Process & Analyze Asana Data in Azure Databricks

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 Asana data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Asana data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Asana data in Databricks, install the driver on your Azure 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.asana.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Asana\lib).

Connect to Asana from Databricks

With the JAR file installed, we are ready to work with live Asana 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 Asana, and create a basic report.

Configure the Connection to Asana

Connect to Asana by referencing the class for the JDBC Driver 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.

driver = "cdata.jdbc.asana.AsanaDriver"
url = "jdbc:asana:RTK=5246...;OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;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 Asana 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 optionally set the following to refine the data returned from Asana.

• WorkspaceId: Set this to the globally unique identifier (gid) associated with your Asana Workspace to only return projects from the specified workspace. To get your workspace id, navigate to https://app.asana.com/api/1.0/workspaces while logged into Asana. This displays a JSON object containing your workspace name and Id.
• ProjectId: Set this to the globally unique identifier (gid) associated with your Asana Project to only return data mapped under the specified project. Project IDs can be found in the URL of your project's Overview page. This will be the numbers directly after /0/.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Asana. OAuth requires the authenticating user to interact with Asana using the browser. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Load Asana Data

Once the connection is configured, you can load Asana data as a dataframe using the CData JDBC Driver and the connection information.

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

Display Asana Data

Check the loaded Asana data by calling the display function.

display (remote_table.select ("Id"))

Analyze Asana Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Asana data for analysis.

% sql

SELECT Id, WorkspaceId FROM projects WHERE Archived = 'true'

The data from Asana 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 Asana and start working with your live Asana data in Apache NiFi. Reach out to our Support Team if you have any questions.