How to work with Dynamics 365 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 Dynamics 365, Spark can work with live Dynamics 365 data. This article describes how to connect to and query Dynamics 365 data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Dynamics 365 data due to optimized data processing built into the driver. When you issue complex SQL queries to Dynamics 365, the driver pushes supported SQL operations, like filters and aggregations, directly to Dynamics 365 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 Dynamics 365 data using native data types.

Getting Started

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Install the CData JDBC Driver for Dynamics 365

Download the CData JDBC Driver for Dynamics 365 installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Dynamics 365 Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Dynamics 365 JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Dynamics 365/lib/cdata.jdbc.dynamics365.jar
   

2. With the shell running, you can connect to Dynamics 365 with a JDBC URL and use the SQL Context load() function to read a table.

   Edition and OrganizationUrl are required connection properties. The Dynamics 365 connector supports connecting to the following editions: CustomerService, FieldService, FinOpsOnline, FinOpsOnPremise, HumanResources, Marketing, ProjectOperations and Sales.

   For Dynamics 365 Business Central, use the separate Dynamics 365 Business Central driver.

   OrganizationUrl is the URL to your Dynamics 365 organization. For instance, https://orgcb42e1d0.crm.dynamics.com

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Dynamics 365 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.dynamics365.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 Dynamics 365, using the connection string generated above.

   scala> val dynamics365_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:dynamics365:OrganizationUrl=https://myaccount.operations.dynamics.com/;Edition=Sales;InitiateOAuth=GETANDREFRESH;").option("dbtable","GoalHeadings").option("driver","cdata.jdbc.dynamics365.Dynamics365Driver").load()
   

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

4. Register the Dynamics 365 data as a temporary table:

   scala> dynamics365_df.registerTable("goalheadings")

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

   scala> dynamics365_df.sqlContext.sql("SELECT GoalHeadingId, Name FROM GoalHeadings WHERE Name = MyAccount").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 Dynamics 365 in Apache Spark, you are able to perform fast and complex analytics on Dynamics 365 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.