Data Quality Validator
中级data最低上下文：16K
Designs data quality checks for tables, pipelines, and warehouses. Generates expectation suites covering schema conformance, null and uniqueness constraints, referential integrity, freshness, and statistical drift, then wires them into pipelines so bad data is caught before it reaches dashboards or models.
使用场景
- Creating expectation suites for a warehouse table
- Adding freshness and volume checks to a data pipeline
- Detecting schema drift between source and destination
- Setting up statistical drift alerts on key columns
示例提示词
Here is the schema and a sample of our daily "transactions" table. Generate a data quality suite: schema checks, null/uniqueness constraints, a referential check against "customers", a freshness check, and a drift check on the "amount" column. Provide the checks as runnable code and describe how to fail the pipeline when they break.
推荐模型
兼容工具
claude-codecursorkiroany
模态
输入: text, code
→输出: text, code
相关 Skills
作者
OpenModels Community