Building production-ready data pipelines in Microsoft Fabric: A complete data quality framework with dlthub

A comprehensive guide to building production-ready data pipelines in Microsoft Fabric using dltHub (dlt), addressing the platform's lack of a built-in data quality engine. Covers a six-stage data quality lifecycle: source profiling, schema/contract enforcement, pre-load Write-Audit-Publish (WAP) validation, controlled lakehouse loading, monitoring, and iterative improvement. Details two deployment patterns—validating at ingestion (Pattern A) vs. using dlt as a quality gate between Bronze and Silver medallion layers (Pattern B)—with code examples and tradeoff analysis. Also covers PII detection and masking strategies, quarantine table patterns for failed records, and monitoring metrics. Particularly targeted at small data teams (1–2 engineers) who need scalable, low-overhead data quality without dedicated tooling.