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

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

Install the CData JDBC Driver for Pipedrive

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

Start a Spark Shell and Connect to Pipedrive Data

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

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.pipedrive.jar

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

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

   scala> val pipedrive_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:pipedrive:AuthScheme=Basic;CompanyDomain=MyCompanyDomain;APIToken=MyAPIToken;").option("dbtable","Deals").option("driver","cdata.jdbc.pipedrive.PipedriveDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Pipedrive data as a temporary table:

   scala> pipedrive_df.registerTable("deals")

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

   scala> pipedrive_df.sqlContext.sql("SELECT PersonName, UserEmail FROM Deals WHERE Value = 50000").collect.foreach(println)

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

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