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

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

Install the CData JDBC Driver for Microsoft Teams

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

Start a Spark Shell and Connect to Microsoft Teams Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Microsoft Teams/lib/cdata.jdbc.msteams.jar
   

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

   You can connect to MS Teams using the embedded OAuth connectivity. When you connect, the MS Teams OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.msteams.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 Microsoft Teams, using the connection string generated above.

   scala> val msteams_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:msteams:OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;").option("dbtable","Teams").option("driver","cdata.jdbc.msteams.MSTeamsDriver").load()
   

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

4. Register the Microsoft Teams data as a temporary table:

   scala> msteams_df.registerTable("teams")

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

   scala> msteams_df.sqlContext.sql("SELECT subject, location_displayName FROM Teams WHERE Id = Jq74mCczmFXk1tC10GB").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 Microsoft Teams in Apache Spark, you are able to perform fast and complex analytics on Microsoft Teams 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.