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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

• 14 MIN READ•Jun 22, 2026

  AI-Ready Metadata Prevents Query Failures

  AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at execution time.

  lineage quality LLM query failuresmetadata for AI agentsgoverned analytics
• 14 MIN READ•Jun 22, 2026

  Autonomous Materialization for Agentic Analytics

  Autonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management.

  AI agents table performancereflectionsautomated acceleration
• 15 MIN READ•Jun 22, 2026

  Composable Semantic Layers for Analytical Agents

  AI agents need more than metric names. They need composable business logic that survives multi-step analysis.

  semantic layer agentsmetrics catalogsagentic analytics
• 15 MIN READ•Jun 22, 2026

  Built for Agents and Managed by Agents

  Dremio Agentic Lakehouse is easiest to understand as two ideas: data built for agent access and platform work managed by agents.

  built for agentsmanaged by agentsautonomous lakehouse

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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