Data quality issues can disrupt key business operations, often surfacing in broken dashboards and inaccurate KPIs, but the data transformation tool dbt offers built-in testing to mitigate such problems. dbt-expectations, inspired by the Great Expectations library, provides a package of tests that are easier to set up and run faster than their counterparts, as they operate directly within a database. These tests, written in YAML templates with SQL, Jinja, and dbt macros, can be applied to various components of a dbt project, including sources, models, columns, and seeds, to address issues like incorrect data types, stale data, missing data, and non-unique or duplicate values. The package's tests surpass dbt's generic options, such as not_null and unique, by offering more granular checks and the ability to add row conditions. dbt-expectations is particularly useful for verifying column types, ensuring data freshness, and preventing missing data and duplicates, thus providing a comprehensive solution for maintaining data integrity and reliability in analytics workflows.