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

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

Install the CData JDBC Driver for Azure DevOps

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

Start a Spark Shell and Connect to Azure DevOps Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Azure DevOps/lib/cdata.jdbc.azuredevops.jar
   

2. With the shell running, you can connect to Azure DevOps with a JDBC URL and use the SQL Context load() function to read a table. You can connect to your Azure DevOps account by providing the Organization and PersonalAccessToken.

   Obtaining a Personal Access Token

   A PersonalAccessToken is necessary for account authentication.

   To generate one, log in to your Azure DevOps Organization account and navigate to Profile -> Personal Access Tokens -> New Token. The generated token will be displayed.

   If you wish to authenticate to Azure DevOps using OAuth refer to the online Help documentation for an authentication guide.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Azure DevOps JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.azuredevops.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 Azure DevOps, using the connection string generated above.

   scala> val azuredevops_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:azuredevops:AuthScheme=Basic;Organization=MyAzureDevOpsOrganization;ProjectId=MyProjectId;PersonalAccessToken=MyPAT;InitiateOAuth=GETANDREFRESH;").option("dbtable","Builds").option("driver","cdata.jdbc.azuredevops.AzureDevOpsDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Azure DevOps data as a temporary table:

   scala> azuredevops_df.registerTable("builds")

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

   scala> azuredevops_df.sqlContext.sql("SELECT Id, BuildNumber FROM Builds WHERE Reason = Manual").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 Azure DevOps in Apache Spark, you are able to perform fast and complex analytics on Azure DevOps 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.