In this tutorial, we’ll walk you through how to create a privacy-preserving synthetic dataset directly within a notebook in Snowflake using Python and the Synthetic Data SDK. You’ll learn how to install and configure the Synthetic Data SDK locally, set up a generator for synthetic data, and customize privacy settings for your dataset.
We’ll demonstrate how to generate synthetic data from a real-world dataset, maintaining the original statistical distribution while ensuring privacy. By the end of this video, you’ll know how to generate and share privacy-preserving synthetic data with your team, all within minutes!
Key Steps Covered:
- Installing the Synthetic Data SDK locally in your environment
- Importing and using pandas to load datasets
- Setting up and configuring the synthetic data generator
- Enabling differential privacy and accessing model metrics
- Real-time sampling of synthetic data
- Sharing and collaborating with colleagues on privacy-preserving data
- Key troubleshooting tips for Snowflake notebook environments
We also explain the necessary network rules for enabling external access to install required libraries and datasets within Snowflake.
For more tutorials and examples, visit our GitHub, where you can explore open-source resources for creating synthetic data across different environments.