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

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

Install the CData JDBC Driver for Aircall

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

Start a Spark Shell and Connect to Aircall Data

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

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

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

   Aircall API Profile Settings

   In Aircall, go to your company settings and create a new API key to receive an API ID and API token. Combine them as

   APIId:APIToken

   for the APIKey property.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Aircall.apip;ProfileSettings='APIKey=your_api_id:your_api_token';").option("dbtable","Calls").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 Aircall data as a temporary table:

   scala> api_df.registerTable("calls")

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

   scala> api_df.sqlContext.sql("SELECT Id, Direction FROM Calls WHERE Status = completed").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 Aircall in Apache Spark, you are able to perform fast and complex analytics on Aircall 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.