The Intelligent
Lakehouse Platform
for Agentic AI Iceberg & Polaris Unifying Data Hybrid & Multi-Cloud Data Warehousing

From the leaders of Apache Polaris, Arrow, and Iceberg

Fastest Lakehouse
Autonomous Optimization
Built for Agentic AI

Dremio is trusted by thousands of companies

Why Dremio?

From the company behind Apache Arrow, Iceberg, and Polaris.

Fastest Lakehouse

Autonomous Optimization

Built for Agentic AI

20X faster than any other lakehouse

Apache Arrow powered columnar processing for lightning-fast performance
Autonomous management continuously analyzes table metadata and query patterns to create intelligent data structures automatically
Transparent query rewriting automatically optimizes queries without changing your SQL or applications
Iceberg clustering intelligently organizes data files for faster queries without manual partitioning—optimizing storage efficiency automatically
End-to-end automatic caching for data, metadata, query plans, and query results

Read the TPC-DS Benchmark Blog Post

No manual effort to optimize performance

Fully autonomous optimization that learns from your workloads
Zero manual partitioning or data pipeline maintenance
Intelligent query acceleration that automatically optimizes performance based on usage patterns
Hands-free performance that adapts as your data evolves

Learn more about autonomous materializations

Best-in-class Agentic Experience (AX)

MCP connection for seamless AI agent connectivity
Semantic layer (context layer) enables AI agents to understand the meaning of the data
Semantic search empowers AI agents to find the data they need
Data federation ensures AI agents can access all your company data, across cloud and local environments
Autonomous performance gives AI agents optimal speed with data access

Leaders In Open Source

The Lakehouse
OSS Standards

Metadata Catalog

Apache Polaris

Dremio co-created

Lakehouse metadata catalog

Apache Polaris: The Definitive Guide

In-Memory / Transport Format

Apache Arrow

Dremio co-created

Lakehouse in-memory and transport format (100M+ downloads/month)

Table Format

Apache Iceberg

Dremio key contributor and leading educator

Lakehouse table format

Apache Iceberg: The Definitive Guide #1 in Iceberg Education. Take the Course

The Intelligent Lakehouse Platform

Dremio provides the AI-enabled semantic layer, intelligent query engine, and open metadata catalog
required to win in the AI era.

Discover the platform

1000s of companies across all industries trust Dremio

Amazon Accelerates Supply Chain Decision Making by Implementing an Innovative Analytics Architecture using Dremio

10x

in query performance, from 60 to 4-6 seconds

60HRS

of work eliminated per project

90%

Reduction in setup time.

Empowering our customers with robust data governance on STACKIT is crucial. Dremio Catalog provides a secure central platform to manage data and ensures full data sovereignty within the trusted STACKIT Cloud

Benjamin Schweizer Senior Manager Domain Product Owner STACKIT

ABC Supply delivers self-service analytics using Dremio to organize domain data and accelerate development

Stephen Brehm Manager of Business Intelligence ABC Supply

Increased

independence for BI team by eliminating data bottlenecks

Optimized

Snowflake performance while maintaining ~9,400 Dremio jobs/day

Provided easy

and fast access to 70+ data sources for 1,200 daily BI users

Maersk built their next generation data platform for unified analytics on Dremio making it the go-to place for end-user SQL queries in only six months

99.97%

uptime, minimizing downtime to just 3 hours in 10 months

Scaled

from handling zero to 1.6 million queries a day

Reduced costs

by avoiding proprietary data access fees and optimizing resource usage

Québec Blue Cross streamlined data product delivery process with full CI/CD by leveraging Dremio with DBT

6X

growth in physical data sets validated and managed

140%

increase in virtual data sets identified and validated

Reduced

Databricks cost while scaling data projects to users.

Nutanix fast tracked data provision to key business verticals by building Data-as-a-Service platform with Dremio

45 million+

Milli-second latency on queries

Unified Access

to data through a single integrated platform

Modernize Your Data Infrastructure:
Real-World Use Cases

Agentic AI

Enable AI agents with direct access to enterprise data through natural language. MCP Server provides zero-integration connectivity to LLMs and AI frameworks.

Unify Data & Analytics

Eliminate data silos with a unified semantic layer and query federation that provides consistent definitions, metrics, and business logic across all your data sources.

Warehouse to Lakehouse

Get faster performance, more flexibility, and lower management overhead than traditional warehouses. The best warehouse is an Iceberg Lakehouse.

