Explains how Iceberg v3's positional deletes and merge-on-read improve event lake performance for fast-inbound data corrections.
Explores Iceberg v3's variant type for standardizing semi-structured AI JSON payloads in lakehouse architectures.
A 2026 playbook for migrating proprietary data warehouses to open lakehouses using zero-copy metadata translation and staged modernization.
Explores multi-engine catalog federation using Apache Polaris to sync metadata across Google Cloud, AWS, and Azure for open lakehouse governance.
Explains how to prepare a data lakehouse for EU AI Act compliance, focusing on data lineage and provenance.
Technical guide on preparing data lakehouses for EU AI Act compliance, focusing on auditable lineage and data provenance.
Analysis of stateless MCP patterns for data platforms, focusing on agentic lakehouse architecture and Dremio integration.
A guide to evaluating agentic analytics tools for enterprise AI, focusing on governance, semantics, and production readiness.
Explains the need for machine-readable metric contracts to standardize business meaning before AI agents access data platforms.
A deep dive comparing block vs. object storage, explaining how lakehouses made slower object storage fast for analytics.
Explores federation vs. lakehouse architectures for unified data access, offering a decision framework and best practices.
An in-depth guide to file compression codecs, covering how data shrinks, codec differences, and practical recommendations for lakehouse architectures.
Analysis of the renaissance in columnar file formats, comparing Parquet, Lance, Vortex, Nimble, and BtrBlocks for modern data workloads.
Explains file encryption for lakehouses, covering Parquet Modular Encryption, Iceberg table encryption, and interoperability challenges.
Explains what open source foundations like Apache, Linux, and Eclipse actually do, focusing on governance differences and their impact on software projects.
Explains the fundamental divide between operational (OLTP) and analytical (OLAP) systems, the physics enforcing it, and the truth about hybrid architectures.
Explores context engineering for AI agents, comparing personal vs. shared context to prevent failures in human and organizational AI use.
Analysis of five lakehouse table formats (Iceberg, Delta Lake, Hudi, Paimon, DuckLake) in 2026: how they work, current status, and future directions.
Explores semantic layers in data platforms, their role in ensuring metric trustworthiness, and their critical importance for AI agent accuracy.
Deep dive into designing custom AI agent harnesses, covering architecture layers like loops, tools, context, and control for production systems.
