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synthetic-data-generation

Generate realistic synthetic datasets for Databricks with Faker, numpy, and Spark.

27|9|Updated Feb 3, 2026
Search Tags:#data generation#synthetic data#reproducibility#faker#parquet#pyspark#databricks
One-click install
npx skills add https://github.com/datasciencemonkey/claude-code-cli-bricks --skill synthetic-data-generation-datasciencemonkey
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: synthetic-data-generation
Source: https://github.com/datasciencemonkey/claude-code-cli-bricks/tree/main/.claude/skills/synthetic-data-generation
Command: npx skills add https://github.com/datasciencemonkey/claude-code-cli-bricks --skill synthetic-data-generation-datasciencemonkey

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables the rapid creation of realistic synthetic datasets for Databricks environments, providing ready-to-use data for testing, demos, and development pipelines without exposing real customer information.

Core Features & Use Cases

  • End-to-end data generation: Create master tables (customers), related tables (orders, tickets) with referential integrity.
  • Non-linear distributions and realism: Uses Faker, numpy, and holidays to simulate real-world patterns, seasonality, and holidays.
  • Pipeline-ready storage: Writes data to Parquet in a Databricks volume for downstream Spark SDP processing.
  • Reproducibility: Seeds and deterministic generation enable repeatable experiments and tests.
  • Use Case: Build synthetic datasets to validate dashboards, ML models, and data governance workflows without risking sensitive data.

Quick Start

Save the script as scripts/generate_data.py, run it on your Databricks cluster using the provided MCP tool, and then validate volumes with get_volume_folder_details.

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 in Databricks for testing?▼

This Skill generates realistic synthetic data for Databricks by using Faker, numpy, and pandas to simulate non-linear distributions and referential integrity, writing the output to Parquet volumes for Spark pipelines.

Can I create synthetic datasets with referential integrity across multiple tables?▼

Yes, this synthetic data generation Skill creates related tables like customers, orders, and tickets while maintaining referential integrity across multiple domains for accurate downstream Spark SDP processing.

Does synthetic data generation support reproducibility for repeatable experiments?▼

Yes, synthetic data generation supports reproducibility by using deterministic seeds, ensuring repeatable experiments and tests by generating identical datasets across multiple Databricks runs.

What Python libraries do I need to install to generate synthetic data on Databricks?▼

You need to install the faker and holidays libraries on your Databricks cluster to generate synthetic data with realistic patterns, seasonality, and non-linear distributions using numpy and pandas.

How do I save generated synthetic data to Parquet volumes for Spark pipelines?▼

You save generated synthetic data to Parquet volumes by running the provided generation script on your Databricks cluster, enabling pipeline-ready storage for downstream Spark SDP processing.

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