Vector Search in NoSQL with MongoDB and Cassandra

In the era of generative AI, traditional keyword search is no longer enough to meet user expectations for intelligent data retrieval. This course teaches you how to leverage NoSQL databases to handle high-dimensional vector data, enabling semantic search and more relevant results. You will gain a solid foundation in how vector search works and how to implement it within established database architectures. By the end of this course, you will be able to integrate vector search capabilities into your data workflows to support modern applications. What you'll learn: - Understand the foundational concepts of vector embeddings and similarity metrics. - Configure MongoDB for efficient vector storage and indexing. - Implement vector search queries and operations in Cassandra. - Apply Retrieval-Augmented Generation (RAG) patterns to improve search accuracy. - Practice optimizing vector indexes for performance and scalability. - Master the integration of vector data within existing NoSQL structures. The course begins with essential terminology and the theory of vector spaces before guiding you through the specific implementation details for both MongoDB and Cassandra. It is designed for beginners and developers looking to expand their database skills into the world of AI, with no prior machine learning expertise required. Start your journey into the future of intelligent data retrieval today.