Executive Summary
Data pipelines support modern analytics, AI, and business decision-making. However, a pipeline completing successfully does not always mean the data is accurate, complete, or available on time. Schema changes, missing records, data-quality issues, and upstream failures can affect downstream systems without being immediately detected.
The white paper explores how organizations can make data operations more responsive by introducing an Agentic DataOps control plane alongside their existing ETL and ELT platforms. It explains how agents can use information from logs, metrics, data-quality checks, lineage, run history, and incident records to identify issues, investigate likely causes, assess downstream impact, and recommend recovery actions.
The paper also outlines how these capabilities can work within defined policies, with deterministic checks, human approval where required, and validation after recovery. It presents a practical approach to improving pipeline reliability while maintaining control over automated actions.
Key Highlights
- Limitations of traditional ETL monitoring.
- Agentic DataOps architecture and core components.
- Specialized agents for detection, diagnosis, and recovery.
- Common pipeline failures and practical use cases.
- Microsoft-first, cloud-portable implementation approach.
- Code structure, policies, and automated testing.
- Governance, human approval, and controlled recovery.
- Metrics to measure reliability and operational efficiency.
