Skip to main content

Python Data Loading from Mux to Snowflake Using dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This page provides technical documentation on how to load data from mux, a solution that tackles complex issues encountered by software teams when creating video platforms, into snowflake, a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. The process is facilitated using an open-source Python library, dlt. mux bridges the gap between video creation and its distribution, allowing for both live-streaming and on-demand video catalogs. snowflake is a powerful tool for handling large data volumes, making it an ideal platform for storing and analyzing video data. dlt serves as the intermediary, enabling the smooth transfer of data from mux to snowflake. For more information about mux, visit

dlt Key Features

  • Snowflake Integration: dlt provides seamless integration with Snowflake, one of the most popular data warehousing platforms. It supports various authentication methods including password, key pair, and external authentication. Learn more about it here.
  • Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This allows for better data management practices and overall data governance. More details are available here.
  • Efficient Data Extraction: dlt offers efficient data extraction through decorators and metadata. It uses techniques like iterators, chunking, and parallelization for scalability, and implicit extraction DAGs for efficient API calls for data enrichments or transformations. Find more information here.
  • Schema Management: dlt provides a comprehensive schema management system. It generates schemas from the data during the normalization process and allows users to affect this standard behavior by providing hints. More about this can be found here.
  • Next Steps and Resources: dlt provides a wealth of resources and tutorials for users to continue learning and building with the library. Users are encouraged to build their sources out of existing building blocks and take full advantage of the dlt library. Discover more here.

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 Snowflake:

pip install "dlt[snowflake]"

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 Snowflake. You can run the following commands to create a starting point for loading data from Mux to Snowflake:

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux snowflake
# 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!

database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
warehouse = "warehouse" # please set me up!
role = "role" # 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 Snowflake 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='snowflake', 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 Snowflake 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

  • Github Actions: dlt can be deployed using Github Actions. This involves setting up a CI/CD pipeline that runs your dlt script on a schedule or in response to certain events. Learn more about it here.
  • Airflow: Another option for deploying dlt is through Airflow. This involves creating an Airflow DAG for your pipeline script and customizing it as per your needs. Detailed instructions can be found here.
  • Google Cloud Functions: dlt also supports deployment through Google Cloud Functions. This involves creating a cloud function that triggers your dlt script. Learn more about this process here.
  • Other methods: There are other methods for deploying dlt as well. You can explore them here.

The running in production section will teach you about:

  • Monitor Your Pipeline: With dlt, you can easily track the performance and status of your pipeline, ensuring that your data is always up-to-date and accurate. Visit How to Monitor your pipeline to learn more.
  • Set Up Alerts: dlt allows you to set up alerts, ensuring that you are always aware of any issues that may arise with your pipeline. This allows you to take immediate action when necessary. Check out Set up alerts for more details.
  • Implement Tracing: Tracing is crucial for understanding the flow of data through your pipeline. dlt provides easy-to-use tools for setting up tracing, allowing you to keep track of your data at all times. Learn more at 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!


Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.