The Dremio Blog

Apache Iceberg 1.12 and Iceberg Rust 0.11 are close to the finish line, and neither one is a routine patch. Apache Iceberg 1.12.0 for Java carries 210 merged pull requests on its milestone. Iceberg Rust 0.11.0 packs roughly two months of work, including end-to-end table encryption and Variant support. Both went through release candidate trouble in mid-September 2026, which tells you something about how much changed.

A manifest file is an Avro file listing data files that belong to an Apache Iceberg table, along with per-column statistics for each one. Manifests are why Iceberg can plan a query without listing directories: the engine reads partition summaries in the manifest list to skip whole manifests, then reads column bounds inside the survivors to skip individual files. Neither stage touches your data.

Apache Polaris implements the Iceberg REST Catalog specification, a set of HTTP endpoints that any Iceberg client can speak. The specification covers namespace and table management, an atomic commit protocol built on assertions rather than locks, server-side scan planning, and three different ways for a client to obtain storage access. Because the contract is the specification rather than a vendor SDK, an engine written against one implementation works against all of them.

Choosing an Apache Iceberg catalog comes down to three questions, and the feature matrices most vendors publish do not help with any of them. Do you need more than one engine to write to the same tables? Do you need governance you do not control to be predictable? And do you need to keep the catalogs you already run while you migrate? Answer those and the field narrows from seven options to about two.

Apache Polaris controls access through role-based access control with four levels: a privilege is granted to a catalog role, the catalog role is granted to a principal role, and the principal role is assigned to a principal. Privileges never attach to a person directly. The two privileges that matter most, TABLE_READ_DATA and TABLE_WRITE_DATA, are the only ones that cause Polaris to issue short-lived storage credentials, and they sit outside the metadata grant that covers everything else.

Apache Polaris is a catalog service for Apache Iceberg. It answers the question “which files make up this table right now, and are you allowed to touch them?” Its architecture has four moving parts: an entity model that nests catalogs, namespaces and tables inside a realm, a persistence layer that stores those entities and their grants, a REST service that speaks the Iceberg REST Catalog API, and a credential broker that hands engines short-lived, path-scoped access to object storage.

The state of the open lakehouse in September 2026 is a stack that stopped arguing about whether it won and started dealing with the consequences of winning.

This is Part 15, the final article of a 15-part Apache Iceberg Masterclass. Part 14 covered hands-on Dremio Cloud. This article covers the three migration strategies and how to execute a zero-downtime migration using the view swap pattern. Most organizations do not start with Iceberg. They have years of data in Hive tables, data warehouses, CSV files, databases, […]

This article is a practical walkthrough of working with Iceberg on Dremio Cloud, covering table creation, data ingestion, optimization, semantic layer construction, and AI-powered analytics.

Streaming ingestion bridges this gap by committing data to Iceberg tables at regular intervals. The challenge is that frequent commits create the small file problem, and managing that trade-off between data freshness and table health is the central concern of streaming to Iceberg.

This article covers the two main ways to access Iceberg data: directly from Python libraries and through MPP (massively parallel processing) query engines.

This article covers the metadata tables that let you inspect Iceberg table internals using standard SQL. Iceberg exposes its internal metadata as queryable virtual tables.

This article covers the four maintenance operations that keep Iceberg tables healthy and the three approaches to running them.

An Iceberg table that works well on day one will not work well on day 365 without maintenance. Every append, update, and delete operation adds files and metadata.

This article examines a newer approach: embedding the catalog directly inside the storage layer. Traditional Iceberg architectures have three components: the query engine, a standalone catalog, and object storage.