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

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

Install the CData JDBC Driver in Databricks

To work with live ActiveTrail 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\CData API Driver for JDBC\lib).

Access ActiveTrail Data in your Notebook: Python

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

Configure the Connection to ActiveTrail

Connect to ActiveTrail 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.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\ActiveTrail.apip;AuthScheme=APIKey;APIKey=your_api_key;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the ActiveTrail 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 ActiveTrail Profile on disk (e.g. C:\profiles\ActiveTrail.apip). Next, set the ProfileSettings connection property to the connection string for ActiveTrail (see below).

ActiveTrail API Profile Settings

ActiveTrail uses API key authentication. To obtain an API key:

1. Log in to your ActiveTrail account at https://app.activetrail.com
2. Navigate to your account settings and locate the API integration section
3. Generate or copy your existing API key

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your ActiveTrail API key.

Load ActiveTrail Data

Once you configure the connection, you can load ActiveTrail 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" , "GroupMembers") \
	.load ()

Display ActiveTrail Data

Check the loaded ActiveTrail data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

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

% sql

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

The data from ActiveTrail 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 ActiveTrail data in Databricks. Reach out to our Support Team if you have any questions.