From 764b53722c31cbbbfecdfbc59197e83a2711e482 Mon Sep 17 00:00:00 2001 From: Sabrina Aquino <77522207+sabrinaaquino@users.noreply.github.com> Date: Thu, 25 Jan 2024 12:37:49 -0300 Subject: [PATCH] Update qdrant-landing/content/blog/what-is-a-vector-database.md Co-authored-by: Mike Jang --- qdrant-landing/content/blog/what-is-a-vector-database.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/what-is-a-vector-database.md b/qdrant-landing/content/blog/what-is-a-vector-database.md index 3be35dc46..f04a2364f 100644 --- a/qdrant-landing/content/blog/what-is-a-vector-database.md +++ b/qdrant-landing/content/blog/what-is-a-vector-database.md @@ -141,7 +141,7 @@ Once the closest vectors are identified at the bottom layer, these points transl Vector databases often deal with datasets that comprise billions of high-dimensional vectors. This data isn't just large in volume but also complex in nature, requiring more computing power and memory to process. Scalable systems can handle this increased complexity without performance degradation. This is achieved through a combination of a **distributed architecture**,** dynamic resource allocation**, **data partitioning**,** load balancing**, and **optimization techniques**. -Systems like Qdrant, developed in Rust, exemplify scalability in vector databases by leveraging Rust's efficiency in **memory management** and **performance**, allowing handling of large-scale data with optimized resource usage. +Systems like Qdrant exemplify scalability in vector databases. It leverages Rust's efficiency in **memory management** and **performance**, which allows handling of large-scale data with optimized resource usage. ### Efficient Query Processing