spark-native-bronze

To generate synthetic data for a Bronze Delta table in Databricks, use a Spark-native distributed approach with `spark.range()` to create large datasets and write them directly to Delta Lake, including necessary metadata columns.

Creating large synthetic datasets in Spark is best achieved using distributed `spark.range()` operations rather than traditional Pandas or Faker libraries, ensuring scalable performance for 100K to 1M+ row generation tasks.

Yes, this Spark-native Bronze data generation method is optimized for speed and clarity, making it ideal for demonstrating canonical data engineering patterns and Bronze table creation during Databricks interview demos.

Use a Spark-native approach over Faker or Pandas to supersede inefficient single-node data generation methods, leveraging distributed Spark processing for scalable synthetic data production and direct Delta Lake writes.

Prepare a retail transaction dataset by generating synthetic events directly into a Bronze Delta table with metadata columns included, ensuring the data is canonical and ready for downstream Silver and Gold transformations.