Build SAS Data Sets-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 SAS Data Sets enables users to access live SAS Data Sets data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping SAS Data Sets data to any data source natively supported in Google Data Fusion, this article explains how to pipe data from SAS Data Sets to Google BigQuery,

Upload the CData JDBC Driver for SAS Data Sets to Google Data Fusion

Upload the CData JDBC Driver for SAS Data Sets to your Google Data Fusion instance to work with live SAS Data Sets 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: cdatasasdatasets-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver 👁 Image
   
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.sasdatasets) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.sasdatasets.SASDataSetsDriver)
   👁 Configuring the driver (Salesforce is shown.)
   
5. Click "Finish"

Connect to SAS Data Sets Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live SAS Data Sets 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 👁 Adding a database source
   
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-sasdatasets)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for SAS Data Sets. For example:
     
     jdbc:sasdatasets:RTK=5246...;URI=C:/myfolder;

     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.

     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.

      java -jar cdata.jdbc.sasdatasets.jar
      

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

     👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
     
   • Set Import Query to a SQL query that will extract the data you want from SAS Data Sets, i.e.:
     SELECT * FROM restaurants
   👁 Configuring the database source
   
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 sasdatasets-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 Data Sets data into
   👁 Configuring the BigQuery sink
   

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

While this is a simple pipeline, you can create more complex SAS Data Sets pipelines with transforms, analytics, conditions, and more. 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 Google Data Fusion today.