The blog post introduces two new notebooks from Gretel—multi-table transform and multi-table synthetics—that offer streamlined processes for anonymizing data in relational databases while preserving the referential integrity of primary and foreign keys. These notebooks can be used independently or in tandem, depending on the level of privacy required. The transform notebook focuses on de-identifying sensitive information, making it suitable for pre-processing or demo environments, while the synthetics notebook augments data and is ideal for statistical or machine learning analyses. Both notebooks operate on a mock e-commerce database and offer configurations for transforming and synthesizing data, ensuring the relationships between tables are maintained. The process includes training models, generating data, and ultimately loading the final anonymized datasets back into the database, with the synthetics notebook providing additional performance reports. This approach allows users to anonymize and augment data effectively, offering robust privacy protections while ensuring the statistical integrity of the dataset.