How virtual datasets in Dremio's semantic layer let AI agents extend analytics models on demand through an MCP server instead of relying on static models.
Learn how to automate compaction, snapshot expiration, and layout optimization in Apache Iceberg using metadata-driven triggers and orchestration tools.
Learn how to scale Apache Iceberg table optimizations across large datasets using parallelism, checkpointing, and fail recovery to ensure reliability.
The three bottlenecks that stall agentic AI (data access, query speed, and semantic meaning) and how Apache Iceberg and Dremio address each one.
