databricks-synthetic-data-generation

To generate synthetic data for Databricks, you can use this Skill to create configurable datasets with varying sizes and distributions using Faker-based generation, then persist them as Parquet files in a catalog/schema volume for QA testing.

Yes, you can create reproducible synthetic datasets in Spark by configuring deterministic seeds and specific date ranges, which ensures stable and repeatable outputs for demos and QA pipelines.

Yes, this approach supports generating raw, non-aggregated data for SDP pipelines by using Faker-based logic to create customer, order, and ticket records, storing them directly as Parquet volumes in your Databricks catalog.

The best way to create configurable Parquet volumes for Databricks QA testing is using a local Python script with Faker-based data generation, executed on Databricks to persist records with varying sizes and distributions into a configurable schema.

Yes, you need to configure a specific catalog and schema because this Skill uses Spark-based persistence to write the generated synthetic data directly into Parquet volumes within your defined Databricks catalog and schema structure.