Posts
All the articles I've posted.
- 14 MIN READ•Jun 22, 2026
AI-Ready Metadata Prevents Query Failures
AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at execution time.
lineage quality LLM query failuresmetadata for AI agentsgoverned analytics - 14 MIN READ•Jun 22, 2026
Autonomous Materialization for Agentic Analytics
Autonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management.
AI agents table performancereflectionsautomated acceleration - 15 MIN READ•Jun 22, 2026
Composable Semantic Layers for Analytical Agents
AI agents need more than metric names. They need composable business logic that survives multi-step analysis.
semantic layer agentsmetrics catalogsagentic analytics - 15 MIN READ•Jun 22, 2026
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.
built for agentsmanaged by agentsautonomous lakehouse - 14 MIN READ•Jun 22, 2026
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.
real-time event streamsactive analytics agentslow latency BI - 14 MIN READ•Jun 22, 2026
The Context Layer for AI Agents
A semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context.
AI metadatalineagedata quality - 15 MIN READ•Jun 22, 2026
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.
agentic analyticslakehouse operating layergoverned AI data - 14 MIN READ•Jun 22, 2026
Event-Driven Table Compaction with Agents
Event-driven compaction is valuable when agents coordinate maintenance with workload signals, table health, and commit safety.
agentic compactionIceberg maintenancetable optimization - 15 MIN READ•Jun 22, 2026
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.
Microsoft Fabric agentic analyticslakehouse schema designAI analytics stack - 13 MIN READ•Jun 22, 2026
Iceberg v4 Performance: Root Manifests and Calls
Apache Iceberg v4 discussion should focus on planning cost, metadata layout, and object storage round trips, not vague claims about faster tables.
root manifestsmetadata round tripsobject storage planning - 13 MIN READ•Jun 22, 2026
What Is LTAP in the Lakehouse?
Lakehouse transactional analytical processing is useful only when teams define freshness, isolation, and workload boundaries clearly.
lakehouse transactional analytical processingIceberg updatesanalytical freshness - 14 MIN READ•Jun 22, 2026
The Model Is Not the Moat
Enterprise AI advantage increasingly comes from governed context, semantic models, and operational data contracts, not only from model choice.
enterprise AI moatsemantic layergoverned lakehouse AI