How to connect and process FHIR Data from Azure Databricks

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live FHIR data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live FHIR data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live FHIR data. When you issue complex SQL queries to FHIR, the driver pushes supported SQL operations, like filters and aggregations, directly to FHIR and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze FHIR data using native data types.

Install the CData JDBC Driver in Azure

To work with live FHIR data in Databricks, install the driver on your Azure cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.fhir.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to FHIR from Databricks

With the JAR file installed, we are ready to work with live FHIR data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query FHIR, and create a basic report.

Configure the Connection to FHIR

Connect to FHIR by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.fhir.FHIRDriver"
url = "jdbc:fhir:RTK=5246...;URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the FHIR JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.

Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.

Sample Service Base URLs

• Generic: http://my_fhir_server/r4b/
• Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
• AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
• Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/

Generic FHIR Instances

The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.

Load FHIR Data

Once the connection is configured, you can load FHIR data as a dataframe using the CData JDBC Driver and the connection information.

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Patient") \
	.load ()

Display FHIR Data

Check the loaded FHIR data by calling the display function.

display (remote_table.select ("Id"))

Analyze FHIR Data in Azure Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the FHIR data for analysis.

% sql

SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'

The data from FHIR is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for FHIR and start working with your live FHIR data in Azure Databricks. Reach out to our Support Team if you have any questions.