How to work with Sage Intacct 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 Sage Intacct, Spark can work with live Sage Intacct data. This article describes how to connect to and query Sage Intacct data from a Spark shell.

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

Getting Started

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Install the CData JDBC Driver for Sage Intacct

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

Start a Spark Shell and Connect to Sage Intacct Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Sage Intacct/lib/cdata.jdbc.sageintacct.jar
   

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

   To connect using the Login method, the following connection properties are required: User, Password, CompanyId, SenderId and SenderPassword.

   User, Password, and CompanyId are the credentials for the account you wish to connect to.

   SenderId and SenderPassword are the Web Services credentials assigned to you by Sage Intacct.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.sageintacct.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 Sage Intacct, using the connection string generated above.

   scala> val sageintacct_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sageintacct:User=myusername;CompanyId=TestCompany;Password=mypassword;SenderId=Test;SenderPassword=abcde123;").option("dbtable","Customer").option("driver","cdata.jdbc.sageintacct.SageIntacctDriver").load()
   

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

4. Register the Sage Intacct data as a temporary table:

   scala> sageintacct_df.registerTable("customer")

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

   scala> sageintacct_df.sqlContext.sql("SELECT Name, TotalDue FROM Customer WHERE CustomerId = 12345").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 Sage Intacct in Apache Spark, you are able to perform fast and complex analytics on Sage Intacct 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.