Build SAS xpt-Connected ETL Processes in Google Data Fusion

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData JDBC Driver for SASxpt enables users to access live SAS xpt data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping SAS xpt data to any data source natively supported in Google Data Fusion, this article walks through piping data from SAS xpt to Google BigQuery,

Upload the CData JDBC Driver for SASxpt to Google Data Fusion

Upload the CData JDBC Driver for SASxpt to your Google Data Fusion instance to work with live SAS xpt data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format driver-version.jar. For example: cdatasasxpt-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver
3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
4. On the "Driver configuration" tab:
   • Name: Create a name for the driver (cdata.jdbc.sasxpt) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.sasxpt.SASXptDriver)
5. Click "Finish"

Connect to SAS xpt Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live SAS xpt data in Google Data Fusion Pipelines.

1. Navigate to the Pipeline Studio to create a new Pipeline
2. From the "Source" options, click "Database" to add a source for the JDBC Driver
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-sasxpt)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for SAS xpt. For example:
     
     jdbc:sasxpt:RTK=5246...;URI=C:/folder;

     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).

     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.

     java -jar cdata.jdbc.sasxpt.jar

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

   • Set Import Query to a SQL query that will extract the data you want from SAS xpt, i.e.:
     SELECT * FROM SampleTable_1
4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
5. Click "Properties" on the BigQuery sink to edit the properties
   • Set the Label
   • Set Reference Name to a value like sasxpt-bigquery
   • Set Project ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
   • Set Dataset to a specific Google BigQuery dataset
   • Set Table to the name of the table you wish to insert SAS xpt data into

With the Source and Sink configured, you are ready to pipe SAS xpt data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from SAS xpt and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex SAS xpt pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData JDBC Driver for SASxpt and start working with your live SAS xpt data in Google Data Fusion today.