Lake to Iceberg Lakehouse

Connect on-premises and cloud data lakes into a unified lakehouse architecture through advanced query federation and virtual data integration - no ETL required.

Hybrid Lakehouse

Connect on-premises and cloud data lakes into a unified lakehouse architecture through advanced query federation and virtual data integration - no ETL required.

Data Fabric

Connect disparate data sources across hybrid and multi-cloud environments with a unified architecture that enables consistent governance, discovery, and access controls.

Solutions by Industry

Financial Services

Manufacturing

Technology

Life Sciences & Healthcare

Retail & Consumer Products

Choose your deployment

Find the right deployment of Dremio that fits your lakehouse needs.

Dremio Cloud

A fully managed Dremio Lakehouse Platform available in AWS and Azure

Dremio Enterprise

Scalable, secure, self-managed software available on Kubernetes, on-premises or in the cloud

Dremio Community Edition

Available on Docker & Kubernetes for local machines or servers. Free with limited feature set

Our Community

Antonio Vieira Santos

@antoniovieirasantos

From empowering enterprises to democratize data access to supporting AI-driven innovation, Dremio truly stands out as a game-changer in the industry. I especially loved hearing...

Raghuram Reddy Peddakapula

Today, I explored how Apache Iceberg handles data changes like updates and merges, manages snapshots, and optimizes data access with partitioning, all without rewriting entire...

Skander Ben Miled, MBA

@skander-ben-miled-mba-🟣🟢-025654130

Thank you, Dremio, for an evening full of great conversations, delicious food, and amazing company! 🙌🍷

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Frequently Asked Questions

01 How does "autonomous performance" actually work? + −

Dremio continuously analyzes query patterns and automatically creates Autonomous Reflections (smart materializations), applies Iceberg Clustering (intelligent data layout), and optimizes tables—all without human intervention. The system learns and adapts as your workload changes.

02 How does the AI integration work? + −

The MCP Server enables AI agents to discover and use data tools like RunSqlQuery and GetSchemaOfTable automatically. AI-enabled semantic search lets you find data using plain language. No custom integrations required—just natural language access to your enterprise data.

03 Do I need to migrate my data or change my queries? + −

No data movement required. Dremio queries data where it lives using advanced federation. Your existing SQL queries work unchanged—they’re automatically optimized at runtime to take advantage of Reflections and intelligent caching. For Iceberg datasets, the management of Reflection based acceleration will be autonomous.

04 Can I use Dremio with Python? + −

Yes, Dremio can be used with Python. Thanks to Dremio’s support for REST, ODBC, JDBC, and Apache Arrow Flight interfaces, you can connect to Dremio from virtually any programming language, including Python. Dremio also offers Python libraries such as dremio-simple-query and pyDremio, which make it easy to query and analyze data directly from your Python environment.

05 Can I create custom connectors for Dremio? + −

Yes. Dremio provides an ARP (Advanced Relational Pushdown) framework that allows you to build custom connectors. The community has already created connectors for many data sources using this framework. However, when possible, we recommend ingesting data into Apache Iceberg tables instead. This allows you to take full advantage of Dremio’s Autonomous Performance features, including acceleration, optimization, and caching.

06 What does "zero data partitions" mean? + −

Traditional systems require manual partitioning schemes that become maintenance nightmares. Dremio’s Iceberg Clustering automatically organizes data for optimal performance without any manual partition management. You get better performance with zero partitioning overhead.

07 What about our existing BI tools and workflows? + −

All your current tools work exactly the same. Dremio provides one-click integrations with Power BI, Tableau, and other BI platforms. Users see faster dashboards and queries without changing anything in their workflow.

08 Can Iceberg tables managed by the Dremio Catalog be used with Spark, Flink, and other tools? + −

Yes. The Dremio Catalog is built on Apache Polaris, which implements the Apache Iceberg REST Catalog specification. This means any tool that supports the Iceberg REST catalog—such as Spark, Flink, and others—can read from and write to tables managed by the Dremio Catalog. Likewise, Dremio can connect to external Iceberg catalogs using its REST Catalog connector.

09 Can I optimize Iceberg tables from non-Dremio catalogs using Dremio? + −

Yes. Dremio’s OPTIMIZE and VACUUM commands can be used on any Iceberg table that Dremio has write access to, regardless of which catalog it resides in. However, automatic optimization features are currently only available for Iceberg tables managed within the Dremio Catalog.

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