How does Dremio achieve sub-second performance on large datasets?

Dremio is built natively on Apache Arrow, a columnar in-memory format that enables vectorized execution with zero-copy data passing between operations. Combined with C3 (Columnar Cloud Cache), a multi-layer NVMe SSD caching tier on executor nodes, result set cache, and Reflections-based query rewriting, the engine avoids redundant I/O and processes data in a format optimized for modern CPUs, delivering consistent sub-second response times even across petabyte-scale data lakes.

Can Dremio query data outside the data lake?

Yes. Dremio supports federated queries across 30+ native source types, including relational databases, cloud data warehouses (Snowflake, Redshift, BigQuery), NoSQL systems, and object storage. Queries execute in place with no data movement, using optimized pushdowns to delegate work to each source engine where possible.

Does Dremio support Apache Iceberg?

Dremio is Iceberg-native. It supports full DML operations (INSERT, UPDATE, DELETE, MERGE), time-travel queries via AT SNAPSHOT and AT TIMESTAMP syntax, Z-order clustering, and automatic compaction, all on open Iceberg tables stored in object storage. Dremio also supports open catalogs like Apache Polaris, so you’re never locked into a proprietary metastore.

How does Dremio support AI agents and modern AI workloads?

Dremio’s query engine is designed for the speed and concurrency that AI agents require. Elastic Engines scale on demand to handle bursts of agentic queries, while workload isolation ensures AI workloads don’t compete with BI or pipeline jobs. Sub-second performance and federated access mean AI agents can retrieve fresh, relevant data from across the data domain without pre-staging or copying it.

How does Dremio handle scaling and cost?

Dremio’s Elastic Engines scale to zero when idle and scale out automatically on demand, so you pay only for active compute. Workload isolation prevents any single job from monopolizing resources, and the elimination of data movement pipelines reduces storage and ETL costs across the board.

Intelligent Query Engine

High Concurrency, High Performance

Built natively on Apache Arrow's columnar format with LLVM code generation, C3 columnar cloud cache, and Elastic Engines for petabyte-scale analytical and AI workloads.

Federated Access Across Your Domain

One SQL interface across 35+ source types (relational databases, data warehouses, object storage, and more) with no data copies required.

Built for Iceberg. Open Formats, No Lock-In.

Native Apache Iceberg support with no proprietary conversion. Works with Dremio's Open Catalog, Apache Polaris, and any Iceberg REST-compatible catalog.

HOW IT WORKS

From query to insight in milliseconds

AI agents and traditional clients connect through one engine. Dremio executes queries across structured and semi-structured data. It leverages pushdowns, columnar caching, and localized materializations (aka Reflections) with automatic query rewrite to deliver the fastest lakehouse performance.

CAPABILITIES

Sub-second queries across any source, on open standards.

Sub-Second Performance at Petabyte Scale

Apache Arrow-native engine queries Iceberg and Parquet directly, no translation layer, format conversion, or performance tax
Apache Arrow Flight and ADBC eliminate serialization overhead for high-throughput data transfer to BI tools, notebooks, and AI clients
Sub-second responses even at petabyte scale, for both analyst queries and high-concurrency agent workloads

Autonomous Reflections

Auto-creates and manages materializations based on observed query patterns over a 7-day window
Optimizer rewrites incoming queries to use pre-computed results transparently, with no manual tuning required
Reflections refresh automatically as base tables update, always optimized to the latest data

Query Federation

Query structured, semi-structured, and unstructured data natively across 35+ enterprise connectors, with VARIANT type and Iceberg V3 support
Optimized pushdowns delegate filters, projections, aggregations, and joins to each source engine
Single SQL interface across relational databases, data warehouses, object storage, and NoSQL systems, with unified access governance for users and AI agents

Iceberg-Native with Full DML Support

Full INSERT, UPDATE, DELETE, MERGE, and TRUNCATE operations on open Apache Iceberg tables
Time-travel queries via AT SNAPSHOT and AT TIMESTAMP syntax for historical analysis
Native support for Apache Polaris and other open catalogs, with no proprietary metastore lock-in

Elastic Scaling and Workload Isolation

Serverless Elastic Engines scale to zero when idle and scale out on demand, so you pay only for active compute
Workload isolation prevents resource contention between analysts, pipelines, and AI agents
Workload management and queue controls deliver predictable SLAs across all query types, from ad hoc exploration to high-concurrency AI agent workloads

COMPARE

How Dremio Stacks Up

See how Dremio's Intelligent Query Engine compares across the capabilities that matter most.

Platform capability comparison
Capability	Dremio	Snowflake	Databricks
Query Engine Architecture	Arrow-native. LLVM codegen. Iceberg-native. No format conversion required.	Proprietary columnar format with query compilation layer.	Photon engine with Delta Lake optimization.
Autonomous Query Acceleration	Reflections auto-optimize queries. ML-driven acceleration without manual tuning.	Materialized views require manual definition.	Delta caching with cluster-level optimization.
Federated Query	Query across any source without data movement. True federation with semantic layer.	Limited federation via external tables.	Unity Catalog with lakehouse federation.
Open Catalog	Native Iceberg, Hive, Delta, Hudi support. Open catalog with no vendor lock-in.	Proprietary catalog with limited export.	Unity Catalog with Delta Lake focus.
Native Iceberg Support	Built for Iceberg from the ground up. Full spec compliance with time travel and schema evolution.	Iceberg tables supported via external tables.	UniForm for Iceberg interoperability.
Query Caching	Multi-layer intelligent caching. Result, metadata, and reflection caching with predictive prefetch.	Result set caching at warehouse level.	Delta cache and Spark cache layers.

Dremio Cloud

Query Engine Architecture Arrow-native. LLVM codegen. Iceberg-native. No format conversion required.
Autonomous Query Acceleration Reflections auto-optimize queries. ML-driven acceleration without manual tuning.
Federated Query Query across any source without data movement. True federation with semantic layer.
Open Catalog Native Iceberg, Hive, Delta, Hudi support. Open catalog with no vendor lock-in.
Native Iceberg Support Built for Iceberg from the ground up. Full spec compliance with time travel and schema evolution.
Query Caching Multi-layer intelligent caching. Result, metadata, and reflection caching with predictive prefetch.

FAQs

Intelligent Query Engine FAQs

Get common questions answered about Dremio's query engine performance, open architecture, and federated data access.

Dremio’s query engine combines LLVM-compiled vectorized execution with an autonomous acceleration layer called Reflections. The optimizer analyzes incoming queries and automatically rewrites them to use pre-materialized results, without any manual tuning from engineers or analysts. In Cloud, Autonomous Reflections continuously monitor query patterns over a 7-day window and create, refresh, or retire accelerators on their own.

RESOURCES

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