✨ AI-assisted Lakehouse Monitoring

Overview

AI-Assisted Lakehouse Monitoring introduces an intelligent observability layer across IOMETE’s data platform — leveraging machine learning and natural language models to automatically detect anomalies, predict performance degradation, and guide users toward proactive resolution.

This initiative evolves traditional monitoring into autonomous, insight-driven observability, combining system telemetry, query analytics, and cost metrics to ensure platform reliability, efficiency, and transparency.

By embedding AI-assisted monitoring directly into the Lakehouse control plane, teams can understand what’s happening, why it’s happening, and how to fix it — faster and with less manual effort.

Planned Features

• Unified Monitoring Dashboard: Central view for compute, storage, and query performance metrics.
• Anomaly Detection (AI-powered): Automatically identify irregularities in query latency, job failures, or cost spikes.
• Automated Alerting: Trigger alerts through email, Slack, or Teams when thresholds or AI-based risk scores are exceeded.
• Usage & Cost Breakdown: Track consumption across domain users, and workloads for governance visibility.
• Workload Optimization Suggestions: Recommend adjustments (cluster size, query parallelism, caching) to improve efficiency.
• Conversational Monitoring Interface: Chat-style interaction (“Why is my query slower today?” → detailed explanation + suggestions).

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