1 min read
Implementing Data Quality Checks in Microsoft Fabric Lakehouses
Poor data quality leads to incorrect business decisions, failed ML models, and eroded trust in analytics platforms. A proactive approach to data validation…
Data Quality
11 articles
1 min read
Data Mesh: Federated Computational Governance
Computational governance scales where manual review cannot. Embed policies in CI/CD pipelines to catch issues before they reach production.
1 min read
Data Quality for AI: Ensuring Clean Data for ML and LLM Applications
Quality encompasses completeness, accuracy, consistency, timeliness, and validity. Each dimension requires specific checks.
1 min read
Data Quality Automation with AI: Beyond Rule-Based Validation
AI-powered data quality catches issues that traditional rules miss.
1 min read
Data Quality Automation with AI: Beyond Rule-Based Validation
AI transforms data quality from reactive checking to proactive management. Start with profiling and anomaly detection, then expand to automated rule…
1 min read
Data Contracts: Agreements Between Data Producers and Consumers
Data contracts formalize the agreement between data producers and consumers. Today I'm exploring how to implement effective data contracts in your data…
1 min read
Fabric Endorsement: Certifying Trusted Data Products
Fabric endorsement — the Promoted and Certified badge system for Fabric items — is a lightweight but important trust signal in a self-service analytics…
1 min read
AI-Powered Data Quality: Intelligent Data Validation
AI-powered data quality goes beyond static rules. Intelligent systems understand context, detect subtle anomalies, and provide actionable recommendations…
1 min read
Data Quality with Delta Live Tables Expectations
The expression must evaluate to true for valid records.
2 min read
Data Observability: Monitoring Your Data Pipelines
Data observability is the capability that answers "is my data healthy right now?" the same way application observability answers "is my application healthy…
1 min read
Data Quality Practices: Building Trust in Your Data
Data quality in 2021 became an engineering discipline. The tools matured, but success requires organizational commitment to treating data as a product.