Going Advanced: Open Source Models, Hermes Agent, and Local AI
Tired of subscriptions and privacy concerns? Open source models and agent frameworks let you run AI on your own hardware.
The archive
652 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
Tired of subscriptions and privacy concerns? Open source models and agent frameworks let you run AI on your own hardware.
A comprehensive comparison of AI agent tooling across Dremio, Snowflake, Databricks, Microsoft Fabric, AWS, Google Cloud, ClickHouse, VeloDB, SpiceAI.
Compare Codex, Claude Code, OpenCode, Gemini CLI, and GitHub Copilot CLI by workflow, model choice, governance, and cost controls.
Static alerts miss cascading pipeline failures. Learn how agentic AI monitors, traces root causes, and automatically rolls back broken enterprise data.
How does an agentic analytics system actually work? Inside the ReAct loop, tool calling, schema exploration, and self-correction that power autonomous.
Apache Iceberg v3 adds deletion vectors, VARIANT type, row lineage, and table encryption. Here's what changed and how to plan your upgrade.
Build a custom agentic analytics system using Python, LangChain, and Dremio. A developer tutorial covering SQL tool binding, prompt design, and secure.
Data lakehouses become data swamps without active governance. Learn how schema enforcement, catalog stewardship, and drift detection keep your lakehouse.
Apache Iceberg gives regulated enterprises data sovereignty with hybrid-cloud deployments. Learn how open catalogs and Iceberg handle compliance requirements.
Apache Iceberg doesn't enforce row and column security on its own. Learn how catalog-level governance with Apache Polaris and Dremio delivers enterprise FGAC.