Metadata Intelligence: Making Your Catalog Work for You1. The Problem Every data platform has two estates: the data, and the description of the data. Teams invest heavily in the first and let the second decay. Open almost any mature Unity Catalog and run Oct 3 · 15 min readtech4nirvana.com
Root Cause Analysis Without the War Room1. The Problem A pipeline fails at 2 AM. The on-call engineer opens the job logs, sees a generic Spark exception, and starts the ritual: check the source table, check the schema, check the upstream jo1Aug 16 · 10 min readtech4nirvana.com
Observability That Thinks: AI for Pipeline MonitoringThe Problem Most pipeline observability today is a wall of thresholds. Row count dropped below X. Job ran longer than Y minutes. Null percentage exceeded Z. Someone picked those numbers months ago, of43JKNJul 30 · 8 min readtech4nirvana.com
AI-Driven Data Quality: From Rules to ReasoningEvery data engineering team has a version of the same story. A critical dashboard starts showing numbers that don't feel right. An analyst flags it Friday afternoon. The on-call engineer traces it bacJun 20 · 11 min readtech4nirvana.com
The Case for AI in Data Engineering Series: AI-Augmented Data Engineering | Article 1 of 7 There is a quiet crisis in most data engineering teams. Pipelines fail at 2 AM. The on-call engineer spends three hours tracing a root cause thaMay 31 · 7 min readtech4nirvana.com