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Loading Data from mux to dremio using Python's dlt Library

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This guide provides technical documentation on how to load data from Mux to Dremio using the open-source Python library, dlt. Mux is a powerful tool that resolves complex issues faced by software teams when developing video features, from live-streaming platforms to on-demand video catalogs. On the other hand, Dremio is a unique data lakehouse solution that caters to leaders at all stages of their data journey, offering flexibility, scalability, and performance. By leveraging dlt, this guide aims to simplify the data transfer process from Mux to Dremio. For more information about Mux, visit Mux's website.

dlt Key Features​

  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.

  • Data Extraction with dlt: dlt simplifies data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction Directed Acyclic Graphs (DAGs) for efficient API calls for data enrichments or transformations. Learn more about data extraction with dlt here.

  • Building Data Pipelines with dlt: dlt provides functionality to support the entire extract and load process. It allows effortless loading via a schema discovery, versioning, and evolution engine. Learn more about building data pipelines with dlt here.

  • How dlt Works: dlt automatically turns JSON returned by any source into a live dataset stored in the destination of your choice. It does this by extracting the JSON data, normalizing it to a schema, and finally loading it to your chosen destination. Learn more about how dlt works here.

  • Mux Verified Source: Mux is a video technology platform that provides infrastructure and tools for developers to build and stream high-quality video content. dlt provides a verified source and a pipeline example to load data using the Mux API to the destination of your choice. Learn more about the Mux verified source 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 Dremio:

pip install "dlt[dremio]"

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

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

dlt[dremio]>=0.3.5

You now have the following folder structure in your project:

mux_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── mux/                   # folder with source specific files
│   └── ...
├── mux_pipeline.py        # 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

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
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

[sources.mux]
mux_api_access_token = "mux_api_access_token" # please set me up!
mux_api_secret_key = "mux_api_secret_key" # please set me up!

[destination.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 32010

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 Dremio 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 mux_pipeline.py, 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='dremio', dataset_name="mux_data"
    )
    load_info = pipeline.run(mux_source())
    print(load_info)


if __name__ == "__main__":
    load_yesterday_video_views()

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

python mux_pipeline.py

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 Dremio 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 can be deployed using Github Actions. It is a powerful CI/CD runner that you can use for free.
  • Deploy with Airflow: You can deploy dlt using Airflow, a platform used to programmatically author, schedule and monitor workflows.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: There are several other ways to deploy dlt. You can explore them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides tools to monitor your pipeline's performance and status. This helps you keep track of your data loads and identify any potential issues early. Read more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to be notified about any significant events or errors that occur during the data loading process. This ensures that you are always informed about the state of your pipeline. Find out how to set up alerts here.
  • Set Up Tracing: Tracing allows you to track the execution of your pipeline and understand the performance of each step. dlt offers tools for setting up tracing to gain insights into your pipeline's behavior. Learn more about setting up tracing here.

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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