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

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

Install the CData JDBC Driver for Templated

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

Start a Spark Shell and Connect to Templated Data

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

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

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

   To authenticate to Templated, you will need an API key. You can obtain your API key from the Templated Dashboard under the API Key tab (app.templated.io > Dashboard > API Key).

   Using API Key Authentication

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Templated API key.

   Example connection string:

   Profile=C:\profiles\Templated.apip;AuthScheme=APIKey;ProfileSettings='APIKey=my_api_key';
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Templated.apip;AuthScheme=APIKey;ProfileSettings='APIKey=my_api_key';").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 Templated 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 , FROM Account WHERE = ").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 Templated in Apache Spark, you are able to perform fast and complex analytics on Templated 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.