Efficient Entity Resolution: Splink Indexing and Blocking in Python

As datasets grow, comparing every single record for duplicates becomes computationally impossible. To solve this, data professionals use entity resolution and record linkage to group matching records efficiently. This course teaches you how to drastically reduce comparison counts and optimize your record linkage pipelines. You will learn to configure Splink, write SQL blocking rules, and implement indexing strategies to handle large datasets without exhausting your system's resources. What you'll learn: - Understand the fundamental concepts of entity resolution, record linkage, and the cartesian product problem. - Configure Splink using Python to prepare and structure your datasets for deduplication. - Write effective SQL blocking rules to filter out unnecessary record comparisons. - Apply advanced indexing techniques to balance linkage accuracy with computational performance. - Analyze blocking performance metrics to ensure no true matches are missed. - Integrate modern database backends like DuckDB with Splink for fast, local data processing. You will start with core terminology and the theory of record linkage before moving into practical configuration. Through written explanations, code walkthroughs, and practical exercises, you will learn to write, test, and refine blocking rules step by step. This course is designed for beginner data analysts, data engineers, and Python developers. No prior experience with entity resolution or Splink is required. Start optimizing your data matching workflows today.