Optimize Performance

Dremio uses a variety of tools to help you autonomously optimize your lakehouse. These tools apply at four stages: (1) source files, (2) intermediate transformations, (3) final or production transformations, and (4) client queries. Dremio also offers tools that allow you to manually fine-tune performance. Both approaches can coexist, enabling Dremio to manage most optimizations automatically while still giving you the flexibility to take direct action when desired.

For details on how Dremio autonomously manages your tables, see Automatic Optimization, which focuses on Iceberg table management.

This section focuses instead on accelerating views and SQL queries, including those from clients such as AI agents and BI dashboards. The principal mechanism is Reflections: precomputed materializations that Dremio uses to transparently rewrite and accelerate queries without any change to the queries themselves.

• How Reflections Work – Understand the query rewrite model, the difference between raw and aggregate Reflections, and how aggregate Reflections cover a broad family of rollup queries from a single definition.

• Autonomous Reflections – Let Dremio analyze your query patterns and manage Reflections automatically. Available for Iceberg tables, Parquet datasets, and any views built on these datasets.

• Manual Reflections – Define and manage your own Reflections. Use this primarily for data formats not supported by Autonomous Reflections, or when you need direct control.

• Results Cache – Understand how Dremio caches the results of queries from AI agents and BI dashboards.