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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

• 30 MIN READ•Jul 6, 2026

  Delete Files vs Deletion Vectors in Apache Iceberg: How V3 Rewrote the Economics of Changing Data

  Iceberg v2 delete files vs v3 deletion vectors. How Roaring bitmaps and per-file vectors transformed merge-on-read performance for CDC and streaming workloads.

  Apache Icebergdata engineeringlakehouse architecture
• 30 MIN READ•Jul 6, 2026

  The Variant Type in Apache Iceberg: How Shredding Turns Messy JSON Into Fast Analytics

  Apache Iceberg v3 introduces the Variant type for flexible JSON storage with columnar performance. Learn how shredding enables fast analytics on semi-structured data.

  Apache Icebergdata engineeringlakehouse architecture
• 30 MIN READ•Jul 6, 2026

  The State of Apache Iceberg v4 in July 2026: What the Dev List Tells Us About the Format's Next Chapter

  What the Iceberg v4 dev list tells us about adaptive metadata trees, single-file commits, column updates, and the format's next chapter in mid-2026.

  Apache Icebergdata engineeringlakehouse architecture
• 30 MIN READ•Jul 6, 2026

  The State of Streaming to Apache Iceberg in July 2026: Every Path, Its Latency, and What to Do When Seconds Are Not Fast Enough

  Every path for streaming data into Iceberg in 2026 — Flink, Spark, Kafka Connect, broker-native, managed pipelines — with honest latency numbers and sub-second hybrid architectures.

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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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