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

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

Install the CData JDBC Driver for Pipeliner CRM

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

Start a Spark Shell and Connect to Pipeliner CRM Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Pipeliner CRM/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the Pipeliner CRM Profile on disk (e.g. C:\profiles\Pipeliner.apip). Next, set the ProfileSettings connection property to the connection string for Pipeliner CRM (see below).

   Pipeliner CRM API Profile Settings

   Navigate to Administration > Obtain API Key within your Pipeliner CRM workspace to retrieve the API Token, API Password, Space ID, and Service URL.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.jar
   

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

   

   Configure the connection to Pipeliner CRM, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Pipeliner.apip;ProfileSettings='User=your_api_token;Password=your_api_password;SpaceId=your_space_id;ServiceUrl=your_service_url';").option("dbtable","Account").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Pipeliner CRM data as a temporary table:

   scala> api_df.registerTable("account")

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

   scala> api_df.sqlContext.sql("SELECT Success, Id FROM Account WHERE Name = Acme Corporation").collect.foreach(println)

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

   

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