Process & Analyze Microsoft Planner Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Microsoft Planner data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Microsoft Planner data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Microsoft Planner data. When you issue complex SQL queries to Microsoft Planner, the driver pushes supported SQL operations, like filters and aggregations, directly to Microsoft Planner and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Microsoft Planner data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Microsoft Planner data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.microsoftplanner.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Microsoft Planner Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Microsoft Planner data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Microsoft Planner, and create a basic report.

Configure the Connection to Microsoft Planner

Connect to Microsoft Planner by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.microsoftplanner.MicrosoftPlannerDriver"
url = "jdbc:microsoftplanner:RTK=5246...;OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

Fill in the connection properties and copy the connection string to the clipboard.

You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Driver opens the MS Planner OAuth endpoint in your default browser. Log in and grant permissions to the Driver. The Driver then completes the OAuth process.

1. Extracts the access token from the callback URL and authenticates requests.
2. Obtains a new access token when the old one expires.
3. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

Load Microsoft Planner Data

Once you configure the connection, you can load Microsoft Planner data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Tasks") \
	.load ()

Display Microsoft Planner Data

Check the loaded Microsoft Planner data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("TaskId"))

Analyze Microsoft Planner Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Microsoft Planner data for reporting, visualization, and analysis.

% sql

SELECT TaskId, startDateTime FROM SAMPLE_VIEW ORDER BY startDateTime DESC LIMIT 5

The data from Microsoft Planner is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Microsoft Planner and start working with your live Microsoft Planner data in Databricks. Reach out to our Support Team if you have any questions.