Agentic Analytics:
The Holy Grail
The problem getting there isn't your AI model. It's your data foundation.
Why AI Assistants Fail
Success depends on three non-negotiable pillars. Most platforms miss all three.
Lack of Context
Your AI doesn't speak your business language. Without a semantic layer, it translates questions into generic SQL, missing specific definitions for "churn" or "revenue".
Data Gravity
Data is everywhere—Postgres, Snowflake, S3. Moving it all to one place is a governance nightmare. Traditional ETL pipelines are too brittle and slow for agentic AI.
Too Slow
Conversations require speed. If your AI takes minutes to answer, the flow breaks. Ad-hoc exploration becomes impossible without sub-second performance.
The Solution: Dremio Agentic Lakehouse
One cohesive platform. No Franken-stack required.
AI Semantic Layer
Teach your AI your business. Map raw tables to business-friendly logic. Enriched with wikis and tags, so your agent understands "active customer" instantly.
- Business Context
- Automatic Labeling
Unified Data Access
Query data where it lives. Federate queries across S3, Snowflake, and Postgres without moving a byte.
Autonomous Performance
Reflections and Caching deliver interactive speed. Reflections are precomputed optimizations that make massive datasets feel instant.
Agentic Interfaces
Built-in AI Agent and Open Source MCP connectivity. Analyze structured and unstructured data together.
Apache Iceberg Native
Built-in Polaris catalog for auto-optimization and governance. Federates queries across AWS Glue, Nessie, Snowflake, and Unity Catalog.
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