Predictable Analytics for the Agentic Workforce
EDB Postgres AI + RAPIDS Accelerator for Apache Spark: High-performance, GPU-accelerated analytics with Apache Iceberg integration.
The challenge: The agentic scaling wall
AI agents generate volatile, non-linear queries that bypass traditional database optimizations.
- Failed queries: 15% of standard Postgres queries timed out at 3TB.
- Manual limits: Index tuning only provided 10% speedup while slowing other operations by 29x.
- ETL latency: Data shuffling stalls real-time decision-making.
The solution: 100% completion at scale
99x
Faster analytics
Spark-RAPIDS vs. tuned Postgres at 1TB scale.
14.8x
Hardware ROI
Speedup on NVIDIA RTX PRO™ 6000 Blackwell Server Edition vs. CPU-only Spark at 3TB.
100%
Query completion
Successful execution rate where standard Postgres timed out.
100x
Data scalability
Capacity increase for analytics without sacrificing performance.
Validated performance (Jan 2026)
| Scale Factor | vs. Tuned Postgres | vs. Spark CPU | Median Speedup |
|---|---|---|---|
| 1TB | 99x faster | 3.9x | 14.2x |
| 3TB | >53x faster* | 7.8x | 33.2x |
| 10TB | Postgres timed out | 7.1x faster | N/A |
*Note: 53x is an understatement; 15% of standard Postgres queries failed to complete within the 2-hour test limit.
On PostgreSQL, several queries do not complete within the allotted two-2 hour timeout, and that number grows with the scale factor.