databricks-synthetic-data-generation

You generate realistic synthetic data for Databricks using Faker and Spark. This creates relational tables with non-linear distributions and seasonal patterns, saving outputs directly to Databricks volumes for downstream pipelines.

Yes, you can preserve referential integrity across related tables like customers, orders, and tickets. The generation process maintains these relationships alongside optional business rules to ensure relational consistency.

You create test datasets with non-linear distributions and seasonal patterns by running a Python generation script on your Databricks cluster. This leverages Faker and Spark to inject realistic temporal and statistical variations into the data.

Yes, the synthetic data generator supports Spark Declarative Pipelines (SDP). It saves generated relational data into Databricks volumes specifically to power downstream SDP workflows and dashboards.

You need to install the Faker library to generate synthetic data in a Databricks volume. Configuration and guidance for library installation and execution are provided alongside the Spark generation script.

The best way to populate Databricks volumes with relational synthetic data is saving a generation script and running it on your cluster. This uses Faker and Spark to validate and save structured records into the target storage.