explainer
What is an in-memory database?
An in-memory database stores its working data primarily in RAM rather than on disk. That difference is mostly about speed and responsiveness.
Disk-based vs in-memory
Traditional databases store data on disk. In-memory databases keep working data in RAM, so reads and writes can happen much faster.
Speed enables more interaction
Lower latency means it's practical to explore data interactively and to serve workloads where waiting for disk I/O would be a bottleneck.
Why it matters for AI
Some AI and analytics workloads benefit from fast access to large working sets. Speed helps with iteration and with serving timely results.
Why it matters for analytics
Enterprise analytics often needs to scan, aggregate, or join across large datasets quickly. In-memory approaches can keep those queries responsive.
where this fits
AIMDB focuses on the intersection of artificial intelligence and in-memory database technology. If you're curious about getting hands-on with this, check out the joining process or apply.