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

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

Install the CData JDBC Driver for Freshteam

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

Start a Spark Shell and Connect to Freshteam Data

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

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

2. With the shell running, you can connect to Freshteam 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 Freshteam Profile on disk (e.g. C:\profiles\Freshteam.apip). Next, set the ProfileSettings connection property to the connection string for Freshteam (see below).

   Freshteam API Profile Settings

   Find your API Key in the top-right corner of your Freshteam account under the API Key section. Your AccountName is the subdomain of your Freshteam URL.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Freshteam 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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Freshteam.apip;ProfileSettings='APIKey=your_api_key;AccountName=your_account_name';").option("dbtable","Branches").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 Freshteam data as a temporary table:

   scala> api_df.registerTable("branches")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Branches WHERE MainOffice = true").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 Freshteam in Apache Spark, you are able to perform fast and complex analytics on Freshteam 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.