Load Soundcloud data to Databricks

SoundCloud is an audio platform API for accessing public and authorized user music resources. Everything needed to build a working Soundcloud → Databricks pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Soundcloud to Databricks pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Soundcloud API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Soundcloud API at a glance

These values come from the Soundcloud API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Soundcloud API?

Send the access token on every API request as the HTTP header Authorization: OAuth <access_token>. SoundCloud documents the token endpoint at https://secure.soundcloud.com/oauth/token; token acquisition uses OAuth 2.1, with PKCE required for the authorization-code flow. The API guide also shows Accept: application/json; charset=utf-8 and form-urlencoded content type for token exchange, but does not establish that these are required on ordinary GET requests.

1. Get your credentials

1. Obtain a SoundCloud Artist Pro subscription; SoundCloud currently requires Artist Pro to register API applications and receive public-API credentials. 2. Recommended CLI method: ensure Node.js 18+ is installed, then run the SoundCloud API credential script with your app metadata, for example: curl -fsSL https://raw.githubusercontent.com/soundcloud/api/master/scripts/sc-api-auth.mjs | node --input-type=module - --remote --name "My API App" --description "What my integration does" --website "https://example.com". 3. Open the printed https://secure.soundcloud.com/activate/... pairing URL, sign in to SoundCloud, and approve the pairing. 4. The CLI creates or retrieves the app and prints its Client ID and Client Secret; store the secret in an environment variable or secrets manager and never commit or expose it. 5. Browser alternative: open SoundCloud Your Apps, create an application, enter its name, description, and website, accept the terms, then copy the Client ID and Client Secret. 6. For public resources, exchange those credentials at https://secure.soundcloud.com/oauth/token using OAuth client credentials: send grant_type=client_credentials as form data and HTTP Basic authentication containing base64(client_id
   ). The resulting bearer-style access token is sent to API requests in Authorization: OAuth ACCESS_TOKEN (SoundCloud's documented header prefix is OAuth, not Bearer).

2. Add them to .dlt/secrets.toml

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[sources.soundcloud_source]
client_id = "your_soundcloud_client_id"
client_secret = "your_soundcloud_client_secret"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Soundcloud data can I load into Databricks?

These are the Soundcloud endpoints dlt can load into Databricks:

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How do I load only new Soundcloud records?

Soundcloud exposes created_at on users/{user_urn}/tracks, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "user_tracks", "endpoint": {
    "path": "users/{user_urn}/tracks",
    "data_selector": "collection",
    "incremental": {"cursor_path": "created_at", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Soundcloud pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading tracks and users/

/tracks from the Soundcloud API into Databricks:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def soundcloud_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.soundcloud.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "user_tracks", "endpoint": {"path": "users/{user_urn}/tracks", "data_selector": "collection"}},
            {"name": "tracks", "endpoint": {"path": "tracks", "data_selector": "collection"}}
        ],
    }
    yield from rest_api_resources(config)


def load_soundcloud_to_databricks() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="soundcloud_pipeline",
        destination="databricks",
        dataset_name="soundcloud_data",
    )
    load_info = pipeline.run(soundcloud_source())
    print(load_info)


if __name__ == "__main__":
    load_soundcloud_to_databricks()

Run it with uv run python soundcloud_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Soundcloud data in Databricks?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("soundcloud_pipeline").dataset()
df = data.tracks.df()
print(df.head())

SQL:

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SELECT * FROM soundcloud_data.tracks LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Soundcloud to Databricks pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Soundcloud loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Soundcloud data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack