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Loading Data from mux to duckdb Using Python's dlt Library

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This page provides technical documentation on how to use the open-source Python library dlt to load data from Mux into DuckDB. Mux is a solution that tackles the complex problems that software teams encounter when building video streaming platforms or on-demand video catalogs. On the other hand, DuckDB is a swift in-process analytical database that supports a feature-rich SQL dialect and has deep integrations into client APIs. Further information about the Mux source can be found at

dlt Key Features

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go. Read more
  • Support for multiple data sources: dlt supports a wide range of data sources, making it a versatile tool for data loading. It can fetch data from various sources including APIs, databases, and files. Read more
  • Robust data pipeline: dlt provides a robust data pipeline that includes fetching data, normalization, loading, and more. Users can also customize the pipeline to suit their specific needs. Read more
  • Data governance: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Support for multiple destinations: dlt supports a wide range of data destinations, including DuckDB, MotherDuck, and more. This allows users to choose the destination that best suits their needs. Read more

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for DuckDB:

pip install "dlt[duckdb]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Mux to DuckDB. You can run the following commands to create a starting point for loading data from Mux to DuckDB:

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux duckdb
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── mux/ # folder with source specific files
│ └── ...
├── # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

mux_api_access_token = "mux_api_access_token" # please set me up!
mux_api_secret_key = "mux_api_secret_key" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations
  • Read more about setting up the Mux source in our docs.
  • Read more about setting up the DuckDB destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at, as well as a folder mux that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

import dlt
from mux import mux_source

def load_yesterday_video_views() -> None:
pipeline = dlt.pipeline(
pipeline_name="mux", destination='duckdb', dataset_name="mux_data"
load_info =

if __name__ == "__main__":

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline mux info

You can also use streamlit to inspect the contents of your DuckDB destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline mux show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

  • Deploy with Github Actions: dlt allows you to deploy your pipelines using Github Actions. This is a great way to automate your workflows and ensure your pipelines are always up-to-date.
  • Deploy with Airflow: If you prefer using Airflow, dlt has got you covered. You can easily deploy your pipelines using Airflow, a platform used to programmatically author, schedule and monitor workflows.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions, allowing you to run your pipelines on Google's serverless platform.
  • More Deployment Options: If you're looking for other ways to deploy your dlt pipelines, you can find more options here.

The running in production section will teach you about:

  • Monitor Your Pipeline: With dlt, you can easily monitor your data pipeline. The library provides detailed load information, including pipeline and dataset names, destination details, and job statuses. This allows you to keep track of your data loads and identify any issues immediately. For more details, check out the guide on How to Monitor your pipeline.
  • Set Up Alerts: dlt allows you to set up alerts for your data pipeline. This way, you can receive immediate notifications about any issues or changes in your data pipeline. This feature helps you to maintain the health and performance of your pipeline. Learn more about it from the guide on how to Set up alerts.
  • Implement Tracing: Tracing is another powerful feature of dlt. It provides timing information on extract, normalize, and load steps, and includes all the config and secret values with full information from where they were obtained. This feature is crucial for debugging and performance optimization. For more information, read the guide on how to set up tracing.

Available Sources and Resources

For this verified source the following sources and resources are available

Source mux_source

"Mux_source" provides data on video content and viewing metrics from the Mux platform.

Resource NameWrite DispositionDescription
assets_resourcemergeFetches metadata about video assets from the Mux API's "assets" endpoint
views_resourceappendFetches data about every video view from yesterday from the Mux API

Additional pipeline guides

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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