---
title: "Apache Iceberg"
description: "24 posts about Apache Iceberg, summarised, each linking to the original."
---

# Apache Iceberg
> 24 posts about Apache Iceberg, summarised, each linking to the original.

## Articles

### [Advancing Apache Iceberg on Databricks: Iceberg v3 GA, Open Sharing, and Unified Governance](https://yomu.fyi/post/advancing-apache-iceberg-on-databricks-iceberg-v3-ga-open-sharing-and.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jason Reid, Ryan Blue, Daniel Weeks, Michelle Leon
- Published: May 28, 2026
- Type: Announcement

Databricks announces a broad set of Apache Iceberg capabilities in Unity Catalog, spanning General Availability, previews, and beta releases. Managed Iceberg is GA, supporting table creation, reads, writes, optimization, governance, and sharing, while Iceberg v3 adds deletion vectors, row tracking, and VARIANT across managed, foreign, and UniForm-enabled tables. Unity Catalog also federates external catalogs, vends credentials, shares live data with Iceberg REST-compatible clients through Delta Sharing, and applies attribute-based access control during server-side scan planning for supported external engines. These capabilities are presented as a unified approach to open APIs, cross-engine governance, zero-copy sharing, and production performance without copying data. The post also outlines Iceberg v4 and a proposal for Delta 5.0 to adopt an adaptive metadata tree structure.


### [The Convergence of Open Table Formats and Open Catalogs: Catalog Commits is Generally Available](https://yomu.fyi/post/the-convergence-of-open-table-formats-and-open-catalogs-catalog-commit.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Benjamin Mathew, Michelle Leon, Lukas Rupprecht, Ryan Johnson
- Published: May 12, 2026
- Type: Announcement

Catalog Commits is generally available for Unity Catalog managed Delta tables, aligning Delta with Iceberg’s catalog-oriented model and making the catalog responsible for table discovery, access, and latest table state. The change addresses three coordination problems: metadata “split brain” when engines write directly to storage, fragmented multi-engine governance, and the historical inability to coordinate atomic writes across multiple tables. With Catalog Commits enabled, Unity Catalog brokers table access through standardized APIs, keeping catalog and table state synchronized and enabling consistent authorization, holistic auditability, automated optimizations, and multi-statement, multi-table ACID transactions on Databricks. The release supports Databricks products and engines including Delta Spark, Delta Flink, Starburst Trino, DuckDB, and StreamNative, while Delta Kernel provides a shared path for connector support.


### [Interoperability between Unity Catalog and Google BigQuery via catalog federation](https://yomu.fyi/post/interoperability-between-unity-catalog-and-google-bigquery-via-catalog.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: John Spencer, Jason Reid
- Published: Apr 29, 2026
- Type: Announcement

Databricks and Google Cloud announce bidirectional catalog federation that lets customers access the same data from Unity Catalog or BigQuery without duplication. The interoperability uses Apache Iceberg REST catalog APIs, enabling both platforms to read and write the same tables through a common open interface. Google Cloud's catalog federation is in preview for reading Unity Catalog-managed tables from BigQuery, while Google Cloud's Lakehouse federation is in private preview for Databricks users to govern and read foreign Iceberg tables. Unity Catalog's policies, fine-grained access controls, and lineage tracking apply to federated data, and users can query it with Genie using natural language. The announcement positions open standards as a path to interoperability across data platforms and previews future catalog metadata exchange through Iceberg REST catalog APIs.


### [Real-Time Decisioning for AI Agents: Why you Need a Customer Context Layer First](https://yomu.fyi/post/real-time-decisioning-for-ai-agents-why-you-need-a-customer-context-la.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alex Dean
- Published: Apr 21, 2026
- Type: Explainer

Real-time decisioning for AI agents depends on more than customer records: it requires a customer context layer that captures what people are doing now and connects that behavior to identity. The post positions the data platform as the center of a composable martech architecture, with agents and applications operating on shared data in open formats such as Apache Iceberg and Delta Lake. Snowplow’s described approach places structured event collection, schema validation, enrichment, and identity resolution before data reaches the platform, while operating in the customer’s cloud environment. It distinguishes historical profiles from granular behavioral event streams and argues that agent interaction outcomes should return to the foundation as first-class events. The resulting four-stage loop—collect, resolve and enrich, serve, and learn—supports simultaneous real-time and historical context, with decision quality dependent on source data coherence.


[Newer posts](https://yomu.fyi/topic/apache-iceberg.md)