Introducing $materialize: SQL interface for FHIR data

FHIR data structures are complex and nested, which makes them challenging to work with using standard SQL tools and analytics platforms.

To address this, Aidbox introduces a new SQL on FHIR operation $materialize. It creates a SQL representation of a ViewDefinition resource, turning complex FHIR structures into familiar table formats that work seamlessly with tools like DBeaver, Metabase, and other SQL-based analytics platforms.

As of September 2025, this is not yet in the SQL on FHIR spec, but we hope to see it standardized in the future.

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What $materialize does

At its core, $materialize bridges two worlds: the flexibility of FHIR and the familiarity of SQL. It:

• Creates a SQL table or view from a ViewDefinition resource.
• Transforms nested FHIR data into flat, queryable structures that SQL tools can understand.
• Provides a familiar interface for analysts and developers working with FHIR data.

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Why this matters

FHIR resources have complex, nested JSON structures that are difficult to query directly with SQL.

Traditionally, this required:

• Writing complex JSON path queries
• Understanding FHIR resource structures in detail
• Custom code to flatten and transform data

$materialize solves this by letting you define the data transformation once as a ViewDefinition, then creating a simple SQL interface that any analyst can use with familiar tools.

Benefits at a glance:

• ‍Simple SQL queries instead of complex JSON operations‍
• Works with any SQL tool (DBeaver, Metabase, Tableau, etc.)‍
• Reusable definitions — define once, query anywhere‍
• Standard FHIR approach using ViewDefinition resources‍
• Performance benefits — avoid complex JSON queries on every request

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Materialization types

$materialize supports three types of materialization, giving you flexibility depending on your use case:

- view (default) — creates a database view that transforms data on the fly

  - Always up-to-date with the latest FHIR data

  - No extra storage required

  - Ideal for most use cases needing a SQL interface

- table — creates a persistent table with a snapshot of the data

  - Fastest reads for large datasets and complex queries

  - Requires manual refresh to update data

  - Uses storage but eliminates query-time processing

- materialized-view — creates a materialized view that can be refreshed

  - Balanced approach between performance and freshness

  - Can be refreshed on demand or scheduled

  - Supports indexes for optimized query performance

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How it works

1. Create a ViewDefinition resource that maps FHIR data to simple columns.
2. POST to /fhir/ViewDefinition/$materialize.
3. Server creates a SQL view/table with the flattened data.
4. The resulting representation is available in the database.
5. Alternatively, you can query it with any SQL tool or Aidbox's /$sql endpoint.

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Example request

Here’s what it looks like in practice.

POST /fhir/ViewDefinition/$materialize
Content-Type: application/json

{
  "name": "patient_view",
  "status": "draft",
  "resource": "Patient",
  "description": "Patient flat view",
  "id": "a099e84c-b3c2-4b6e-8115-a580d25f6495",
  "resourceType": "ViewDefinition",
  "select": [{
    "column": [{
      "name": "id",
      "path": "id",
      "type": "id"
    }, {
      "name": "birth_date",
      "path": "birthDate",
      "type": "date"
    }, {
      "name": "family_name",
      "path": "name.family",
      "type": "string"
    }]
  }]
}

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The response contains information about the created view:

{
  "resourceType": "Parameters",
  "parameter": [{
    "name": "viewName",
    "valueString": "sof.patient_view"
  }, {
    "name": "viewType",
    "valueString": "view"
  }, {
    "name": "viewSchema",
    "valueString": "sof"
  }]
}

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Now you can query the flattened data with simple SQL:

SELECT * FROM sof.patient_view WHERE birth_date > '1990-01-01';

Practical uses

Because $materialize outputs standard SQL, it opens up a wide range of possibilities:

• ‍Analytics and reporting — connect FHIR data to BI tools like Metabase or Tableau‍
• Data exploration — explore FHIR data with SQL tools like DBeaver‍
• ETL pipelines — extract flattened data for downstream systems‍
• Custom applications — build apps that need tabular views of FHIR data‍
• Research and analysis — let researchers query FHIR data with standard SQL

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Performance considerations

While the primary benefit of $materialize is simplifying data access, performance matters for certain use cases:

• ‍High-frequency queries: If you run the same complex queries repeatedly (dashboards, reports), materialized-view or table types can significantly reduce response times.‍
• Large datasets: When working with millions of FHIR resources, materialized storage avoids reprocessing JSON structures on each query.‍
• Complex transformations: ViewDefinitions with multiple joins and aggregations benefit from precomputation.‍
• Analytics workloads: BI tools performing scans across large datasets see dramatic improvements with materialized storage.

For most exploratory work and moderate query volumes, the default view type provides the best balance of simplicity and performance.

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Closing notes

The default view type is perfect for most scenarios where you need a SQL interface to FHIR data — always current, with no extra maintenance.

Consider table or materialized-view types when handling high-frequency queries or large datasets that would benefit from precomputed results.

$materialize transforms complex FHIR structures into simple SQL interfaces, making FHIR data accessible to anyone who knows SQL and enabling the use of standard analytics tools across the healthcare data ecosystem.

For more information on how $materialize works and its parameters, see the Aidbox documentation.