Amazon’s New AI Data Tool Writes 25 Records at Once — and Prevents Loss
Faster AI updates mean quicker fraud detection and fewer lost records.
Amazon SageMaker Feature Store is where companies keep the data that powers their AI models. Until now, adding a piece of data meant one call at a time. If a fraud-detection system needed to update 10,000 records per second across five separate data groups, that meant 50,000 separate requests per second — a huge bottleneck.
Amazon’s new BatchWriteRecord API fixes that. Instead of writing one record per call, it lets you write up to 25 records in a single request, even across different data groups. If one record fails, the others still succeed, so you don’t have to start over. And it keeps the same “newest data wins” rule that prevents older data from overwriting newer data.
The second big update is ListRecords. Before, if you used the In-Memory storage tier and lost your record identifiers, there was no way to find them — they were gone forever. Now you can list and browse the records in a data group, just like looking through a file folder. That gives teams a safety net when bugs or pipeline failures happen.
For everyday users, the impact is indirect but real: faster AI systems, lower data storage costs, and fewer catastrophic failures. For engineers, it means less overhead and less fear of losing valuable data. These tools are available today for anyone using Amazon SageMaker Feature Store.
- Write up to 25 records in a single API call, instead of one at a time — up to 25x fewer requests.
- New ListRecords lets you see what’s stored in the in-memory tier, making lost data recoverable.
- Works across multiple feature groups at once, so high-speed jobs like fraud detection run more smoothly.
Why It Matters
Faster AI development, lower costs, and fewer data disasters for companies relying on machine learning.