In this module, we will explore the foundational principles of vector databases, examining why they have become an essential technology in data systems today. We’ll also compare vector databases with traditional databases, shedding light on their unique features and use cases.

In this module, we will cover the core concepts behind vector databases, such as vectors, embeddings, and high-dimensional spaces. By looking at practical examples, you will gain insights into how vectors and embeddings improve data retrieval and management in vector databases.

In this module, we will focus on the essential concepts of search similarity within vector databases. You’ll explore K-Nearest Neighbors (KNN) and Approximate Nearest Neighbors (ANN), understanding their role in improving search accuracy and efficiency in high-dimensional data spaces.

In this module, we’ll dive into the different indexing strategies for vector databases, explaining how each technique works and its real-world applications. You’ll also learn how to select the most suitable index for your data-driven projects, ensuring optimized search performance.

In this module, we will examine the key players in the vector database landscape, including Pinecone, Qdrant, Milvus, and Weaviate. You’ll gain hands-on experience with each platform, learning their strengths and specific use cases for vector-based applications.

In this module, we will provide live demonstrations of popular vector databases—Pinecone and Weaviate. You’ll see firsthand how these platforms are used in real-world applications and how they manage vector data for efficient search and retrieval.

In this module, we will look ahead to the future of vector databases, discussing how the technology is expected to evolve. You’ll gain insights into upcoming trends and innovations that will influence the development and adoption of vector databases in the coming years.