synthetic-data-generation

To generate realistic synthetic data for Databricks, you can use this Skill to produce coherent, non-sensitive datasets with Faker and PySpark, writing the output as Parquet files to a Databricks volume.

The best way to create non-sensitive test datasets with temporal patterns in Spark is using Faker to simulate non-linear distributions and holiday effects, then saving the output as Parquet for downstream pipelines.

Yes, you need to install the Faker and holidays Python libraries to generate synthetic data in PySpark. These dependencies provide realistic temporal patterns and varied data domains for your datasets.

Yes, you can generate related master and transaction tables like customers and orders for demos. This Skill creates coherent datasets across multiple data domains, including varied sizes and date ranges.

Saving synthetic data as Parquet to a Databricks volume supports downstream SDP pipelines by providing structured bronze, silver, and gold data layers for safe testing, training, and data validation.

A limitation of using Faker for synthetic data generation in Spark is that while it produces realistic distributions and temporal effects, the data is entirely artificial and requires PySpark handling for large-scale processing.