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databricks-synthetic-data-generation

Generate realistic synthetic data for Databricks using Python, Faker, and Spark.

Updated Mar 12, 2026
Search Tags:#data pipeline#data generation#synthetic data#faker#spark#databricks#volume
One-click install
npx skills add https://github.com/juanlamadrid20/coda --skill databricks-synthetic-data-generation-juanlamadrid20
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-synthetic-data-generation
Source: https://github.com/juanlamadrid20/coda/tree/main/.claude/skills/databricks-synthetic-data-generation
Command: npx skills add https://github.com/juanlamadrid20/coda --skill databricks-synthetic-data-generation-juanlamadrid20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faker, holidays, pyspark, and includes scripts (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 non-linear distributions, temporal patterns, and row coherence.
  • 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 sales demo.

Quick Start

Use the databricks-synthetic-data-generation skill to create 10,000 customer records, 50,000 orders, and 20,000 support tickets and save them to the 'my_catalog.my_schema.raw_data' volume.

Frequently Asked Questions about databricks-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 pipelines?▼

Generate realistic synthetic data in Databricks by using Python, Faker, and Spark to create records with non-linear distributions and temporal patterns, saving the output as parquet files in Databricks Volumes.

What is the best way to create demo datasets with referential integrity in Spark?▼

Creating demo datasets with referential integrity in Spark involves using Faker to generate coherent attributes across related tables, ensuring row consistency for customers, orders, and support tickets.

Can I save synthetic data generated with PySpark directly to Databricks Volumes?▼

Yes, you can save synthetic data generated with PySpark directly to Databricks Volumes. The skill outputs production-ready parquet files into specified catalogs and schemas for downstream analytics.

Does synthetic data generation with Faker support custom date ranges and event patterns?▼

Synthetic data generation with Faker supports custom date ranges and specific event patterns. It includes configuration options to model temporal patterns and control data volume for your target schema.

How do I populate Spark Declarative Pipelines with realistic test data?▼

Populate Spark Declarative Pipelines by configuring Python scripts to generate realistic test data with temporal distributions, saving the resulting parquet files into Databricks Volumes as pipeline input.

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