Vector-Based Entity Resolution with K-Nearest Embeddings

In massive datasets, finding duplicate or related records—known as entity resolution—can be computationally expensive and slow. Traditional string-matching methods often fail to capture semantic meaning and struggle to scale. This text-based course guides you through modern entity resolution using vector embeddings and k-nearest neighbor (k-NN) blocking. You will learn how to represent text data as high-dimensional vectors, store them in LanceDB, and perform highly efficient semantic similarity searches to group duplicate entities. What you'll learn: - Understand the fundamental concepts of entity resolution, blocking, and semantic similarity - Generate high-quality text embeddings to capture the true meaning of your data - Configure and query LanceDB, a modern serverless vector database, for fast similarity search - Apply k-nearest embeddings blocking to dramatically reduce the search space for duplicate detection - Evaluate the accuracy and computational efficiency of your entity resolution pipeline You will start with the core terminology of data matching and vector spaces before moving on to hands-on configuration of vector databases and embedding pipelines. The course wraps up with practical written exercises to help you implement and refine your own entity resolution workflows. This course is designed for beginner data analysts, software developers, and database engineers who want to learn modern data deduplication techniques. No prior experience with vector databases is required. Start reading today to unlock faster, smarter data matching workflows.