Generative AI is revolutionizing data and analytics, but its applications demand advanced data management capabilities to handle vast, diverse, and complex datasets that include images, video, audio, documents and text. Traditional databases were originally designed for structured data and exact matches. However, they are proving insufficient for GenAI models, which often operate in high-dimensional spaces and require searching for similarities.
Vector databases are advanced databases designed for optimized storage and retrieval of high-dimensional vector data. They excel in conducting large-scale similarity searches and streamline data management for cutting-edge AI applications. Their key advantage lies in supporting specialized vector indexes, which enable fast query processing and deliver the high performance required for analyzing complex data.
At the upcoming Forrester’s Technology And Innovation Summit, September 9–12, I am excited to present on the topic of vector databases. Data professionals will gain valuable insights into leveraging vector databases to elevate their AI strategy and implement industry best practices. This session will delve into the distinct advantages and practical applications of vector databases, highlighting their pivotal role for organization dedicated to optimizing their AI strategy.
Here’s a preview of some of the content that I’ll talk about in the session:
Distinctive capabilities of vector databases. Unlike traditional databases, vector databases excel in efficiently storing and retrieving complex vector data, which are generated by providers such as OpenAI, Hugging Face, and Cohere. By indexing vectors, they databases enable rapid execution of similarity searches. We will explore their distinct advantages over conventional databases.
Choosing between native and multi-model vector database. Native vector databases are purpose-built to efficiently manage complex, multi-dimensional vector data at scale. On the other hand, multimodal databases are now incorporating vector functionalities, including storage, indexing, and querying capabilities. In my presentation, we will analyze the strengths and limitations of both native vector databases and multimodal databases with vector support.
Exploring diverse use cases for vector databases. As interest in models leveraging complex, high-dimensional data, particularly in Generative AI applications, continues to surge, vector databases are gaining prominence with a multitude of emerging use cases. While Retrieval Augmented Generation (RAG) currently dominates, the landscape is poised to expand into non-RAG applications in the near future. We will explore diverse use case scenarios and unfold forthcoming developments in this evolving market.
Don’t miss out. I’ll be diving into the details at the Technology And Innovation Summit, so check out the agenda and secure your spot!
