synthetic-data-generation

Name: synthetic-data-generation
Availability: InStock
Author: laurentprat-db

Generate realistic synthetic data with Faker and Spark for Databricks Volumes.

Updated Feb 27, 2026

Search Tags:#data pipeline #data generation #synthetic data #faker #spark #parquet #databricks

Ready to run

npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill synthetic-data-generation-laurentprat-db

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: synthetic-data-generation
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/synthetic-data-generation
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill synthetic-data-generation-laurentprat-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faker, holidays, pyspark, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation of realistic, story-driven synthetic data for Databricks, essential for testing, demos, and populating data pipelines.

Core Features & Use Cases

Realistic Data Generation: Uses Faker and Spark to create data with non-linear distributions, integrity constraints, and temporal patterns.
Databricks Integration: Saves generated data as parquet files into Databricks Volumes.
Use Case: Generate 10,000 customer records, 50,000 orders, and 20,000 support tickets with realistic attributes and temporal distributions for a new product demo.

Quick Start

Use the synthetic-data-generation skill to create customer, order, and ticket data and save it to the my_catalog.my_schema.raw_data volume.

Frequently Asked Questions about synthetic-data-generation

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

FAQPage Schema

How do I generate realistic synthetic data for Databricks data pipelines?▼

Generate realistic synthetic data for Databricks by using Python with Faker and Spark to create datasets featuring non-linear distributions and enforced integrity constraints. The data is saved as parquet files directly into Databricks Volumes.

Can I use Faker with Spark to create test data with non-linear distributions?▼

Yes, Faker integrates with PySpark to generate test data featuring non-linear distributions, temporal patterns, and integrity constraints. This combination allows you to create realistic story-driven datasets for testing and demos.

What's the best way to create demo datasets for Databricks with enforced integrity constraints?▼

Use a Python and Spark-based synthetic data generation approach that enforces integrity constraints and temporal patterns. It automates creating related records, like customers and orders, saving them as parquet files in Databricks Volumes.

Do I need PySpark and Faker to generate synthetic parquet files in Databricks Volumes?▼

Yes, PySpark and Faker are required dependencies for generating synthetic parquet files in Databricks Volumes. The skill uses them to produce realistic attributes and handle the data writing process.

Does synthetic data generation work with Databricks Volumes and parquet format?▼

Yes, synthetic data generation saves generated records as parquet files directly into Databricks Volumes. You can target specific catalogs and schemas, such as my_catalog.my_schema.raw_data, for your output data.

When should I use synthetic data generation for data pipelines instead of production data?▼

Use synthetic data generation when building data pipelines, creating test data, or preparing demo datasets. It provides realistic story-driven records with temporal distributions without exposing sensitive production data.