Data Quality Frameworks

Data quality is the foundation of trustworthy analytics and machine learning. This skill helps you set up automated checks to catch missing values, duplicates, and incorrect data before they cause problems. You will learn to use popular tools like Great Expectations and dbt to build validation rules that run on every new batch of data.

Great Expectations lets you define expectations for your data, such as 'order_id must be unique' or 'age must be between 0 and 120'. dbt tests add similar checks inside your transformation workflows. Data contracts help different teams agree on what clean data looks like. Together they create a safety net that keeps your pipelines reliable.

Whether you are a data engineer, analyst, or scientist, this skill gives you practical patterns to monitor data quality continuously. You will see real code examples and learn how to fail a pipeline when quality drops. The result is less time debugging bad data and more confidence in your reports and models.