How to connect and process SAS Data Sets 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 SAS Data Sets data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live SAS Data Sets data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live SAS Data Sets data. When you issue complex SQL queries to SAS Data Sets, the driver pushes supported SQL operations, like filters and aggregations, directly to SAS Data Sets 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 SAS Data Sets data using native data types.

Install the CData JDBC Driver in Azure

To work with live SAS Data Sets 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.sasdatasets.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to SAS Data Sets from Databricks

With the JAR file installed, we are ready to work with live SAS Data Sets 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 SAS Data Sets, and create a basic report.

Configure the Connection to SAS Data Sets

Connect to SAS Data Sets 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.sasdatasets.SASDataSetsDriver"
url = "jdbc:sasdatasets:RTK=5246...;URI=C:/myfolder;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SAS Data Sets 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 the following connection properties to connect to your SAS DataSet files:

Connecting to Local Files

• Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
• Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

Connecting to Cloud-Hosted SAS DataSet Files

While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

Load SAS Data Sets Data

Once the connection is configured, you can load SAS Data Sets 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" , "restaurants") \
	.load ()

Display SAS Data Sets Data

Check the loaded SAS Data Sets data by calling the display function.

display (remote_table.select ("name"))

Analyze SAS Data Sets 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 SAS Data Sets data for analysis.

% sql

SELECT name, borough FROM restaurants WHERE cuisine = 'American'

The data from SAS Data Sets 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 SAS Data Sets and start working with your live SAS Data Sets data in Azure Databricks. Reach out to our Support Team if you have any questions.