Posts
All the articles I've posted.
- 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 - 14 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 - 13 MIN READ•Jun 22, 2026
PyIceberg at Scale Without Apache Spark
Python-first Iceberg work is useful when it stays honest about what Python should and should not do.
Spark-free IcebergPython Iceberg pipelinesREST catalog concurrency - 14 MIN READ•Jun 22, 2026
The Real-Time Lakehouse with Streaming and Iceberg
The real-time lakehouse is not one engine. It is a contract between streams, table commits, query paths, and freshness expectations.
streaming SQLIceberg storageevent-driven lakehouse - 13 MIN READ•Jun 22, 2026
REST Catalog V2 LoadTable and Client Capability
REST Catalog V2 LoadTable work matters because clients and catalogs need explicit contracts, not optimistic assumptions.
client capabilitiesIceberg REST catalogopen catalog contracts - 14 MIN READ•Jun 22, 2026
Rust vs C++ in Native Iceberg Scan Operators
The Rust versus C++ discussion is really about table-layer execution safety, interoperability, and performance envelopes.
Rust data systemsC++ query enginesvectorized scans