Brightcove Python API Docs | dltHub

Brightcove is a video cloud platform providing APIs to manage video assets, players, playback and analytics. The REST API base URL is https://cms.api.brightcove.com/v1 and Most requests require an OAuth2 Bearer access token; Playback API uses a policy_key..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Brightcove data in under 10 minutes.

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What data can I load from Brightcove?

Here are some of the endpoints you can load from Brightcove:

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How do I authenticate with the Brightcove API?

Obtain client_id and client_secret from Brightcove Studio, then POST to https://oauth.brightcove.com/v4/access_token with HTTP Basic authentication to receive a short‑lived Bearer token. Include "Authorization: Bearer {access_token}" on API calls; Playback calls also require "Accept: application/json;pk={policy_key}".

1. Get your credentials

1. Log in to Brightcove Studio. 2) Navigate to Admin → API Authentication. 3) Click “Create new client credentials”, provide a name and select required scopes. 4) Save and copy the generated client_id and client_secret. 5) Use these values to request an access token as described in the authentication page.

2. Add them to .dlt/secrets.toml

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[sources.brightcove_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
account_id = "your_account_id"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Brightcove API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python brightcove_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline brightcove_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset brightcove_data
The duckdb destination used duckdb:/brightcove.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline brightcove_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads videos and players from the Brightcove API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def brightcove_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cms.api.brightcove.com/v1",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "videos", "endpoint": {"path": "v1/accounts/{account_id}/videos"}},
            {"name": "players", "endpoint": {"path": "v2/accounts/{account_id}/players"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="brightcove_pipeline",
        destination="duckdb",
        dataset_name="brightcove_data",
    )
    load_info = pipeline.run(brightcove_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("brightcove_pipeline").dataset()
sessions_df = data.videos.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM brightcove_data.videos LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("brightcove_pipeline").dataset()
data.videos.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Brightcove data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, your access_token is missing, invalid, or expired. Access tokens expire in ~300 seconds; request a new token using client_id/client_secret at https://oauth.brightcove.com/v4/access_token. Ensure token is sent as Authorization: Bearer {access_token}.

Insufficient scope or 403

403 Forbidden indicates the token lacks required scopes. Recreate client credentials with the necessary API operations selected in Studio's API Authentication page.

Rate limiting (429)

Brightcove returns 429 Too Many Requests when limits are exceeded. Implement exponential backoff and respect Retry-After header if provided.

Pagination

Many list endpoints support limit/offset or page/limit query parameters; check specific API reference. For large exports, iterate using limit and offset until a returned array is empty.

Playback policy_key issues

Playback API requires a policy_key passed in Accept header as Accept: application/json;pk={policy_key}. Using an invalid policy_key returns 401/403.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack