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

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

Install the CData JDBC Driver for Acumatica

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

Start a Spark Shell and Connect to Acumatica Data

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

   Set the following connection properties to connect to Acumatica:

   • User: Set this to your username.
   • Password: Set this to your password.
   • Company: Set this to your company.
   • Url: Set this to your Acumatica URL, in the format http://{Acumatica ERP instance URL}/entity/{Endpoint name}/{Endpoint version}/.
     For example: https://acumatica.com/entity/Default/17.200.001/

   See the Getting Started guide in the CData driver documentation for more information.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.acumatica.jar

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

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

   scala> val acumatica_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:acumatica:Url = https://try.acumatica.com/ISV/entity/Default/17.200.001/;User=user;Password=password;Company=CompanyName;").option("dbtable","Events").option("driver","cdata.jdbc.acumatica.AcumaticaDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Acumatica data as a temporary table:

   scala> acumatica_df.registerTable("events")

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

   scala> acumatica_df.sqlContext.sql("SELECT Id, location_displayname FROM Events WHERE Id = 1").collect.foreach(println)

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

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