Autonomous resource optimization is the practice of letting a data platform tune its own performance-related resources, such as acceleration structures, table file layout, and compute capacity, based on observed workload and data changes instead of manual configuration by engineers. Rather than an administrator deciding which aggregates to precompute, when to compact files, or how to cluster a table, the platform watches query patterns and table health and acts on them within limits the team sets. Dremio’s autonomous features are a well-documented example, but the idea applies to any system that closes the loop between monitoring and tuning.
How Autonomous Resource Optimization Works
Most implementations follow the same cycle:
- Observe. Collect signals such as query history, scan volumes, file counts and sizes, delete file buildup, and engine utilization.
- Decide. Score candidate actions, for example which precomputed aggregation would serve the most recent queries, or which tables have drifted from a target file size.
- Act. Create, refresh, or drop acceleration structures, rewrite files, or adjust where work runs, usually on dedicated compute so maintenance does not compete with user queries.
- Re-evaluate. Measure whether the change helped and reverse it if it no longer earns its cost.
Examples in Dremio
Dremio’s documentation describes two concrete features that fit this pattern.
Autonomous Reflections. Reflections are precomputed, optimized copies of data that Dremio’s optimizer can substitute into queries. With Autonomous Reflections turned on, Dremio creates and drops Reflections on Iceberg tables, Parquet datasets, and views built on them, based on query patterns from the last seven days. The docs list guardrails: up to 100 Reflections, at most 10 created per day, low-scoring Reflections disabled for seven days before they are dropped, and a Reflection is only used when it is fully in sync with its source. Iceberg-backed Reflections refresh when the underlying table changes.
Automatic table optimization. For Iceberg tables in its catalog, Dremio Cloud runs maintenance on a dedicated engine. Its documentation lists five operations: clustering, data file compaction, partition evolution, manifest file rewriting, and handling of position delete files. Compaction targets 256 MB files, and clustering uses clustering keys that the table owner defines with CREATE TABLE or ALTER TABLE.
Autonomous Versus Manual Tuning
| Task | Manual approach | Autonomous approach |
|---|---|---|
| Query acceleration | Engineers design aggregates or materialized views from guesses about workload | Platform proposes and retires structures from actual query history |
| File layout | Scheduled OPTIMIZE or compaction jobs per table | Platform detects small files and delete buildup and rewrites as needed |
| Clustering | Periodic sort or Z-order rewrites | Incremental clustering toward a target depth using declared keys |
| Oversight | Runbooks and tickets | Guardrails, history logs, and admin overrides |
Autonomy does not remove the need for judgment. Teams still choose clustering keys, decide which tables are in scope, size the engines that run maintenance, and review history logs to confirm actions match their cost and freshness goals.
Why It Matters in the Lakehouse
An open lakehouse built on Apache Iceberg puts table maintenance in the hands of whoever runs the platform. Streaming writes and frequent updates create small files and delete files that slow down engines such as Dremio, Trino, and Spark over time, and workloads from AI agents tend to be less predictable than scheduled dashboards. Autonomous optimization keeps tables and acceleration aligned with what users actually run, so performance does not depend on someone noticing a slow dashboard first.
Related Reading
- When the Query Optimizer Starts Managing Its Own Materializations
- Autonomous Table Optimization When Your Query Workload Stops Being Predictable
- Data compaction
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.