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Deliverable guide
Data Model and Schema Design Source-Aware Guide
Defines source-aware business process, grain, entity, fact, dimension, key, relationship, constraint, history, naming, lineage, privacy, and migration design review, evidence handling, and action boundaries.
Read the fileOverview
Data Model and Schema Design Overview
Source-aware deliverable bundle for business process, grain, entity, fact, dimension, key, relationship, constraint, history, naming, lineage, privacy, and migration design review, evidence reconciliation.
Read the fileWorkflow
Data Model and Schema Design Source-Aware Triage
1. State the decision and direct answer possible now. 2. Record Verified, Provided, Assumed, and Needs verification separately. 3.
Read the fileQuality rubric
Data Model and Schema Design Source-Awareness Check
A passing response contains all five required sections, names a source URL, preserves prompt evidence, avoids invented provenance and reviewer ownership, and names these prohibited actions.
Read the fileIs this bundle right for your task?
Who it is for
- Data architects, analytics and data engineers, database designers, application teams, governance owners, and reviewers preparing schema decisions
When to use it
- A new analytical or operational model needs its business process, declared grain, entities, identifiers, facts, dimensions, and aggregation rules defined.
- Relationships, cardinality, keys, nulls, constraints, history, naming, types, lineage, privacy, retention, or access decisions need source evidence reconciled.
- A schema migration needs compatibility, performance, quality tests, ownership, rollback, and downstream impact reviewed before implementation.
What you need to provide
- Business questions and process scope, source systems and contracts, representative profiles and quality evidence, entity and identifier definitions, declared grain, fact and aggregation rules, dimension and history requirements, relationships, and cardinality.
- Null and constraint policy, naming and data types, privacy classification and retention, access and lineage, target platform, performance constraints, migration and compatibility requirements, tests, owners, rollback, and approvals.
Tasks and expected outputs
Questions it helps answer
- Prepare a data model and schema design review brief without fabricating local facts.
- Separate verified, provided, assumed, and missing evidence.
- Produce a review-ready decision with explicit verification and approval boundaries.
What it helps produce
- Data Model and Schema Design review brief
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Load the Data Model and Schema Design bundle and provide the business questions, process scope, source contracts and sample profiles, entity and identifier definitions, proposed grain, facts and aggregation rules, dimensions and history behavior, relationships and cardinality, constraints, privacy classification, target platform, migration compatibility, tests, and rollback. Ask the agent to draft a schema review brief with unresolved evidence clearly marked.
Context path: bundles/deliverables/data-model-schema-design
What the bundle includes
Frameworks
- source-evidence matrix
- qualified-review gate
Evaluations
- Data Model and Schema Design source-awareness check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Inventing source schema, entity identity, grain, cardinality, key uniqueness, metric additivity, history behavior, data quality, privacy classification, or migration safety; creating or altering database objects, access, retention, migrations, or data without explicit approval.
Known limitations
- Use the listed authoritative or identified source surfaces for general Data Model and Schema Design guidance; local facts, records, values, states, permissions, and results require inspected evidence.
- Task-specific work requires current evidence for business questions and process scope, source systems and contracts, sample profiles and quality, entity and identifier definitions, declared grain, facts and aggregation rules, dimensions and history requirements, relationships and cardinality, null and constraint policy, naming and data types, privacy classification and retention, access, lineage, target platform, performance, migration and compatibility, tests, owners, and approvals.
- Do not infer source schema, entity identity, grain, cardinality, key uniqueness, metric additivity, history behavior, data quality, privacy classification, or migration safety.
Safety notes
- Minimize personal, customer, employee, financial, credential, security, privileged, health, student, and other sensitive data.
- Require explicit confirmation before actions that create or alter schemas, tables, fields, keys, constraints, migrations, retention, or access controls; read, copy, transform, or delete data; deploy database changes.
- Route legal, privacy, security, compliance, financial, employment, clinical, safety, and other qualified judgments to an evidenced accountable reviewer.