From Anomaly Detection to Agentic AI: Engineering Intelligent Data Reliability on Databricks
How Lokeshkumar Madabathula Is Exploring Intelligent Data Operations Through Real-Time Anomaly Detection, Lakehouse Architecture, and Governed Agentic AI
As enterprises increasingly depend on real-time data for analytics, operational decisions, machine learning, and artificial intelligence, maintaining data reliability has become a critical engineering challenge. Traditional monitoring systems can identify failures and generate alerts, but understanding why an issue occurred, what downstream systems may be affected, and how it should be resolved often requires extensive manual investigation.
Lokeshkumar Madabathula, a senior data engineering professional with experience in enterprise-scale data and analytics solutions, is exploring this challenge through his work, “From Anomaly Detection to Agentic AI: Engineering Intelligent Data Reliability on Databricks.” The initiative examines how real-time data engineering, machine-learning-based anomaly detection, lakehouse architecture, and governed Agentic AI can be combined to support more intelligent enterprise data operations.
The central objective is to move beyond reactive monitoring toward a reliability framework that can...
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