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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 method for this acceration is Dremio's patterned materialization and query-rewriting, known as Reflections.

  • Autonomous Reflections – Learn how Dremio automatically learns your query patterns and manages Reflections to optimize performance accordingly. This capability is available for Iceberg tables, UniForm tables, Parquet datasets, and any views built on these datasets.

  • Manual Reflections – Use this option primarily for data formats not supported by Autonomous Reflections. Learn how to define your own Reflections and the best practices for using and managing them.

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