synthetic-data-generation

You can generate realistic synthetic data for Databricks using Python, Faker, and Spark to create datasets with non-linear distributions, referential integrity, and time-based patterns. The generated data is saved as Parquet files directly into Unity Catalog Volumes.

Yes, you can use Faker with Spark to create synthetic datasets that maintain referential integrity across related tables, such as linking customer records to their associated orders and support tickets for believable distributions.

The best way to create test data for Spark Declarative Pipelines is to generate story-driven synthetic datasets and save them as Parquet files into Unity Catalog Volumes, which serve as the direct input source for your downstream pipelines.

Yes, synthetic data generation works with Unity Catalog Volumes by saving the raw generated data as Parquet files directly into the catalog schema, making it immediately accessible for analytics and AI development.

Using synthetic data eliminates the need for sensitive production data during testing and development. It automates the creation of story-driven datasets, ensuring data privacy while providing realistic distributions for feature demos.

Yes, you need Python and Spark alongside the Faker and holidays libraries to generate synthetic data with non-linear distributions and time-based patterns for your data pipelines.