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How to work with Oracle HCM Cloud Data in Apache Spark using SQL

Access and process Oracle HCM Cloud 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 Oracle HCM Cloud, Spark can work with live Oracle HCM Cloud data. This article describes how to connect to and query Oracle HCM Cloud data from a Spark shell.

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

Install the CData JDBC Driver for Oracle HCM Cloud

Download the CData JDBC Driver for Oracle HCM Cloud installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Oracle HCM Cloud Data

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

    Using Basic Authentication

    You must set the following to authenticate to Oracle HCM Cloud:

    • Url: The Url of your account.
    • User: The user of your account.
    • Password: The password of your account.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.oraclehcm.jar

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

    Configure the connection to Oracle HCM Cloud, using the connection string generated above.

    scala> val oraclehcm_df ="jdbc").option("url", "jdbc:oraclehcm:Url=;User=user;Password=password;").option("dbtable","RecruitingCESites").option("driver","cdata.jdbc.oraclehcm.OracleHCMDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Oracle HCM Cloud data as a temporary table:

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

    scala> oraclehcm_df.sqlContext.sql("SELECT SiteId, SiteName FROM RecruitingCESites WHERE Language = English").collect.foreach(println)

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

Using the CData JDBC Driver for Oracle HCM Cloud in Apache Spark, you are able to perform fast and complex analytics on Oracle HCM Cloud 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.