How to integrate Spotify with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData API Driver for JDBC, Airflow can work with live Spotify data. This article describes how to connect to and query Spotify data from an Apache Airflow instance and store the results in a CSV file.

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

Configuring the Connection to Spotify

Built-in Connection String Designer

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

Using OAuth Authentication

Spotify uses OAuth 2.0 for authentication. You will need to create an application in the Spotify Developer Dashboard to obtain your client credentials.

Setting Up Your Spotify Application

1. Visit the Spotify Developer Dashboard.
2. Log in with your Spotify account and click Create app.
3. Provide an app name, description, and set a Redirect URI (e.g.,

   http://localhost:33333

   for desktop applications).
4. Copy your Client ID and Client Secret from the app settings.

Connection Properties

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 your Spotify application's Client ID.
• OAuthClientSecret: Set this to your Spotify application's Client Secret.
• Scope: Set this to the required OAuth scopes (space-separated). The default includes all read scopes needed for the tables in this profile.
• CallbackURL: Set this to the Redirect URI configured in your Spotify application (e.g., http://localhost:33333).

Example Connection String

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

Available OAuth Scopes

• user-read-private: Read access to user's subscription details and explicit content settings.
• user-read-email: Read access to user's email address.
• user-library-read: Read access to a user's saved tracks, albums, episodes, shows, and audiobooks.
• playlist-read-private: Read access to user's private playlists.
• playlist-read-collaborative: Read access to collaborative playlists the user follows.
• user-follow-read: Read access to the list of artists the current user follows.
• user-read-playback-state: Read access to a user's player state (device, current track, progress).
• user-read-currently-playing: Read access to a user's currently playing content.
• user-read-playback-history: Read access to a user's recently played tracks.
• user-top-read: Read access to a user's top artists and tracks.

To host the JDBC driver in clustered environments or in the cloud, 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.

The following are essential properties needed for our JDBC connection.

Establishing a JDBC Connection within Airflow

1. Log into your Apache Airflow instance.
2. On the navbar of your Airflow instance, hover over Admin and then click Connections.
3. Next, click the + sign on the following screen to create a new connection.
4. In the Add Connection form, fill out the required connection properties:
   • Connection Id: Name the connection, i.e.: api_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:api:RTK=5246...;Profile=C:\profiles\Spotify.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;)
   • Driver Class: cdata.jdbc.api.APIDriver
   • Driver Path: PATH/TO/cdata.jdbc.api.jar
   
5. Test your new connection by clicking the Test button at the bottom of the form.
6. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections:

Creating a DAG

A DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow. Our workflow is to simply run a SQL query against Spotify data and store the results in a CSV file.

1. To get started, in the Home directory, there should be an "airflow" folder. Within there, we can create a new directory and title it "dags". In here, we store Python files that convert into Airflow DAGs shown on the UI.
2. Next, create a new Python file and title it spotify_hook.py. Insert the following code inside of this new file:

   	import time
   	from datetime import datetime
   	from airflow.decorators import dag, task
   	from airflow.providers.jdbc.hooks.jdbc import JdbcHook
   	import pandas as pd
   
   	# Declare Dag
   	@dag(dag_id="spotify_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=['load_csv'])
   	
   	# Define Dag Function
   	def extract_and_load():
   	# Define tasks
   		@task()
   		def jdbc_extract():
   			try:
   				hook = JdbcHook(jdbc_conn_id="jdbc")
   				sql = """ select * from Account """
   				df = hook.get_pandas_df(sql)
   				df.to_csv("/{some_file_path}/{name_of_csv}.csv",header=False, index=False, quoting=1)
   				# print(df.head())
   				print(df)
   				tbl_dict = df.to_dict('dict')
   				return tbl_dict
   			except Exception as e:
   				print("Data extract error: " + str(e))
               
   		jdbc_extract()
       
   	sf_extract_and_load = extract_and_load()
   

3. Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "spotify_hook".
4. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e. play) button to run the DAG. This executes the SQL query in our spotify_hook.py file and export the results as a CSV to whichever file path we designated in our code.
5. After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv.
6. Open the CSV file to see that your Spotify data is now available for use in CSV format thanks to Apache Airflow.

More Information & Free Trial

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Spotify data in Apache Airflow. Reach out to our Support Team if you have any questions.