Add data quality gates to Microsoft Fabric with dlt. Validate schemas, catch bad records, and mask PII before data reaches your lakehouse and downstream analytics.

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.

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

2. The challenges of data quality in Microsoft Fabric Link icon

4. Mapping the DQ lifecycle to dlthub Link icon

5. Protecting sensitive data (PII) Link icon

6. Integrating into a Microsoft Fabric pipeline Link icon

6.5 Alternative pattern: dlt quality gates between medallion layers Link icon