Analysis of agentic AI standards in 2026, including MCP, A2A, WebMCP, OSI, and the emerging protocol stack for agents.
A comprehensive look at Apache Arrow's impact ten years in, covering its origins, technical details, adoption, and future in AI workloads.
An in-depth analysis of Apache Parquet in 2026, covering new features, versioning debates, and its evolving role in the lakehouse and AI eras.
Analysis of Apache Polaris in July 2026, covering its graduation, release updates, federation, governance, and adoption as an open lakehouse catalog.
Career advice for engineers in the AI era, focusing on scarce resources like reputation and problem selection over solution skills.
Explores an architecture pattern combining Amazon S3 Tables and MCP for governed conversational AI access to Iceberg data.
Explores the trade-offs between decoupled catalogs and managed tables in open table formats like Apache Iceberg, focusing on architectural freedom and operational simplicity.
Explains designing idempotent pipelines in agentic lakehouses to prevent double-write anomalies using Iceberg and workflow safeguards.
Designing secure, air-gapped data lakehouses using Apache Iceberg for defense, healthcare, finance, and other high-security sectors.
Explores fine-grained security for AI agents in analytics, covering dynamic access control, identity propagation, and governance at machine speed.
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.
