How to use dbt-expectations to detect data quality issues

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.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.