Build a synthetic data pipeline using Gretel and Apache Airflow

In this blog post, a synthetic data pipeline is built using Apache Airflow, Gretel's Synthetic Data APIs, and PostgreSQL. The purpose of the pipeline is to extract user activity features from a database, generate a synthetic version of the dataset, and save it to S3 for use by data scientists without compromising customer privacy. The pipeline consists of three stages: Extract, Synthesize, and Load. Gretel's Python SDKs are used to integrate with Airflow tasks, and an example booking pipeline is provided along with instructions on how to run it end-to-end.