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

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

Install the CData JDBC Driver for Teamgate

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

Start a Spark Shell and Connect to Teamgate Data

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

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

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

   Teamgate API Profile Settings

   To obtain your API Key, log into Teamgate and navigate to Settings > Additional Features > External Apps > New API Key Request. For your Auth Token, go to My Profile > Integrations > API Access > Auth Token.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Teamgate 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 Teamgate, using the connection string generated above.

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

   scala> api_df.registerTable("activities")

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

   scala> api_df.sqlContext.sql("SELECT Id, Status FROM Activities WHERE Status = Completed").collect.foreach(println)

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

   

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