A check in the wrong place catches bad data too late. See where each data quality check belongs: ingestion, transformation, release gates, and quarantine.
“Clean Data” Is Not a Requirement: Writing Rules People Can Act On
Clean is a feeling, not a spec. Write data quality rules people can act on, with a testable check, a threshold, and a named owner, so every failure is owned.
Count Is Not Risk: Ranking Data Issues by What They Actually Cost
The biggest pile of errors is rarely your biggest problem. Try ranking data issues by impact and likelihood, not row count, and fix what actually hurts.
Data Quality, Data Reliability, and Data Observability: Telling the Three Apart
“The dashboard is wrong” hides three problems. Learn how data quality, data reliability, and data observability differ, and which one you are actually facing.
SQL Server Monitoring Across Cloud, Hybrid, and On-Prem
Let us talk about SQL Server Monitoring Across Cloud, Hybrid, and On-Prem. This is not a tooling problem.
Real-Time Troubleshooting Views That Turn DMV Data Into Answers
Real-time troubleshooting views turn raw DMV data into something a DBA can act on at 9 PM. Here is what belongs in them, how often to collect it, and why wait stats need deltas, not totals.
Deeper Diagnostics and Actionable Dashboards
That is a harder problem than it looks, and most monitoring tools do not solve it. Let us talk about Deeper Diagnostics and Actionable Dashboards.