Data Quality Validator

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

MitteldataMindestens 16K Kontext

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.

data-quality validation data-engineering great-expectations pipelines

Anwendungsfälle

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

Beispiel-Prompt

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.

Empfohlene Modelle

claude-opus-4-8 gpt-5-6-sol deepseek-v4

Kompatible Werkzeuge

claude-codecursorkiroany

Modalitäten

Eingabe: text, code

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Ausgabe: text, code