Real ChatGPT answer · index seedxUz2YjA3MhccompleteB2B software · data and analytics platformscommercial
We keep getting bad data in our production reports. What is the step by step process for implementing Great Expectations across our data lakes?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We keep getting bad data in our production reports. What is the step by step process for implementing Great Expectations across our data lakes?
2 real searches · 1 real-search pass
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Great Expectations GX documentation current 1.x checkpoints data sources expectations validation definitions data lake Spark Google ↗
Great Expectations documentation deployment production data quality validation actions checkpoints Data Docs Google ↗
- GX Core overview · docs.greatexpectations.io
- Create a Data Context · docs.greatexpectations.io
- Get started with Great Expectations and Databricks · docs.greatexpectations.io
- Get started with Great Expectations and SQL · docs.greatexpectations.io
- Trigger actions based on results · docs.greatexpectations.io
- Data Validation workflow · docs.greatexpectations.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Great Expectations GX documentation current 1.x checkpoints data sources expectations validation definitions data lake Spark OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Great Expectations documentation deployment production data quality validation actions checkpoints Data Docs OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |