Learn
observability
7 registry resources · 6 tutorials, 1 hook · roadmap: Observability
What this topic covers
DE//AGENT currently lists 7 resources under observability: 6 tutorials, 1 hook. 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:
- Declare a freshness SLA and compute worst-case lag from a schedule — via Express a freshness check as a metric and an alert
- Express staleness as a metric and write an alerting rule — via Express a freshness check as a metric and an alert
- Configure no-data handling so silence is an alert, not a pass — via Express a freshness check as a metric and an alert
- Separate infrastructure metrics from data signals — freshness, volume, schema, distribution — via Hold the observability mental model: infrastructure metrics versus data signals
- Place lineage and the catalog on the observability map, next to metrics and traces — via Hold the observability mental model: infrastructure metrics versus data signals
- State what each layer answers and which tool owns it — via Hold the observability mental model: infrastructure metrics versus data signals
- Assemble the full stack — metrics, alerts, traces, lineage, catalog — via Capstone: the data-platform observability checklist
- Gate schedule and column changes pre-merge with SLA and impact checks — via Capstone: the data-platform observability checklist
10 more objectives live on the individual resource pages.
Where this fits in the learning roadmap
These resources map to stage: Observability (7). 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 |
|---|---|---|---|---|
| Express a freshness check as a metric and an alert | Tutorial | beginner | community | 34.15 |
| Hold the observability mental model: infrastructure metrics versus data signals | Tutorial | beginner | community | 34.15 |
| Capstone: the data-platform observability checklist | Tutorial | intermediate | community | 34.15 |
| Instrument pipelines with OpenTelemetry: traces for data jobs | Tutorial | intermediate | community | 34.15 |
| Incident drill: a broken upstream caught by freshness and lineage | Tutorial | intermediate | community flags | 31.15 |
| Production gotchas: alert fatigue, missing contracts, no-data silence | Tutorial | intermediate | community flags | 31.15 |
| Freshness Pre-Merge Check | Hook | intermediate | community flags | 31.1 |
How scores are computed: source quality score.
Where to start
Highest composite score first:
- Express a freshness check as a metric and an alert score 34.15 — beginner
- Hold the observability mental model: infrastructure metrics versus data signals score 34.15 — beginner
- Capstone: the data-platform observability checklist score 34.15 — intermediate
Scores rank provenance, completeness and trust — read how verification works before adopting anything.
Good fits
Cautions and limitations
Security-flagged entries: Incident drill: a broken upstream caught by freshness and lineage (2 flags), Production gotchas: alert fatigue, missing contracts, no-data silence (2 flags), Freshness Pre-Merge Check (2 flags). Flags mean the record declares capabilities like credential use or write access — read the security posture on each page before connecting.
Production use cases
- Express a freshness check as a metric and an alert — The first hands-on of the series: declare a freshness SLA, express staleness as a metric, and write an alert rule — including what happens when no data arrives at all. Ends with th… details
- Hold the observability mental model: infrastructure metrics versus data signals — Two layers live on one data platform: infrastructure metrics (CPU, lag, job success) and data signals (freshness, volume, schema, distribution). This lesson separates them, places … details
- Capstone: the data-platform observability checklist — Ten lessons compress into one page. Declare SLAs, express them as metrics and alerts with explicit no-data handling, instrument runs with traces, emit lineage events, keep the cata… details
- Instrument pipelines with OpenTelemetry: traces for data jobs — Traces give every pipeline run a story — which task ran, how long, with what attributes. Wrap a job with OpenTelemetry, adopt the semantic conventions so attribute names match acro… details
Further study
Adjacent topics: tutorial (6 shared), sla (3 shared), freshness (3 shared), production (2 shared), prometheus (2 shared), grafana (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 |
|---|---|---|
| Express a freshness check as a metric and an alert | Tutorial | 34.15 |
| Hold the observability mental model: infrastructure metrics versus data signals | Tutorial | 34.15 |
| Capstone: the data-platform observability checklist | Tutorial | 34.15 |
| Instrument pipelines with OpenTelemetry: traces for data jobs | Tutorial | 34.15 |
| Incident drill: a broken upstream caught by freshness and lineage | Tutorial | 31.15 |
| Production gotchas: alert fatigue, missing contracts, no-data silence | Tutorial | 31.15 |
| Freshness Pre-Merge Check | Hook | 31.1 |