What Is LTAP in the Lakehouse?
Lakehouse transactional analytical processing is useful only when teams define freshness, isolation, and workload boundaries clearly. For…
The archive
Writing, page 13
546 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
The Model Is Not the Moat
Enterprise AI advantage increasingly comes from governed context, semantic models, and operational data contracts, not only from model…
PyIceberg at Scale Without Apache Spark
Python-first Iceberg work is useful when it stays honest about what Python should and should not do. For Python data engineers and platform…
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. For…
REST Catalog V2 LoadTable and Client Capability
REST Catalog V2 LoadTable work matters because clients and catalogs need explicit contracts, not optimistic assumptions. For lakehouse…
Rust vs C++ in Native Iceberg Scan Operators
The Rust versus C++ discussion is really about table-layer execution safety, interoperability, and performance envelopes. For engineers…
Server-Side Commit Deconflicting in REST Catalogs
Server-side commit deconflicting is about moving concurrency control closer to the catalog contract. For engineers responsible for…
Snowflake Interoperable Lakehouse Lessons
Interoperable lakehouse announcements matter when they change production contracts, not just import and export narratives. For platform…
Unity AI Gateway vs Apache Polaris Control Planes
The right comparison is not vendor scoreboard. It is closed AI governance gateway versus open catalog control plane. For enterprise…
The Complete Guide to Agentic Coding Tools in 2026
Agentic coding tools have matured into four distinct categories that serve different developer workflows: CLI agents for terminal-first…