Google Cloud has published a practical guide on generating synthetic data with Gretel and BigQuery DataFrames. This guide provides a deep dive into the technical aspects of synthetic data generation, focusing on ensuring high data quality, privacy protection, and compliance with privacy regulations. It starts by working with a BigQuery patient records table, de-identifying the data in Part 1, and then generating synthetic data to save back to BigQuery in Part 2. The guide also covers important aspects such as installing and configuring the Gretel and BigQuery DataFrames tools, as well as using Gretel Transform v2 to de-identify personally identifiable information (PII). Furthermore, it demonstrates how to use Gretel's Navigator Fine Tuning (NavFT) to generate high-quality, domain-specific synthetic data by fine-tuning pre-trained models on datasets. The guide also includes code examples and tips on using BigQuery with Gretel. By following this guide, users can unlock the power of synthetic data to enhance their data science, analytics, and AI development workflows while ensuring data privacy and compliance.