Entity Resolution: Combining Indexing Methods for Scalable Data Matching

Managing duplicate records across massive datasets is a critical challenge in modern data engineering. Standard database queries often fail to catch subtle variations in names, addresses, and identifiers, leading to poor data quality and skewed analytics. This text-based course guides you through the fundamentals of entity resolution and data deduplication. You will learn how to design, implement, and combine multiple indexing methods—such as equality and sort-based blocking—to dramatically reduce computational complexity while maintaining high matching accuracy. What you'll learn: 1. Understand the core concepts of entity resolution, record linkage, and data deduplication. 2. Implement standard equality blocking to group similar records efficiently. 3. Apply sorted neighborhood methods to handle typographical errors and formatting variations. 4. Combine multiple indexing strategies to balance precision and recall in large datasets. 5. Evaluate the performance of your entity resolution pipelines using standard metrics. 6. Explore modern vector-based blocking techniques for handling unstructured text data. The course begins with essential definitions and foundational data-matching concepts before moving step-by-step through practical indexing algorithms. You will read clear explanations, analyze structured code examples, and practice combining methods to solve complex data-cleansing scenarios. This course is designed for beginner data analysts, database administrators, and junior data engineers looking to improve data quality. No prior experience with entity resolution is required, though a basic understanding of programming logic and database concepts is helpful. Start mastering entity resolution today and build cleaner, more reliable datasets.