Work with ADP 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 ADP, Spark can work with live ADP data. This article describes how to connect to and query ADP data from a Spark shell.

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

Install the CData JDBC Driver for ADP

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

Start a Spark Shell and Connect to ADP Data

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

   Connect to ADP by specifying the following properties:

   • SSLClientCert: Set this to the certificate provided during registration.
   • SSLClientCertPassword: Set this to the password of the certificate.
   • UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
   • RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

   The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. For more information, refer to the OAuth section in the Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.adp.jar

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

   Configure the connection to ADP, using the connection string generated above.

   scala> val adp_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:adp:OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'").option("dbtable","Workers").option("driver","cdata.jdbc.adp.ADPDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the ADP data as a temporary table:

   scala> adp_df.registerTable("workers")

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

   scala> adp_df.sqlContext.sql("SELECT AssociateOID, WorkerID FROM Workers WHERE AssociateOID = G3349PZGBADQY8H8").collect.foreach(println)

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

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