Work with SAS Data Sets Data in Apache Spark Using SQL

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SAS Data Sets JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with SAS Data Sets.



Access and process SAS Data Sets Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for SAS Data Sets, Spark can work with live SAS Data Sets data. This article describes how to connect to and query SAS Data Sets data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live SAS Data Sets data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze SAS Data Sets data using native data types.

Install the CData JDBC Driver for SAS Data Sets

Download the CData JDBC Driver for SAS Data Sets installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to SAS Data Sets Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for SAS Data Sets JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for SAS Data Sets/lib/cdata.jdbc.sasdatasets.jar
  2. With the shell running, you can connect to SAS Data Sets with a JDBC URL and use the SQL Context load() function to read a table.

    Set the following connection properties to connect to your SAS DataSets files:

    • URI: Set this to the folder containing your .sas7bdat resources. Currently we only support local files.

    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.

    Configure the connection to SAS Data Sets, using the connection string generated above.

    scala> val sasdatasets_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sasdatasets:URI=C:/myfolder;").option("dbtable","restaurants").option("driver","cdata.jdbc.sasdatasets.SASDataSetsDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the SAS Data Sets data as a temporary table:

    scala> sasdatasets_df.registerTable("restaurants")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> sasdatasets_df.sqlContext.sql("SELECT name, borough FROM restaurants WHERE cuisine = American").collect.foreach(println)

    You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for SAS Data Sets in Apache Spark, you are able to perform fast and complex analytics on SAS Data Sets data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.