The Intelligent
Lakehouse Platform
for Agentic AI

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.

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.

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.

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.

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.

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.

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.

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.