Technology

Dremio

A lakehouse query engine that provides SQL analytics directly on S3-stored data with integrated Iceberg table management, data reflections (materialized views), and a semantic layer.

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Summary

What it is

A lakehouse query engine that provides SQL analytics directly on S3-stored data with integrated Iceberg table management, data reflections (materialized views), and a semantic layer.

Where it fits

Dremio occupies the query engine layer between S3 object storage and BI/analytics tools. It differentiates from Trino and Spark by combining query execution with built-in Iceberg catalog management and acceleration structures (reflections) that reduce S3 scan overhead.

Misconceptions / Traps

Dremio is not just another Trino distribution. Its reflection-based acceleration, Arrow Flight-based connectivity, and integrated Iceberg catalog differentiate its architecture.
Reflections (pre-computed aggregations and materializations) must be maintained. Stale reflections serve incorrect results, and maintaining them adds operational cost.
Dremio Cloud and Dremio Software have different feature sets. Self-managed Dremio requires capacity planning for coordinator and executor nodes.

Key Connections

scoped_to Lakehouse, S3 — queries S3-stored lakehouse data
depends_on Apache Iceberg — native Iceberg table format support
depends_on Apache Arrow — uses Arrow Flight for data transfer
solves Cold Scan Latency — reflections pre-compute query results

Definition

What it is

A lakehouse query engine that provides SQL access to data on S3 with a built-in reflections layer (materialized accelerations), an integrated Iceberg catalog (Arctic/Nessie-based), and sub-second query performance via Apache Arrow-based execution.

Why it exists

Query engines like Trino and Spark require external catalogs and lack built-in acceleration layers. Dremio packages catalog management, query acceleration, and Iceberg-native operations into a unified engine optimized for S3-based lakehouses.

Primary use cases

Interactive SQL analytics over S3, Iceberg table management, self-service BI acceleration on lakehouse data.

Recent developments

Latest signals

SAP is acquiring Dremio. SAP announced the acquisition in early May 2026, with the deal expected to close in Q3 2026 (terms undisclosed). SAP plans to fold Dremio into its Business Data Cloud, using Dremio's Iceberg-native engine and open Apache Polaris catalog as the semantic/discovery layer over both SAP and non-SAP data for agentic-AI workloads. Per Blocks & Files.
Iceberg V3 support went GA (April 2026). The current release adds Apache Iceberg V3 features — VARIANT type, deletion vectors, and row-level lineage — alongside autonomous Reflections, Iceberg clustering with Z-order, two-level pruning, and automated compaction. Per The Best Data Lakehouse Tools for Apache Iceberg in 2026.
Autonomous performance features reached GA. New GA capabilities include Live Reflections (auto-updating materialized views), Result Set Caching (up to 28x query acceleration), Reflection Recommendations (analyzes query patterns to suggest accelerations), and Automatic Iceberg Data Ingestion (auto-ingest pipes from S3). Per DBTA.
TPC-DS benchmark claim: fastest lakehouse. Dremio reports completing all 99 TPC-DS queries on a 1TB dataset in 22 seconds on an 8-node cluster, claiming up to 20x faster than major cloud lakehouse providers, crediting Reflections, the Apache Arrow engine, Columnar Cloud Cache (C3), and Iceberg-native architecture. Per Dremio.
Recognized in Forrester's Data Lakehouses Landscape, Q1 2026. Dremio was named in Forrester's Q1 2026 Data Lakehouses Landscape report, which found 92% of organizations plan to shift most analytic/AI workloads to a lakehouse within a year and 87% expect the lakehouse to be their primary architecture by 2027. Per GlobeNewswire.