Load Zencoder data to DuckDB

Zencoder is a cloud-based video and audio transcoding service that enables fast, scalable media processing across various platforms and devices. Everything needed to build a working Zencoder → DuckDB 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 Zencoder to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Zencoder to DuckDB and run it on dltHub

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 Zencoder 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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Zencoder API at a glance

These values come from the Zencoder API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Zencoder API?

Authentication is performed by passing a valid API key in a custom HTTP header named 'Zencoder-Api-Key'. Requests must also include a 'Content-Type: application/json' header.

1. Get your credentials

To obtain your Zencoder API key, navigate to the Zencoder Admin Dashboard at auth.zencoder.ai and sign in. Once authenticated, go to the Administration menu and select API Keys. Click the Add button to generate a new key, provide a descriptive name for your integration, and copy the generated key immediately, as it will not be displayed again. If you do not see the Administration section, ensure your account has Owner or Manager permissions.

2. Add them to .dlt/secrets.toml

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[sources.zencoder_source]
api_key = "zak_live_..."

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 Zencoder data can I load into DuckDB?

These are the Zencoder endpoints dlt can load into DuckDB:

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How do I load only new Zencoder records?

Zencoder exposes page on jobs, 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": "jobs", "endpoint": {
    "path": "jobs",
    "incremental": {"cursor_path": "page", "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 Zencoder pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /jobs and /account from the Zencoder API into DuckDB:

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

@dlt.source
def zencoder_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.zencoder.com/api/v2",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "jobs"}},
            {"name": "account", "endpoint": {"path": "account"}}
        ],
    }
    yield from rest_api_resources(config)


def load_zencoder_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zencoder_pipeline",
        destination="duckdb",
        dataset_name="zencoder_data",
    )
    load_info = pipeline.run(zencoder_source())
    print(load_info)


if __name__ == "__main__":
    load_zencoder_to_duckdb()

Run it with python zencoder_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 Zencoder data in DuckDB?

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("zencoder_pipeline").dataset()
df = data.jobs.df()
print(df.head())

SQL:

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SELECT * FROM zencoder_data.jobs LIMIT 10;

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

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How do I deploy the Zencoder to DuckDB 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 Zencoder 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 Zencoder 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