databricks-synthetic-data-gen

Generate synthetic data stories in Databricks using Spark and Faker.

1|Updated Dec 15, 2025

Search Tags:#serverless #faker #spark #data-generation #databricks #referential-integrity #synthetic-data

Ready to run

npx skills add https://github.com/lucaslessachaves/default --skill databricks-synthetic-data-gen-lucaslessachaves

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-synthetic-data-gen
Source: https://github.com/lucaslessachaves/default/tree/main/.claude/skills/databricks-synthetic-data-gen
Command: npx skills add https://github.com/lucaslessachaves/default --skill databricks-synthetic-data-gen-lucaslessachaves

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Databricks synthetic data generation helps data teams quickly create realistic datasets for testing, demos, and model development without exposing real customer data.

Core Features & Use Cases

Spark + Faker-based generation for scalable data from thousands to millions of rows.
Supports multiple formats (Delta, Parquet, JSON) and serverless execution for fast prototyping.
Builds data narratives with incidents, business impact, and root-cause analysis to showcase Databricks value.

Quick Start

Describe your data story, then run synthetic data generation in Databricks with Spark and Faker to produce a dataset and master-detail tables.

Frequently Asked Questions about databricks-synthetic-data-gen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate synthetic data in Databricks for testing and demos?▼

Synthetic data generation in Databricks uses Spark and Faker to create scalable datasets. It enforces Pandas UDF patterns and a frontmatter-driven schema creation policy to ensure safe, repeatable results with non-uniform distributions across thousands to millions of rows.

Can I build multi-table relationships with referential integrity using Faker in Spark?▼

Yes, Spark with Faker supports multi-table relationships and referential integrity. You can build master-detail tables with non-uniform distributions to create realistic data narratives that demonstrate business incidents and enable root-cause analysis.

Does synthetic data generation work with Databricks serverless compute?▼

Synthetic data generation in Databricks supports serverless execution for fast prototyping. It leverages Spark and Faker to produce scalable datasets in Delta, Parquet, and JSON formats without exposing real customer data.

What is the best way to create a data story with a business incident for root-cause analysis?▼

The best way to create a data story for root-cause analysis is generating synthetic data in Databricks. This builds data narratives with specific incidents and measurable business impact, enabling teams to showcase Databricks value through realistic testing scenarios.

Do I need Pandas UDFs to generate non-uniform distributions in synthetic data?▼

Yes, you need Pandas UDFs to generate non-uniform distributions in synthetic data. The skill enforces Pandas UDF patterns alongside Spark and Faker to ensure safe, repeatable results with realistic data distributions.