Process & Analyze Teamgate 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 Teamgate data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Teamgate data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Teamgate 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.api.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Teamgate Data in your Notebook: Python

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

Configure the Connection to Teamgate

Connect to Teamgate by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the 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.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Teamgate.apip;ProfileSettings='APIKey=your_api_key;AuthToken=your_auth_token';"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Teamgate 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.

Start by setting the Profile connection property to the location of the Teamgate Profile on disk (e.g. C:\profiles\Teamgate.apip). Next, set the ProfileSettings connection property to the connection string for Teamgate (see below).

Teamgate API Profile Settings

To obtain your API Key, log into Teamgate and navigate to Settings > Additional Features > External Apps > New API Key Request. For your Auth Token, go to My Profile > Integrations > API Access > Auth Token.

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Teamgate Data

Once you configure the connection, you can load Teamgate 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" , "Activities") \
	.load ()

Display Teamgate Data

Check the loaded Teamgate data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

👁 Displaying Teamgate Data

Analyze Teamgate 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 Teamgate data for reporting, visualization, and analysis.

% sql

SELECT Id, Status FROM SAMPLE_VIEW ORDER BY Status DESC LIMIT 5

👁 Displaying Teamgate Data

The data from Teamgate 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 API Driver for JDBC and start working with your live Teamgate data in Databricks. Reach out to our Support Team if you have any questions.