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This blog contains articles about the Apache Iceberg Data Lakehouse (using your data lake as your data warehouse) and the Agentic Lakehouse (lakehouses optimized for AI agents). If you are looking to understand what Apache Iceberg is, how a data lakehouse works, or where to find deep-dive resources, you will find all of that content right 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

• 8 MIN READ•May 28, 2026

  Active Monitoring: How Agentic AI Auto-Heals and Protects Enterprise Data Pipelines

  Static alerts miss cascading pipeline failures. Learn how agentic AI monitors, traces root causes, and automatically rolls back broken enterprise data pipelines.

  Active Monitoring Agentic AI Pipelines
• 7 MIN READ•May 28, 2026

  Anatomy of an Agentic Analytics System: Inside the Multi-Step Reasoning Loop

  How does an agentic analytics system actually work? Inside the ReAct loop, tool calling, schema exploration, and self-correction that power autonomous data agents.

  Anatomy Agentic Analytics System
• 8 MIN READ•May 28, 2026

  Mastering Apache Iceberg v3: What's New and How to Plan Your Upgrade

  Apache Iceberg v3 adds deletion vectors, VARIANT type, row lineage, and table encryption. Here's what changed and how to plan your upgrade.

  Apache Iceberg V3 Upgrade
• 7 MIN READ•May 28, 2026

  Building a Custom Agentic Analytics System: Python, LangChain, and SQL Data Lakes

  Build a custom agentic analytics system using Python, LangChain, and Dremio. A developer tutorial covering SQL tool binding, prompt design, and secure execution.

  Building Custom Agentic Analytics Python

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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