Written by Alex Merced Developer from devNursery.com and alexmercedcoder.dev You should follow him on Twitter and checkout his articles on LogRocket.
The four-layer architecture of the agentic lakehouse: object storage, Apache Iceberg table format, Apache Polaris catalog, and the semantic/agent layer. How each layer provides guarantees for AI agent access.
ClickHouse has become the leading real-time analytics engine for AI agent workloads, with event-loop architectures that let agents query and act at sub-second speeds.
Metric catalogs define what terms mean. Composable analytics defines how terms combine, transform, and relate. For AI agents, composability is what turns definitions into reasoning.
Lakehouse context layers bridge the gap between raw Iceberg tables and AI agents that need business meaning. Atlan and Snowflake Horizon each take different approaches to the same problem.
A deep technical breakdown of Apache Iceberg v4's proposed architecture: adaptive metadata trees, one-file commits, relative paths, column families, and what the Delta 5.0 convergence actually means for your data platform.
Iceberg REST catalog remote signing provides per-file pre-signed URL access for regulated datasets. How it differs from credential vending, audit trail capabilities, and Snowflake implementation for PII/compliance workloads.
