Data Quality Across Platforms: SQL to Microsoft Fabric Guide
Most organisations run several data platforms at once – Oracle for finance, MySQL for product, Microsoft Fabric for BI. The usual response is a separate quality checking tool for each one, which means three sets of rules, three owner lists, and three places an auditor has to look. This article explains how one check registry extends from a single SQL Server database to Oracle enterprise applications, MySQL, and every layer of a Fabric medallion architecture without rebuilding anything or splitting your audit trail.
Data Quality Monitoring Explained
Most data quality problems are discovered the wrong way round: a stakeholder notices a number that looks off in Power BI, and only then does anyone go looking for the bad record sitting three tables upstream. The Numlytics Data Quality Monitor flips that – a governed check registry and live Power BI dashboard that catches failing records before they reach a report, running on SQL Server, Oracle, MySQL, or Microsoft Fabric with no external service and no data leaving your environment.
The Real Cost of Bad Data: What It Actually Costs Across Oracle, SQL Server, and Microsoft Fabric
The cost of bad data never shows up as a single line item. It shows up as a finance team quietly re-running a report before a board meeting, an analyst spending an afternoon reconciling two numbers that should already match, and a sales forecast leadership stopped trusting months ago without ever saying so. This article walks through where those costs actually hide – inside an Oracle ERP and across a Microsoft Fabric medallion architecture and what a governed data quality framework changes about that pattern.