Learn
expectations
2 registry resources · 1 tutorial, 1 reference · roadmap: Data Quality
What this topic covers
DE//AGENT currently lists 2 resources under expectations: 1 tutorial, 1 reference. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.
What you can learn here
Documented learning objectives across these resources:
- Read a failing expectation precisely — column, rule, value — via Respond to a failing quality expectation
- Quantify a failure — how many rows and which ones — via Respond to a failing quality expectation
- Decide block vs quarantine vs pass-with-warning on evidence — via Respond to a failing quality expectation
Where this fits in the learning roadmap
These resources map to stage: Data Quality (2). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.
Compare the registry's options
Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.
| Resource | Type | Difficulty | Verification | Score |
|---|---|---|---|---|
| Respond to a failing quality expectation | Tutorial | beginner | community | 34.15 |
| Great Expectations Documentation | Reference | — | community | 26.7 |
How scores are computed: source quality score.
Where to start
Highest composite score first:
- Respond to a failing quality expectation score 34.15 — beginner
- Great Expectations Documentation score 26.7 — difficulty not documented
Scores rank provenance, completeness and trust — read how verification works before adopting anything.
Good fits
Start here if you're new: Respond to a failing quality expectation.
Production use cases
- Respond to a failing quality expectation — A quality expectation failed in production and bad data may be reaching consumers. Follow the data-quality-failure runbook: read the failure precisely, quantify it, then make the o… details
- Great Expectations Documentation — Great Expectations documentation — expectations (expectations, suites, checkpoints) as a YAML DSL for data quality, plus the data-doc approach that connects quality rules to docume… details
Further study
Adjacent topics: data-quality (2 shared).
Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.
Full resource list
| Resource | Type | Score |
|---|---|---|
| Respond to a failing quality expectation | Tutorial | 34.15 |
| Great Expectations Documentation | Reference | 26.7 |