Autonomous Reflections

10-100x faster queries for every AI and BI workload

A Paradigm Shift for Performance Management

Autonomous Reflections are intelligently created and updated materializations based on query patterns that act like a persistent, always-fresh cache—automatically optimizing performance while ensuring queries run on live, up-to-date data with no SQL changes.

Performance in The AI Era

Challenge

In the BI era, maintaining sub-second queries across dashboards was difficult and typically resulted in the development of multi-step precomputation pipelines and extracts manageable but inconvenient.

However, in the AI era, this approach fundamentally breaks down. AI requires live data across unified semantic models at a scale and complexity that manual optimization cannot address. With both humans and AI agents demanding sub-second access to enterprise data, intelligent automation has become an essential requirement rather than just a nice-to-have feature.

Solution

Enter Autonomous Reflections.

Autonomous Reflections analyze query patterns, determines what materializations will optimize performance, and then creates, updates, and deletes these materializations to maintain an optimized environment. SQL queries are automatically rewritten at runtime to take advantage of the materializations, so queries don’t have to be rewritten.

No Manual Performance Tuning.
Sub-Second Queries, Every Time.

Autonomous Reflections eliminates time-consuming Zero Manual performance tuning, delivers sub-second queries for all AI and BI workloads, and significantly reduces compute costs.

• Zero Manual Tuning: Free your data team to focus on innovation instead of maintenance
• Consistent High Performance: Sub-second response times for both AI and analytics workload.
• Cost Efficiency: Lower resource utilization with intelligent
optimizations and automatic scaling

How Autonomous Reflections Work

Reflections are easy to configure and monitor through a centralized interface that identifies incremental data changes, updates only affected views, and provides clear insights into usage patterns.

Smart Query Acceleration: Automatically identifies optimization opportunities and rewrites query plans
Two Reflection Types: Raw reflections for data reorganization and Aggregation reflections for precomputed metrics
Zero-Config Operation: Creates and maintains optimal reflections without manual intervention

Adaptive Learning: Continuously evolves as query patterns and data change
Real-Time Sync: Ensures data freshness with Iceberg tables and Parquet metadata
Intelligent Monitoring: Track which queries leverage Reflections, monitor refresh frequency, and measure performance gains