AI-Ready Metadata Prevents Query Failures
AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at execution time.
The archive
Writing, page 23
657 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
Autonomous Materialization for Agentic Analytics
Autonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management.
Composable Semantic Layers for Analytical Agents
AI agents need more than metric names. They need composable business logic that survives multi-step analysis.
Built for Agents and Managed by Agents
Dremio Agentic Lakehouse is easiest to understand as two ideas: data built for agent access and platform work managed by agents.
ClickHouse in the Loop for Active Agents
Low-latency analytical systems can help active agents, but only when event loops include validation, context, and safety boundaries.
The Context Layer for AI Agents
A semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context.
Lakehouse as the Operating Layer for Agentic AI
Agentic AI announcements are useful when they validate the need for governed data, semantic context, and cost-aware execution.
Event-Driven Table Compaction with Agents
Event-driven compaction is valuable when agents coordinate maintenance with workload signals, table health, and commit safety.
Fabric Agentic Analytics and Lakehouse Schema Design
Microsoft Fabric agentic analytics is a reminder that schemas, semantic models, and governed lakehouse design now shape AI behavior.
Fine-Grained Security for AI Agents
Machine-speed analytics requires machine-enforced policy, identity, masking, filtering, and audit controls.