Build Strava-Connected ETL Processes in Google Data Fusion

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData API Driver for JDBC enables users to access live Strava data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Strava data to any data source natively supported in Google Data Fusion, this article explains how to pipe data from Strava to Google BigQuery,

Upload the CData API Driver for JDBC to Google Data Fusion

Upload the CData API Driver for JDBC to your Google Data Fusion instance to work with live Strava data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format driver-version.jar. For example: cdataapi-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver
3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
4. On the "Driver configuration" tab:
   • Name: Create a name for the driver (cdata.jdbc.api) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.api.APIDriver)
   
5. Click "Finish"

Connect to Strava Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Strava data in Google Data Fusion Pipelines.

1. Navigate to the Pipeline Studio to create a new Pipeline
2. From the "Source" options, click "Database" to add a source for the JDBC Driver
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-api)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Strava. For example:
     
     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;

     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;
     

     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.

     
   • Set Import Query to a SQL query that will extract the data you want from Strava, i.e.:
     SELECT * FROM Athlete
   
4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
5. Click "Properties" on the BigQuery sink to edit the properties
   • Set the Label
   • Set Reference Name to a value like api-bigquery
   • Set Project ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
   • Set Dataset to a specific Google BigQuery dataset
   • Set Table to the name of the table you wish to insert Strava data into
   

With the Source and Sink configured, you are ready to pipe Strava data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from Strava and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex Strava pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Strava data in Google Data Fusion today.