Load Mux - Video data to Azure Blob Storage

Mux is a developer platform for video streaming and management through REST APIs. Everything needed to build a working Mux - Video → Azure Blob Storage 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 Mux - Video to Azure Blob Storage 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 Mux - Video 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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Mux - Video API at a glance

These values come from the Mux - Video API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Mux - Video API?

Use HTTP Basic Authentication over HTTPS. The username is the Mux Access Token ID and the password is the Access Token Secret; the resulting header is Authorization: Basic base64(MUX_TOKEN_ID

). No additional required authentication header is documented.

1. Get your credentials

1. Sign in to the Mux Dashboard and open Settings → Access Tokens, or go directly to https://dashboard.mux.com/settings/access-tokens. 2. Select the intended Mux environment, such as Production; access tokens are scoped to an environment. 3. Create/generate an access token, provide an internal name, and assign the required permissions. For the Video API, grant the minimum needed Mux Video permissions; read-only integrations should use read permission, while pipelines that create or modify assets/uploads/live streams need write permission. 4. Copy and securely store both the generated Token ID and Token Secret immediately. Mux uses the Token ID as the HTTP Basic Auth username and the Token Secret as the password, and does not recover a lost secret; create a new token if it is lost. 5. Send requests over HTTPS to https://api.mux.com. The Video API base path is https://api.mux.com/video/v1. Do not expose the credentials in browser or mobile client code.

2. Add them to .dlt/secrets.toml

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[sources.mux_video_source]
token_id = "your_mux_access_token_id"
token_secret = "your_mux_access_token_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 Mux - Video data can I load into Azure Blob Storage?

These are the Mux - Video endpoints dlt can load into Azure Blob Storage:

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How do I load only new Mux - Video records?

Mux - Video exposes created_at on video/v1/assets, 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": "assets", "endpoint": {
    "path": "video/v1/assets",
    "data_selector": "data",
    "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 Mux - Video pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading assets and live-streams from the Mux - Video API into Azure Blob Storage:

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

@dlt.source
def mux_video_source(token_id_token_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mux.com",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": token_id_token_secret},
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "video/v1/assets", "data_selector": "data"}},
            {"name": "live_streams", "endpoint": {"path": "video/v1/live-streams", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mux_video_to_azure_blob_storage_filesystem() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mux_video_pipeline",
        destination="filesystem",
        dataset_name="mux_video_data",
    )
    load_info = pipeline.run(mux_video_source())
    print(load_info)


if __name__ == "__main__":
    load_mux_video_to_azure_blob_storage_filesystem()

Run it with uv run python mux_video_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 configure Azure Blob Storage as the destination?

dlt writes to Azure Blob Storage through its filesystem destination; the az:// scheme in bucket_url selects it. Install the extra with uv add "dlt[az]", then add to .dlt/config.toml and .dlt/secrets.toml:

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# .dlt/config.toml
[destination.filesystem]
bucket_url = "az://<container>/<path>"

# .dlt/secrets.toml
[destination.filesystem.credentials]
azure_storage_account_name = "<storage account name>"
azure_storage_account_key = "<storage account key>"

dlt writes JSONL by default; pass loader_file_format="parquet" to pipeline.run() for Parquet. Delta and Iceberg table formats are supported too. See the dlt filesystem destination docs.

Changing bucket_url is all it takes to write the same data to Amazon S3, Google Cloud Storage or local files instead; see dlt's filesystem destination for all four.

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How do I query Mux - Video data in Azure Blob Storage?

After a run, pipeline.dataset() holds the loaded tables, one per resource. A Relation is a query on that dataset, either a whole table or a SQL string, and reading it gives you the data as a Pandas DataFrame, a PyArrow table or Python tuples. Nothing is read until you ask for it. A filesystem destination holds files rather than tables, so dlt reads them through DuckDB views and the interface is the same as for a SQL destination; for frequent reads load Delta or Iceberg tables, which DuckDB reads only as far as the query needs (see reading data from filesystem).

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import dlt

pipeline = dlt.pipeline("mux_video_pipeline")

# Step 1: Get the dataset from the pipeline
dataset = pipeline.dataset()

# Step 2: Get a Relation, either a whole table...
assets = dataset.table("assets")
# ...or any SQL query against Azure Blob Storage
sample = dataset("SELECT * FROM assets LIMIT 100")

# Step 3: Read the data
df = assets.df()  # or .df(chunk_size=50) for a large table
arrow_table = sample.arrow()  # a PyArrow Table
rows = sample.fetchall()  # a list of Python tuples

# Which tables were loaded, and how big are they?
print(dataset.row_counts().df())
"""
     table_name  row_count
0        assets         13
1  live_streams          3
"""

The columns are the fields the Mux - Video API returns. Your agent can run the same queries through the MCP server's execute_sql_query tool, and uv run dlthub show opens the Pipeline Dashboard with the schema, load metrics and a query box. See access loaded data in Python and exploring it in marimo notebooks.

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How do I deploy the Mux - Video to Azure Blob Storage 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 Mux - Video 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 Mux - Video 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