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Python Data Loading from mux to azure cloud storage with dlt

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Mux to Azure Cloud Storage. Mux is a solution that addresses the complex challenges software teams encounter when creating video platforms, including live-streaming and on-demand video catalogs. Azure Cloud Storage is a filesystem destination that stores data on Microsoft Azure, facilitating the creation of datalakes. The data can be uploaded in JSONL, Parquet, or CSV formats. For more details on Mux, visit https://www.mux.com/.

dlt Key Features​

  • Robust Governance Support: dlt pipelines provide strong governance capabilities through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.

  • Scalability and Fine-tuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines, including running extraction, normalization, and load in parallel, and fine-tuning memory buffers, intermediary file sizes, and compression options. Read more about these features here.

  • Filesystem & Buckets: dlt supports storing data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. It uses fsspec to abstract file operations. Learn more about this feature here.

  • Data Extraction: dlt allows for scalable data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs to handle dependencies between data sources and their transformations automatically. Learn more about data extraction here.

  • Memory/Disk Management: dlt allows for fine-tuning of memory and CPU usage by controlling the size of in-memory buffers and the size and number of intermediary files. Learn more about memory/disk management 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 Azure Cloud Storage:

pip install "dlt[filesystem]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_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 Azure Cloud Storage destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials] section in your secrets.toml.

[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem', 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 Azure Cloud Storage 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: Learn how to deploy your dlt pipeline using GitHub Actions.
  • Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions.
  • Explore Other Deployment Methods: Check out additional methods for deploying your dlt pipeline here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operation and quick issue detection. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified about important events and errors in your dlt pipeline, ensuring you can respond promptly. Set up alerts
  • And set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you debug and optimize performance. And 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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