What problem does it solve? Building a credible financial-services data foundation on Databricks requires coordinating multi-source ingestion, Unity Catalog governance, medallion architecture, identity resolution, data quality, lineage, and privacy controls — and doing it honestly for demos without overstating simulated evidence. ## Core Features & Use Cases - Multi-Source Medallion Ingestion: Land two distinct source types (e.g., relational snapshots and transaction events) through Bronze, Silver, and Gold layers with preserved source fidelity and quarantined invalid records. - Governance and Privacy by Persona: Define owners, least-privilege grants, and classify sensitive fields (tax IDs, payment identifiers, contact data) for masking, row filters, or restricted schemas. - Visible Quality and Lineage: Persist pass/fail quality counts, rejected rows, and trace one transaction from both sources through all medallion layers. - Use Case: A solutions engineer building a fraud-detection demo needs a governed Layer 1 foundation where transaction events and account snapshots converge on a customer identity spine, with quality evidence and an ML-ready handoff manifest for Layer 2 feature engineering. ## Quick Start Use this skill to design and validate the governed Layer 1 data foundation for my Databricks FSI fraud demo, covering ingestion, medallion layers, quality checks, lineage, and the Layer 2 handoff manifest.
