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Framework guide
Medallion Architecture Source-Aware Guide
Defines source-aware bronze, silver, and gold layer purpose, grain, schema, quality, lineage, batch or streaming, access, and consumption review, evidence handling, and action boundaries.
Read the fileOverview
Medallion Architecture Overview
Source-aware framework bundle for bronze, silver, and gold layer purpose, grain, schema, quality, lineage, batch or streaming, access, and consumption review, evidence reconciliation, reviewable decisions, and.
Read the fileWorkflow
Medallion Architecture Source-Aware Triage
1. State the decision and direct answer possible now. 2. Record Verified, Provided, Assumed, and Needs verification separately. 3.
Read the fileTemplate
Medallion Architecture review brief
Review-ready artifact for bronze, silver, and gold layer purpose, grain, schema, quality, lineage, batch or streaming, access, and consumption review.
Read the fileIs this bundle right for your task?
Who it is for
- Data architects, data and analytics engineers, platform teams, governance owners, and reviewers designing or assessing layered lakehouse systems
When to use it
- A source or table needs a defensible bronze, silver, or gold role based on business use, grain, transformation, quality, and consumer needs.
- Schema evolution, deduplication, quarantine, lineage, refresh, latency, or access concerns need evidence reconciled across layers.
- A proposed batch or streaming architecture needs platform constraints, ownership, tests, costs, rollback, and approval gates made explicit.
What you need to provide
- Platform and runtime version, business use case, source contracts, ingestion mode, existing layer definitions and owners, table names and grain, schemas, and evolution policy.
- Quality and quarantine rules, transformations, keys and deduplication logic, lineage, refresh and latency targets, access controls, cost evidence, tests, consumers, proposed changes, rollback, and approvals.
Tasks and expected outputs
Questions it helps answer
- Prepare a medallion architecture 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
- Medallion Architecture 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 Medallion Architecture bundle and provide the platform version, use case, source contracts, ingestion mode, current tables and declared grain, schemas and evolution rules, transformation and deduplication logic, quality and quarantine evidence, lineage, refresh targets, access controls, costs, tests, consumers, and rollback constraints. Ask the agent to draft a layer-by-layer review brief with assumptions and decision gates.
Context path: bundles/frameworks/medallion-architecture
What the bundle includes
Frameworks
- Medallion Architecture
- source-evidence matrix
- qualified-review gate
Evaluations
- Medallion Architecture 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 layer assignment, table or schema state, data quality, lineage, freshness, duplication, access, cost, or analytical readiness; creating or altering tables, schemas, pipelines, quality rules, controls, schedules, workloads, data, or published datasets without approval.
Known limitations
- Use the listed authoritative or identified source surfaces for general Medallion Architecture guidance; local facts, records, values, states, permissions, and results require inspected evidence.
- Task-specific work requires current evidence for platform and runtime version, business use case, source contracts and ingestion mode, layer definitions and ownership, table names and grain, schema and evolution policy, quality rules and quarantine behavior, transformations, keys and deduplication, lineage, refresh and latency, access controls, cost, tests, consumers, approvals, and rollback.
- Do not infer layer assignment, table or schema state, data quality, lineage, freshness, duplication, access, cost, or analytical readiness.
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 tables, schemas, pipelines, quality rules, access controls, retention, or schedules; run workloads; read, write, or delete data; or publish datasets.
- Route legal, privacy, security, compliance, financial, employment, clinical, safety, and other qualified judgments to an evidenced accountable reviewer.