What is the best way to implement automated data quality checks as part of our data governance rollout required by management today?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to implement automated data quality checks as part of our data governance rollout required by management today?
1 real search · 1 real-search pass
official documentation dbt data tests unique not_null relationships accepted_values freshness; Great Expectations validation checkpoints data quality documentation; Microsoft Purview data quality rules governance documentationGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
official documentation dbt data tests unique not_null relationships accepted_values freshness; Great Expectations validation checkpoints data quality documentation; Microsoft Purview data quality rules governance documentation Google ↗
- Add data tests to your DAG · docs.getdbt.com
- About data tests property · docs.getdbt.com
- Writing custom generic data tests · docs.getdbt.com
Queries
1 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
official documentation dbt data tests unique not_null relationships accepted_values freshness; Great Expectations validation checkpoints data quality documentation; Microsoft Purview data quality rules governance documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |