databricks-synthetic-data-gen

Generate realistic synthetic data in Databricks using serverless Spark and Faker.

Updated Jul 4, 2026

Search Tags:#data generation #Spark #synthetic data #Faker #Databricks #serverless computing

Ready to run

npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-synthetic-data-gen-mkgs-databricks-demos

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires databricks-connect, faker, numpy, pandas, holidays, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill addresses the challenge of creating realistic synthetic data for Databricks environments, simulating real-world data scenarios and business contexts.

Core Features & Use Cases

Synthetic Data Generation: Supports the creation of large volumes of synthetic data for different business use cases and data structures.
Serverless Spark & Faker Integration: Uses serverless Spark sessions and Faker for fast, parallel data generation.
Data Patterns and Assumptions: Ensures data follows realistic business patterns, including time-based and categorical distributions.
Use Case: This skill is ideal for data science teams building and testing models with synthetic datasets that resemble real customer data, without the risk of data exposure.

Quick Start

To generate synthetic data, run the following command: databricks run-python /path/to/your/script.py --name generate_synthetic_data

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 for testing in a Databricks environment?▼

You can generate synthetic data for Databricks by running a Python script that leverages serverless Spark sessions and Faker to create realistic datasets mimicking actual business scenarios.

What is the best way to create large volumes of realistic test data for Apache Spark?▼

The best way to create large test datasets for Spark is using parallel data generation with serverless Spark sessions integrated with Faker, ensuring data follows realistic time-based and categorical distributions.

Do I need databricks-connect to use Faker for synthetic data generation?▼

Yes, databricks-connect is required as a dependency, alongside faker, numpy, pandas, and holidays, to set up your environment and accurately generate synthetic data with realistic patterns.

Can I use custom schema and catalog configurations when generating synthetic data?▼

Yes, synthetic data generation supports custom catalog and schema configurations to ensure the generated datasets accurately map to your specific real-world business data structures.

Why use synthetic data instead of real customer data for data science models?▼

Synthetic data allows data science teams to build and test models using datasets that resemble real customer data without the risk of actual data exposure or privacy violations.

How does serverless computing improve synthetic data generation in Spark?▼

Serverless computing improves synthetic data generation by providing fast, parallel processing through serverless Spark sessions, allowing you to quickly generate large volumes of data for development.