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databricks-synthetic-data-gen

Skill

Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use whe

0Machine Learning

Install

git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-synthetic-data-gen

What is databricks-synthetic-data-gen?

Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~102 tokens of context used while enabled, before you invoke anything

Documentation

README · ~8 min read

Catalog and schema are always user-supplied — never default to any value. If the user hasn't provided them, ask. For any UC write, always create the schema if it doesn't exist before writing data.

Databricks Synthetic Data Generation

Generate realistic, story-driven synthetic data for Databricks using Spark + Faker + Pandas UDFs (strongly recommended).

Data Must Tell a Business Story

Synthetic data should demonstrate how Databricks helps solve real business problems.

The pattern: Something goes wrong → business impact ($) → analyze root cause → identify affected customers → fix and prevent.

Key principles:

  • Problem → Impact → Analysis → Solution — Include an incident, anomaly, or issue that causes measurable business impact. The data lets you find the root cause and act on it.
  • Industry-relevant but simple — Use domain terms (e.g., "SLA breach", "churn", "stockout") but keep the schema easy to understand. A few tables, clear relationships.
  • Business metrics with $ impact — Revenue, MRR, cost, conversion rate. Every story needs a dollar sign to show why it matters.

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