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Iceberg Lakehouse is the technical encyclopedia for Apache Iceberg, lakehouse catalogs, the Agentic Lakehouse, and modern data architecture. Whether you are learning what table formats are, how to deploy Apache Polaris, or how to connect engines to Iceberg tables, you will find the definitive reference material here.

This blog is not affiliated with the Apache Foundation or the Apache Iceberg project whose official page is iceberg.apache.org.

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Recent Posts

• 31 MIN READ•Jul 28, 2026

  Guardrails for Analytics Agents That Do More Than Answer Questions

  The risk isn't agents going rogue, it's agents acting correctly on bad input at machine speed. Here's how to classify actions by consequence, gate capability, and design approval steps people actually use.

  AI AgentsGuardrailsData Governance
• 31 MIN READ•Jul 28, 2026

  Building Agent Telemetry Tables in Iceberg That Survive an Audit

  A practical guide to building agent decision traces in Apache Iceberg that support audit reconstruction, governance review, and cost attribution across sessions.

  Apache IcebergAI AgentsData Governance
• 31 MIN READ•Jul 28, 2026

  What Agentic Analytics Actually Costs, and How to Keep It Bounded

  Agent analytics generates two cost streams that scale on different variables. Here's the arithmetic, the levers that actually move the number, and how to build attribution before you need it.

  AI AgentsAnalyticsCost Optimization
• 31 MIN READ•Jul 28, 2026

  Running an Apache Iceberg Lakehouse With No Internet Connection

  A practical guide to deploying an Iceberg lakehouse in air-gapped environments: component choices, artifact pipelines, identity without a cloud, and the operational realities that surprise teams.

  Apache IcebergAir-GappedData Engineering

Must Reads on Iceberg, Agentic AI and Lakehouse from Around the Web

• The Definitive Guide to the Semantic Layer

  Understand what a semantic layer is, why it matters for modern data architectures, and how it creates a consistent, governed layer between raw data and business consumers.

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• Apache Polaris: The Catalog Standard for Lakehouses and AI

  A deep dive into Apache Polaris, the open-source catalog that is emerging as the standard for managing Iceberg tables across multi-engine Lakehouses and AI workloads.

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• What Are Table Formats and Why Were They Needed?

  Explore the history and motivations behind open table formats like Apache Iceberg, Delta Lake, and Apache Hudi, and why they solved critical problems in big data engineering.

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• What is Dremio?

  A comprehensive overview of Dremio's Lakehouse platform — how it unifies data access, accelerates queries, and powers self-service analytics across cloud and on-premise sources.

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• What Apache Iceberg Native Actually Means

  Not all Iceberg integrations are equal. This article breaks down what it truly means for a platform to be 'Apache Iceberg native' and why the distinction matters for your architecture.

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• Open Source and the Data Lakehouse

  A survey of the open source ecosystem powering modern Data Lakehouses — from Apache Iceberg and Nessie to Apache Arrow and Spark — and how they work together.

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• What is Agentic Analytics?

  Discover how AI agents are transforming analytics pipelines — autonomously querying data, generating insights, and taking actions — and what it means for the future of the Lakehouse.

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