databricks-synthetic-data-gen

Generate realistic synthetic data at scale in Databricks by using Spark, Faker, and Pandas UDFs to create millions of rows. This approach handles complex patterns like referential integrity and non-linear distributions for testing and demos.

Yes, you can write synthetic data directly to Unity Catalog Volumes. The generated datasets can be saved as Delta tables, Parquet, JSON, or CSV formats within your specified catalog and schema.

Creating test datasets with realistic purchase patterns is best handled by generating synthetic data with Spark and Faker. This supports customer segmentation, referential integrity, and time-based trends for e-commerce, IoT, or CRM domains.

Synthetic data generation with Faker supports non-linear distributions and referential integrity. It leverages Pandas UDFs to handle complex data patterns, ensuring generated records maintain realistic relationships and time-based trends across tables.

Yes, you need databricks-connect along with faker, numpy, pandas, and holidays dependencies to generate sample data. These tools enable scalable serverless execution and complex pattern generation within your Databricks environment.

While synthetic data generation in Spark creates realistic patterns and distributions, it remains simulated data. It cannot replicate exact production anomalies or sensitive real-world behaviors, making it ideal for demos and testing but not for exact production replication.