Enables an AI agent to authenticate with and make curl requests to the Dremio REST API for both Dremio Software and Dremio Cloud.
Enables an AI agent to install, configure, and use the dremio-cli Python tool to manage Dremio Software and Cloud from the command line.
Guides an AI agent through adding data sources to Dremio by asking the user the right questions, recommending connection settings, and linking to the exact documentation for each connector.
Teaches an AI agent how to create, manage, and maintain Apache Iceberg tables in Dremio — including DML, schema evolution, time travel, table maintenance, partitioning, and versioned catalog workflows.
Teaches an AI agent data modeling best practices in a Dremio lakehouse — medallion architecture, views vs tables, reflections strategy, partitioning, dimensional modeling, and semantic layer design.
Enables an AI agent to use dremio-simple-query (lightweight SQL/Arrow Flight) and DremioFrame (full dataframe builder with ingestion, modeling, and admin) to interact with Dremio from Python.
Teaches an AI agent the Dremio SQL dialect — unique syntax, Iceberg DML, reflections DDL, versioned queries, RBAC commands, and links to the full SQL reference docs.