faker-data-generation

What problem does it solve?

This Skill automates the creation of synthetic datasets for testing data pipelines, enabling robust data quality validation and simulation of production-like scenarios.

Core Features & Use Cases

• Realistic Data Generation: Creates data with non-linear distributions, temporal patterns, and row coherence.
• Configurable Corruption: Intentionally introduces data quality issues (nulls, invalid formats, out-of-range values) to test DLT expectations.
• Use Case: Generate 10,000 customer records with realistic attributes and a 5% corruption rate to test your Bronze layer ingestion and DLT quality checks.

Quick Start

Generate 1000 customer records with a 5% corruption rate for the default catalog and schema.