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

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Welcome to our technical documentation on how to load data from Mux to BigQuery using dlt, an open-source Python library. Mux addresses complex challenges that software teams encounter when developing video platforms, including live-streaming and on-demand video catalogs. On the other hand, BigQuery is a cost-effective, serverless enterprise data warehouse that operates across various cloud platforms and scales in accordance with your data. This guide will provide step-by-step instructions on how to leverage dlt to facilitate efficient data transfer from Mux to BigQuery. For more information about Mux, please visit https://www.mux.com/.

dlt Key Features​

  • Scalability and Performance: dlt provides robust scalability and performance features through its support for iterators, chunking, and parallelization techniques. This allows for efficient processing of large datasets by breaking them down into manageable chunks. Read More

  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This ensures data consistency and integrity. Read More

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Read More

  • Google BigQuery Support: dlt provides support for Google BigQuery as a destination. This includes a detailed setup guide for initializing a project with a pipeline that loads to BigQuery and securely handling credentials. Read More

  • DuckDB Support: dlt also offers support for DuckDB as a destination. This includes a comprehensive setup guide, support for multiple file formats, and column hints. 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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir my-mux-pipeline
cd my-mux-pipeline
# initialize a new pipeline with your source and destination
dlt init mux bigquery
# 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[bigquery]>=0.3.5

You now have the following folder structure in your project:

my-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:

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

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.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the BigQuery destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Mux source in the dlt verifed sources documentation.

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='bigquery', 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 BigQuery 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 provides a convenient way to deploy your pipelines using Github Actions. This allows for automated CI/CD workflows that can run your pipeline on a schedule or in response to certain events. Learn more about this here.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow, a powerful platform used to programmatically author, schedule and monitor workflows. dlt integrates seamlessly with Airflow, making it easy to manage and monitor your data pipelines. Find more details here.
  • Deploy with Google Cloud Functions: If you're using Google Cloud Platform, dlt allows you to deploy your pipelines as Google Cloud Functions. This serverless execution environment executes your code in response to events and automatically manages the compute resources for you. Learn how to deploy with Google Cloud Functions here.
  • Other Deployment Options: dlt offers a variety of other deployment options to suit your specific needs. You can explore these other options here.

The running in production section will teach you about:​

  • Monitor your pipeline: With dlt, you can easily monitor your data pipeline. This includes information about the pipeline and dataset name, destination information, and a list of loaded packages. Learn more about this feature here.
  • Set up alerts: dlt allows you to set up alerts for your data pipeline. This includes alerts for schema changes and load failures. You can find more information about setting up alerts here.
  • Set up tracing: Tracing in dlt provides timing information on extract, normalize, and load steps. It also provides all the config and secret values with full information from where they were obtained. 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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