Public health medallion architecture

Public health teams need streaming events, program datasets, and sensitive records to move through governed layers with recovery and auditability.

Mage

I mapped this healthcare use case as a Mage workflow.

  • ingest health program apps, streaming events, person data, and public datasets
  • validate offsets, retries, schema drift, PHI/PII handling, and program rules
  • promote bronze, silver, and gold workflow layers
  • publish government dashboards, ML training data, audit logs, and safe AI context

You can inspect the stream offsets, retry state, validation results, medallion promotions, delivery targets, and audit evidence before downstream teams use the output.

Use Mage to run public health medallion workflows with auditability and safe outputs.

Public health teams need streaming events, program datasets, and sensitive records to move through governed layers with recovery and auditability.

Health program apps and streaming events feed Mage bronze, silver, and gold workflows with offset and retry controls, validation, audit logs, government dashboards, and ML training outputs.

From healthcare source chaos to governed action

Use Mage as the execution layer between the specific sources, validation rules, governed delivery targets, and reusable context this healthcare workflow needs.

Move Stripe customer data into BigQuery every weekday before 8am.

Map health sources. Sequence program apps, streaming events, public datasets, and person-level records into bronze, silver, and gold workflow layers.

Why did revenue freshness drop?

The Stripe ingest arrived 42 minutes late, delaying the customer revenue model.

Stripe ingest delayed 42 min

Validate medallion layers. Check event completeness, schema drift, PHI/PII handling, offset recovery, and program quality rules before promotion.

Customer syncRun delayed
Inspect run
Late source foundRetry availableOpen logs

Register audit context. Attach offsets, retries, validation evidence, lineage, access scope, and audit history to the workflow context.

Pipeline
Table
Chart
File
AnswerRevenue freshness dropped after the Stripe ingest arrived late.

Deliver public health outputs. Publish governed aggregates, government dashboards, ML training data, audit exports, and safe AI context.

How Mage runs public health medallion workflows

Mage turns public health streams and program datasets into governed medallion workflows with recovery, validation, and audit context.

Run the public health medallion workflow. Ingest health events, validate sensitive data, promote bronze, silver, and gold layers, publish dashboards and ML outputs, and preserve the audit trail.

The public health medallion workflow is ready to review. I staged the health events, validated sensitive data, promoted trusted layers, prepared dashboards and ML outputs, and logged the audit trail.

Customer health workflow
3 sourcesDaily at 7amChecks enabled

Start with the health feed. Start with the health-event stream, public health feed, program dataset, or model-training refresh that needs audit-ready execution.

Build medallion layers. Mage sequences program apps, streaming events, public datasets, and person-level records through bronze, silver, and gold layers.

Validate sensitive data. Validate event completeness, schema drift, program rules, PHI/PII handling, and recovery paths before promotion.

Promote trusted layers. Promote raw events into standardized, analysis-ready, and model-ready layers with explicit quality gates.

Deliver trusted outputs. Publish governed aggregates, government dashboards, audit exports, ML training sets, and safe context packages.

Preserve audit evidence. Keep offsets, retries, validation results, lineage, access scope, and run history attached to every public health workflow.

What the workflow includes

Model the medallion path Mage ingests health program events, person-level records, public datasets, and streaming offsets into bronze, silver, and gold workflow layers with visible state.

Keep sensitive runs recoverable Validation, retry, offset, lineage, and audit checks run before aggregate dashboards, training sets, or downstream analytics receive refreshed data.

Deliver governed outputs Dashboards, ML training data, audit exports, and safe AI context inherit the same source-backed workflow evidence instead of relying on stale extracts.

Use case details

The workflow view shows sources on the left, Mage execution in the middle, and governed outputs plus AI context on the right.

Business problem

Public health teams need streaming events, program datasets, and sensitive records to move through governed layers with recovery and auditability.

Mage workflow story

Move health program apps, streaming events, public datasets, validation, retries, audit logs, dashboards, and ML training outputs through one governed medallion workflow.

Workflow diagram

Health program apps and streaming events feed Mage bronze, silver, and gold workflows with offset and retry controls, validation, audit logs, government dashboards, and ML training outputs.

Bring Mage the program apps, streaming events, public datasets, and model-training handoffs that need stronger recovery and auditability. Mage will map the medallion layers, validation gates, retry controls, and governed outputs with you.