Entity Resolution on Snowflake with dbt and Python

In modern data platforms, messy and duplicate data can lead to poor business decisions and inaccurate reporting. Resolving identical real-world entities across disparate data sources is a critical skill for any data professional. This text-based course guides you through building reliable, scalable entity resolution pipelines using Snowflake, dbt, and Python. You will progress from core concepts of data deduplication to deploying production-ready transformation workflows. By the end of this course, you will have a clear, actionable understanding of how to clean, match, and consolidate messy enterprise datasets. What you'll learn: Understand the foundational principles of entity resolution and data matching; Configure dbt projects to orchestrate complex data transformation pipelines; Write Python-based matching algorithms and integrate them with Snowflake; Implement modern data quality checks using dbt testing frameworks; Build scalable deduplication workflows that process large-scale datasets efficiently; Apply best practices for managing data lineage and maintaining clean warehouse schemas. This course begins with essential definitions of entity resolution and matching techniques before moving step-by-step into hands-on pipeline construction using dbt and Python models in Snowflake. This course is designed for beginners to data engineering and analytics engineering who have a basic familiarity with SQL and want to learn modern data cleaning workflows. Start reading today to master the art of data deduplication and build trusted data pipelines.