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Blueprint Card
Overview
Uses AI to automatically profile incoming data feeds by assessing schema structures, value distributions, and statistical properties in real time. It continuously scans for quality issues and anomalies—such as schema drift, missing attributes, duplicate records, and outliers—and generates intelligent remediation recommendations or direct fixes (including automated NiFi flows and Spark jobs) to significantly enhance data reliability for downstream enterprise analytics and AI models.
Demo
Walkthrough via Reprise Watch the demo
Key Features
Automated AI profiling to evaluate incoming data feeds and continuously detect quality issues, anomalies, and schema drift.
Autonomous generation of remediation assets and pipelines, including Apache NiFi flows, Spark jobs, and YAML policies.
Intelligent classification and PII sensitivity tagging integrated with governance and security policies.
Version-controlled asset history enabling human-in-the-loop review, approval, and seamless deployment tracking.
Use Case
Autonomous AI agentic workflows to build self-healing data pipelines that continuously detect anomalies, rectify schema drift, and guarantee high-fidelity data reliability for enterprise analytics and downstream AI models.
Architecture
Autonomous Data Quality Agents running on Cloudera AI Workbench and Inference Service (NVIDIA NIM integration). The agent profiles data feeds, classifies PII and sensitive tags, generates automated NiFi flows and Spark remediation jobs, and tracks asset history for steward review before executing across Cloudera DataFlow, Data Engineering, and Open Data Lakehouse components.
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