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Snowflake’s support for Python stored procedures allows data engineers and scientists to leverage Python’s vast ecosystem directly within Snowflake. This capability enables advanced analytics, custom data processing, and seamless integration of Python libraries. One particularly powerful feature is the ability to import and use Python files (.py) directly within a Snowflake stored procedure, which promotes code modularity, reusability, and better organization.
In this blog post, we’ll explore how to create and utilize a .Py file inside a Snowflake Python stored procedure. We’ll also walk through a practical use case involving customer churn prediction and engagement analysis.
Let’s imagine a scenario where you want to analyze customer data to predict churn and categorize engagement levels. This analysis will be performed within Snowflake, but to keep the logic organized and reusable, we’ll store the prediction and categorization logic in a separate .Py file.
Step 1: Writing the Python File (Cust_Predict.py)
Start by creating a .Py file (Cust_Predict.py) that contains the functions required for our analysis. Here’s an example:
This file defines three functions:
- predict_churn: Predicts whether a customer is likely to churn based on their logins, transactions, and satisfaction score.
- calculate_engagement_score: Calculates a customer’s engagement score.
- categorize_customer: Categorizes customers into ‘High’, ‘Medium’, or ‘Low’ engagement based on their score.
Step 2: Creating the Python Stored Procedure
Next, we’ll create a Snowflake Python stored procedure that imports the above Py file and uses its functions to analyze customer data.
Step 3: Visualizing the Results with Streamlit in Snowsight
Once the stored procedure is in place, you can use Streamlit within Snowsight to visualize the results. Here’s how:
Output:
