CL-320 Cloud & Platform

Data Quality & Pipeline Observability

Make data trustworthy by engineering for it rather than apologising for it: contracts stating what a consumer may rely on, checks that catch freshness, volume, schema and distribution breaks, and a response path that names who is paged and when. Learners instrument a pipeline with executable expectations, trace an upstream failure to the affected dashboards through lineage, quarantine and replay bad records, and publish a trust scorecard for a dataset.

B4Professional

6modules

42skill atoms

2role journeys

Curriculum

What this course covers.

6 modules, 42 named skill atoms. Expand any module to see them.

1Contracts and Expectations7 skill atoms

a data contract between producer and consumerschema, semantics and SLA in one artefactexpectations expressed as executable checksGreat Expectations and Soda suitescontract versioning and breaking change policyproducer tests running in the producer pipelinea contract with no owner as decoration

2The Four Check Families7 skill atoms

freshness against an expected arrival windowvolume checks with row count boundsschema drift from added, dropped and retyped columnsdistribution checks on nulls, uniqueness and rangesreferential integrity across tablesthresholds derived from history not guessedcheck cost on every partition

3Anomaly Detection on Pipelines7 skill atoms

seasonality across daily and weekly volumerolling median and MAD against static thresholdssegment anomalies hidden inside a healthy totaltraining windows polluted by past incidentsprecision and recall of an alerting rulesuppression for known-noisy tableswhen a static rule beats a model

4Lineage and Impact Analysis7 skill atoms

column-level lineage parsed from SQLOpenLineage events emitted by jobsupstream failure traced to affected dashboardsblast radius sized before a schema changeorphaned tables and unused models foundlineage gaps between toolsimpact notice sent to consumers automatically

5Quarantine, Replay and Recovery7 skill atoms

circuit breaker halting a run before publishquarantine tables for rejected recordspartial publish against blocking the whole runreplay from source after the fix landsidempotent reprocessing of a corrected windowbackfill announced to downstream consumersrestating a published metric honestly

6Ownership, Paging and Trust7 skill atoms

severity tiers mapped to response expectationwhat pages now and what waits until morningdata on-call rota and escalation pathincident review written for data incidentstime to detection and time to resolution trackedconsumer-reported against detected issuesa quality scorecard published per dataset

Where it fits

CL-320 in the role journeys.

This course appears in 2 of our 45 role journeys. Here is what a learner takes immediately before and after it in each.

Data Platform Engineer

Professional stage

CL-227→CL-320→CL-222

Data Engineer

Professional stage

CL-317→CL-320→CL-227

Roles this course serves

Data Engineer Data Platform Engineer

The capability ladder

This course is authored to band B4.

Every course we run is written to one rung of the CASI ladder, so a plan can be assembled to take a team from where they are to where they need to be.

What do B1–B6 mean?The CASI Capability Ladder — click to expand

Every course targets a band on the CASI Capability Ladder — our six-band proficiency scale, anchored to open standards (O*NET, ESCO, NICE, NIST AI RMF, Bloom's). A band tells you how deep a course goes, and what evidence proves it.

What the learner can doTypical evidence

AwareUnderstands concepts and vocabulary; uses tools with guidance

Knowledge checks

FoundationPerforms standard tasks correctly in familiar contexts

Guided labs, autograded exercises

PractitionerDelivers complete pieces of work independently

Scenario labs, proctored hands-on exams

ProfessionalHandles production-grade complexity, trade-offs and failure modes

Break-fix drills, design defenses

AdvancedEngineers systems end-to-end under constraints; leads others

Rubric-scored capstones, vivas

ExpertSets direction; recognised authority across teams

Portfolio + panel evaluation

A note on B6. Courses in this catalog target B1–B5. B6 is not taught — it is recognised, through a portfolio and a panel, once someone is setting direction for others. Every journey here is built to land a learner at B5.

Other Cloud courses at this level.

CL-202

Run CL-320 for your team.

This course runs at several lengths depending on how deep you need to go and how much of it your people already have. Tell us who is being trained and we will scope it.

Add it to a training plan Talk to our team Check your team’s level free

Data Quality & Pipeline Observability

What this course covers.

CL-320 in the role journeys.

Data Platform Engineer

Data Engineer

Roles this course serves

This course is authored to band B4.

Other Cloud courses at this level.

AWS Solutions Architecture

AWS Data Engineering

Azure Solutions Architecture

Azure Data Engineering

Google Cloud Data Engineering Bootcamp

GCP Architecture & Operations

Run CL-320 for your team.