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

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

Install the CData JDBC Driver for Bannerbear

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

Start a Spark Shell and Connect to Bannerbear Data

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

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

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

   Bannerbear API Profile Settings

   Retrieve your API keys from the Bannerbear dashboard. Specify the key type (Project or Master) via the KeyType property.

   Built-in Connection String Designer

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

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