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Python Data Loading from mux to databricks with dlt Library

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This page provides technical documentation on how to load data from Mux to Databricks using the open-source Python library, dlt. Mux is a solution that handles complex issues faced by software teams when creating video content, including live-streaming platforms and on-demand video catalogs. On the other hand, Databricks is a unified data analytics platform developed by the original creators of Apache Spark™, designed to accelerate innovation by integrating data science, engineering, and business. By using dlt, you can facilitate the process of transferring data from Mux to Databricks. For more details about Mux, visit https://www.mux.com/.

dlt Key Features​

  • Robust governance support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Efficient data extraction: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets. Read more
  • Databricks support: dlt supports Databricks as a destination. To use the Databricks destination, you need to install the DLT library with Databricks dependencies. Read more
  • Building data pipelines: dlt offers functionality to support the entire extract and load process. By utilizing a dlt pipeline, you can easily adapt and structure data as it evolves, reducing the time spent on maintenance and development. Read more
  • DuckDB support: dlt supports DuckDB as a destination. To use DuckDB as a destination, you need to install the DLT library with DuckDB dependencies. 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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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='databricks', 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 Databricks 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 simple command line interface to deploy your pipeline using Github Actions. It's as simple as running dlt deploy <script>.py github-action --schedule "*/30 * * * *". More details can be found here.
  • Deploy with Airflow: If you prefer using Airflow for orchestrating your data pipelines, dlt got you covered. You can deploy your pipeline on Airflow with the command dlt deploy <script>.py airflow-composer. More details can be found here.
  • Deploy with Google Cloud Functions: dlt also supports deployment on Google Cloud Functions. It's as simple as running dlt deploy <script>.py google-cloud-function. More details can be found here.
  • Other Deployment Options: Apart from the above, dlt supports a variety of other deployment options. You can explore them here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides comprehensive monitoring capabilities. You can keep track of your pipeline's performance, data quality, and more. Learn how to effectively monitor your pipeline here.
  • Set up alerts: Stay informed about your pipeline's status with dlt's alerting features. You can set up alerts for various events and issues. Find out how to set up alerts here.
  • Set up tracing: Tracing in dlt allows you to understand the execution flow of your pipeline, making it easier to debug and optimize. Learn how to set 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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