The Dremio Blog

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.

Iceberg 1.11.0, released in May 2026, contains the full implementation of the v3 spec(ification), including features like deletion vectors, the VARIANT type, and row lineage. But what comes next? V4, obviously, as four comes after 3. However, it's still early days on the v4 spec, so we are a ways off from the next version […]

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.

Apache Ossie is currently undergoing incubation at The Apache Software Foundation (ASF). If you've been following the Open Semantic Interchange project — the open specification for semantic layer and ontology — there's an important update. The project has been accepted into the Apache Incubator under a new name: Apache Ossie (Incubating). The spec, the community, and […]

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.

Apache Parquet is the recommended file format used in every modern data platform, and for good reason. But what are those reasons? And would it really matter if you stuck with CSV? The short answer is "YES". The slightly longer answer is "Yes, because columns". The full answer is below, so keep on reading to […]

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.

For most of its history, the semantic layer was considered a solved problem. You built it once, business users queried it wherever it lived, and (hopefully) everyone would agreed on what "revenue" meant. However, much like the information in your data dictionary, the popularity of the semantic layer went stale and businesses turned to new, […]

Data pipelines used to require a lot of infrastructure to keep running: separate compute for transformation, staging layers between systems, and a growing stack of tools to manage it all. Dremio changes the equation. With native ingestion, flexible transformation, and AI-assisted pipeline development, teams can build and operate end-to-end ELT workflows directly in the lakehouse, […]

Most conversations about AI and data platforms start with MCP. That's understandable: the Model Context Protocol has become the standard way to give AI agents a window into a data system, and Dremio's MCP server does this well. But MCP solves the specific problem of giving agents a supervised, conversational interface to your data. What […]