Category: Lakehouse

2026-06-22 • Alex Merced

AI-Ready Metadata Prevents Query Failures

AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at executi...

2026-06-22 • Alex Merced

Autonomous Materialization for Agentic Analytics

Autonomous materialization accelerates agentic analytics by letting AI agents create and manage materialized views witho...

2026-06-22 • Alex Merced

Composable Semantic Layers for Analytical Agents

Composable semantic layers for analytical agents go beyond metrics lists to provide rich, reusable business context for ...

2026-06-22 • Alex Merced

Built for Agents and Managed by Agents

The Dremio Agentic Lakehouse is built for agents and managed by agents, creating an autonomous data platform for AI-driv...

2026-06-22 • Alex Merced

ClickHouse in the Loop for Active Agents

ClickHouse in the loop for active agents enables real-time event streams and low-latency analytics for agent-driven deci...

2026-06-22 • Alex Merced

The Context Layer for AI Agents

The context layer for AI agents provides semantic lineage, quality, and governance metadata so agents understand the dat...

2026-06-22 • Alex Merced

Lakehouse as the Operating Layer for Agentic AI

The lakehouse as the operating layer for agentic AI provides governed, semantic-aware data infrastructure for autonomous...

2026-06-22 • Alex Merced

Event-Driven Table Compaction with Agents

Event-driven table compaction with agents automates Iceberg maintenance using agentic coordination for optimal table per...

2026-06-22 • Alex Merced

Fabric Agentic Analytics and Lakehouse Schema Design

Fabric agentic analytics and lakehouse schema design explores how Microsoft Fabric architecture enables AI-driven analyt...

2026-06-22 • Alex Merced

Fine-Grained Security for AI Agents

Fine-grained security for AI agents requires agent identities, token exchange, and column-level guardrails to protect se...

2026-06-22 • Alex Merced

Iceberg v4 Performance: Root Manifests and Calls

Iceberg v4 performance improvements through root manifests reduce metadata round trips and optimize object storage plann...

2026-06-22 • Alex Merced

What Is LTAP in the Lakehouse?

LTAP in the lakehouse combines transactional and analytical processing for real-time Iceberg updates with analytical fre...

2026-06-22 • Alex Merced

The Model Is Not the Moat

The model is not the moat — the semantic lakehouse layer and governed enterprise AI infrastructure provide the real comp...

2026-06-22 • Alex Merced

PyIceberg at Scale Without Apache Spark

PyIceberg at scale without Apache Spark demonstrates Python-native Iceberg pipelines using REST catalog concurrency for ...

2026-06-22 • Alex Merced

The Real-Time Lakehouse with Streaming and Iceberg

The real-time lakehouse with streaming SQL and Iceberg storage enables event-driven lakehouse architectures for low-late...

2026-06-22 • Alex Merced

REST Catalog V2 LoadTable and Client Capability

REST Catalog V2 LoadTable and client capability protocols define open catalog contracts for interoperable Iceberg catalo...

2026-06-22 • Alex Merced

Rust vs C++ in Native Iceberg Scan Operators

Rust vs C++ in native Iceberg scan operators compares performance and safety tradeoffs for vectorized scans in modern da...

2026-06-22 • Alex Merced

Server-Side Commit Deconflicting in REST Catalogs

Server-side commit deconflicting in REST catalogs resolves Iceberg commit conflicts with optimistic concurrency control ...

2026-06-22 • Alex Merced

Snowflake Interoperable Lakehouse Lessons

Snowflake interoperable lakehouse lessons explore Iceberg interoperability and open table formats for multi-engine lakeh...