databricks-synthetic-data-gen

You generate scalable synthetic data in Databricks by executing a Python script that leverages Spark, Faker, and Pandas UDFs for distributed generation. This approach produces test datasets without exposing real data.

Yes, synthetic data generation with Faker integrates with Unity Catalog. The generated datasets are written directly to configured Unity Catalog volumes, ensuring secure storage and referential integrity for your testing workflows.

Yes, you can use serverless compute to generate synthetic data in Spark. The workload supports serverless compute environments and outputs multiple formats including Parquet, JSON, CSV, and Delta.

To create synthetic datasets for Databricks, you need a Spark environment along with the Faker, pandas, and numpy libraries. These dependencies enable Pandas UDFs to generate distributed data efficiently.

The best way to maintain referential integrity when generating test data is to use end-to-end workflows that integrate with Unity Catalog. This ensures relationships between synthetic tables remain consistent for analytics scenarios.

Supported output formats when writing synthetic data to Databricks volumes include Parquet, JSON, CSV, and Delta. This allows flexible integration with downstream analytics pipelines and demo environments.