Linux

Similar to many other MPP data warehouse appliances like Netezza, Greenplum, Redshift also provide support to odbc driver on Linux operating system. You can download and configure it.

Follow below link to download and configure Redshift ODBC drivers for Linux:

Configure Redshift ODBC on Windows

As mentioned earlier, Redshift does provide an odbc driver for Windows machines as well. Based on your system configuration, you can download and configure the Redshift odbc drivers.

Follow below link for more information:

Note that, you will not be able to use pyodbc module without installing and configuring OBDC drivers on required system. This step is one of the pre-requisites to use pyodbc module.

Redshift ODBC Connection String

The Redshift odbc connection string is similar to other relational database connection string.

Below is the sample Redshift ODBC connection string:

Driver={Amazon Redshift (x64)}; Server=redshift-host; Database=database; UID=user; PWD=master_user_password_here; Port=5439

You will get your Redshift cluster odbc connection string in your cluster configuration tab.

For example, below is the sample odbc connection string:

Driver={Amazon Redshift (x64)}; Server=example-cluster.qwertyuiop1.us-west-2.redshift.amazonaws.com; Database=dev; UID=redshiftuser; PWD=Password@123; Port=5439

Connect Redshift using ODBC Driver and Python pyodbc Module

If you have configured Redshift ODBC drivers properly, then you are good go ahead and test it using the Python pyodbc module.

Note that, you may install pyodbc module if you haven’t done it already. 

 pip install pyodbc

The next step would be to write a small Python program that uses pyodbc module to connect to Redshift database.

Here is the example:

import pyodbc

# pyodbc connection string
conn = pyodbc.connect("Driver={Amazon Redshift (x64)}; Server=example-cluster.qwertyuiop1.us-west-2.redshift.amazonaws.com; Database=dev; UID=redshiftuser; PWD=Password@123; Port=5439")

# Define Cursor
cus=conn.cursor()

# Execute SQL statement to get current datetime and store result in cursor
cus.execute("select now();")

# Display the content of cursor
row = cus.fetchone()

print(row)

Output:

(datetime.datetime(2019, 8, 22, 15, 1, 19, 995164), )

Best Practices for Connecting Redshift using Python pyodbc

Here are some best practices for connecting Redshift using Python pyodbc:

  • Use connection pooling: Connection pooling allows multiple requests to share a single connection to Redshift, which can improve performance and reduce resource usage.
  • Use the latest ODBC driver: The ODBC driver is a crucial component for connecting to Redshift using pyodbc. It’s important to use the latest version of the driver to ensure compatibility and performance.
  • Optimize query performance: Redshift is optimized for large-scale data warehousing, but it’s important to optimize queries for performance.
  • Monitor pyodbc connection activity: Monitoring connection activity can help identify potential issues with connection pooling, query performance, and security.

Conclusion

In conclusion, connecting Redshift using ODBC driver and Python pyodbc can be an effective way to perform data analysis and management. However, it’s important to follow best practices to ensure optimal performance, security, and compatibility. By using connection pooling, query optimization, the latest ODBC driver and connection monitoring you can avoid common errors and issues.

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Hope this helps 🙂

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