How to work with SAP Business Warehouse Data in Apache Spark using SQL

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

The CData JDBC Driver offers unmatched performance for interacting with live SAP Business Warehouse data due to optimized data processing built into the driver. When you issue complex SQL queries to SAP Business Warehouse, the driver pushes supported SQL operations, like filters and aggregations, directly to SAP Business Warehouse 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 SAP Business Warehouse data using native data types.

Install the CData JDBC Driver for SAP Business Warehouse

Download the CData JDBC Driver for SAP Business Warehouse installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to SAP Business Warehouse Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for SAP Business Warehouse JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for SAP Business Warehouse/lib/cdata.jdbc.sapbusinesswarehouse.jar
   

2. With the shell running, you can connect to SAP Business Warehouse with a JDBC URL and use the SQL Context load() function to read a table.

   To connect to SAP Business Warehouse, set the URL property to a valid SAP Business Warehouse server base URL. The driver must connect to SAP Business Warehouse instances hosted over HTTP with XMLA access.

   The driver supports the following authentication schemes via the AuthScheme property:

   • None: Anonymous authentication, if available on the server.
   • Basic: Set User and Password and set AuthScheme to .
   • Kerberos: See the Using Kerberos section of the help documentation for the required Kerberos properties.

   By default, the driver attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the SAP Business Warehouse JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.sapbusinesswarehouse.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.)
   

   Configure the connection to SAP Business Warehouse, using the connection string generated above.

   scala> val sapbusinesswarehouse_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sapbusinesswarehouse:URL=https://mysapserver:8000;AuthScheme=Basic;User=username;Password=password;").option("dbtable","Sales").option("driver","cdata.jdbc.sapbusinesswarehouse.SAPBusinessWarehouseDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the SAP Business Warehouse data as a temporary table:

   scala> sapbusinesswarehouse_df.registerTable("sales")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> sapbusinesswarehouse_df.sqlContext.sql("SELECT CustomerCount, City FROM Sales WHERE Country = US").collect.foreach(println)

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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