Process & Analyze SAS xpt Data in Databricks (AWS)

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 xpt data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live SAS xpt data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live SAS xpt data in Databricks, install the driver on your Databricks 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.sasxpt.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access SAS xpt Data in your Notebook: Python

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

Configure the Connection to SAS xpt

Connect to SAS xpt by referencing the JDBC Driver class 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.

Step 1: Connection Information

driver = "cdata.jdbc.sasxpt.SASXptDriver"
url = "jdbc:sasxpt:RTK=5246...;URI=C:/folder;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SAS xpt 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.

Connecting to Local SASXpt Files

You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

Connecting to S3 data source

You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

• URI: Set this to the folder within your bucket that you would like to connect to.
• AWSAccessKey: Set this to your AWS account access key.
• AWSSecretKey: Set this to your AWS account secret key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

Connecting to Azure Data Lake Storage Gen2

You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

• URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
• AzureAccount: Set this to the name of the Azure Data Lake storage account.
• AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

Load SAS xpt Data

Once you configure the connection, you can load SAS xpt data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "SampleTable_1") \
	.load ()

Display SAS xpt Data

Check the loaded SAS xpt data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze SAS xpt Data in Databricks

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

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the SAS xpt data for reporting, visualization, and analysis.

% sql

SELECT Id, Column1 FROM SAMPLE_VIEW ORDER BY Column1 DESC LIMIT 5

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