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

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

Install the CData JDBC Driver in Databricks

To work with live Strava 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 Strava Data in your Notebook: Python

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

Configure the Connection to Strava

Connect to Strava 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\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;"

Built-in Connection String Designer

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

To authenticate to Strava, and connect to your own data or to allow other users to connect to their data, you can use the OAuth standard.

Using OAuth Authentication

You must create a custom OAuth application to connect to Strava. To create a custom OAuth application:

1. Log into the Strava API Settings page
2. Create a new application or select an existing application
3. Set the "Authorization Callback Domain" to your callback URL domain (e.g. localhost)
4. Note down the Client ID and Client Secret

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the Client ID from your Strava API application.
• OAuthClientSecret: Set this to the Client Secret from your Strava API application.
• CallbackURL: Set this to the redirect URI matching your application's callback domain.

Example connection string:

Profile=C:\profiles\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;

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

Load Strava Data

Once you configure the connection, you can load Strava 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" , "Athlete") \
	.load ()

Display Strava Data

Check the loaded Strava data by calling the display function.

Step 3: Checking the result

display (remote_table.select (""))

👁 Displaying Strava Data

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

% sql

SELECT , FROM SAMPLE_VIEW ORDER BY DESC LIMIT 5

👁 Displaying Strava Data

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