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

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

Install the CData JDBC Driver for Quaderno

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

Start a Spark Shell and Connect to Quaderno Data

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

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

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

   Quaderno API Profile Settings

   Find your API Key in your Quaderno account under Developers > API Keys > Private Key. Your AccountName is the subdomain of your Quaderno URL.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("coupons")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Coupons WHERE Code = SUMMER2024").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 Quaderno in Apache Spark, you are able to perform fast and complex analytics on Quaderno 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.