Healthcare organizations face an ongoing challenge: data is everywhere, but it’s rarely connected. Claims systems, EHRs, lab results, pharmacy feeds, member portals, social determinants of health data—all in different formats, on different platforms. To get real value out of this information, you need to move, standardize, and integrate it at scale.
That’s where Microsoft’s data integration services come in.
Azure Data Factory Today
Azure Data Factory (ADF) is Microsoft’s enterprise-grade service for Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), and general data integration. It’s designed to orchestrate and automate data movement across cloud and on-premises environments.
For healthcare, ADF is especially useful for:
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Consolidating claims, clinical, and operational data into centralized repositories (data lakes, warehouses, databases).
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Powering analytics use cases like risk stratification, quality reporting, and cost-of-care analysis.
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Supporting compliance with HIPAA by building repeatable, auditable workflows.
By parameterizing and automating pipelines, ADF helps reduce manual effort and errors, making it easier to ingest CMS files, normalize HL7/FHIR messages, or route encounter data from multiple provider groups into a payer’s warehouse.
Introducing Microsoft Fabric Data Factory
In 2023, Microsoft launched Microsoft Fabric, a unified platform for analytics, storage, and integration. As part of it, they introduced Fabric Data Factory—a next-generation version of ADF.
Fabric Data Factory offers:
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AI-assisted pipeline building with Copilot.
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Tighter integration with Fabric services, including Lakehouse and Power BI.
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Simplified monitoring and management for data pipelines.
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Compatibility with most ADF activities (roughly 90%).
Microsoft has also released migration guides for organizations that want to move existing ADF pipelines into Fabric.
Important to note: ADF has not been retired. It continues to be fully supported and updated. Microsoft’s new investments, though, are going into Fabric Data Factory.
Parameterization in Healthcare Pipelines
Parameterization is one of the most powerful features in ADF—and it carries over to Fabric.
Instead of building separate pipelines for each new file or source, parameterization lets you build flexible, reusable pipelines.
Healthcare example:
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A payer receives encounter data feeds from dozens of provider groups, each with a different file structure.
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Without parameterization, every feed would require its own pipeline, datasets, and copy activities.
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With parameterization, a single dynamic pipeline can process them all. The file name, table name, and destination system can be passed in as parameters.
This reduces maintenance, speeds onboarding of new sources, and cuts errors. It also makes regulatory reporting and CMS data submissions far more manageable.
Typical Use Cases in Healthcare
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Ingesting HL7/FHIR feeds from EHRs into a centralized data lake.
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Loading CMS Part D or encounter data for risk adjustment and compliance.
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Combining claims, clinical, and SDOH data for population health analytics.
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Preparing structured training data for AI models in care management or prior authorization.
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Automating monthly or daily data refreshes for dashboards and reports.
Azure Data Factory vs. Fabric Data Factory
| Scenario | Best Fit |
|---|---|
| Mature, complex ETL processes across hybrid cloud and on-prem systems | Azure Data Factory |
| Modern analytics platform with built-in AI, BI, and governance | Fabric Data Factory |
Healthcare teams that already run ADF don’t need to rush to Fabric. But if you’re starting fresh, or planning to modernize your analytics environment, Fabric is worth strong consideration.
Conclusion
Azure Data Factory remains a powerful solution for orchestrating healthcare data pipelines. Fabric Data Factory is the future-facing option, tightly integrated with Microsoft’s broader analytics ecosystem. Both tools support parameterization and automation, which are critical for reducing complexity, improving data quality, and accelerating time to insight.
For healthcare organizations, the message is simple: whether you choose ADF or Fabric, you can build scalable, compliant, and efficient data integration pipelines that unlock the value of claims, clinical, and operational data.
