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Python Guide: Loading Data from mux to aws s3 using dlt Library

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This documentation provides detailed instructions on how to use the open-source Python library, dlt, to load data from Mux into AWS S3. Mux is a powerful tool that simplifies complex processes involved in building a variety of video platforms, ranging from live-streaming to on-demand video catalogs. On the other hand, AWS S3 is a filesystem destination that facilitates the creation of datalakes by storing data in formats such as JSONL, Parquet, or CSV. By leveraging the functionalities of dlt, you can efficiently transfer data from Mux to AWS S3. For more information about Mux, please visit https://www.mux.com/.

dlt Key Features​

  • Initialise the dlt project: The dlt init command lets you start a new dlt project quickly and easily. You can specify the source and the destination for your pipeline. For more information, visit this link.
  • Filesystem & buckets: The filesystem destination allows you to store data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. For more details, check out this page.
  • Set up bucket storage and credentials: dlt allows you to set up bucket storage and credentials for AWS S3, Google Storage, Azure Blob Storage, and Local file system. You can learn more about this here.
  • Advanced: Using dlt init with branches, local folders or git repos: You can deploy from a branch of the verified-sources repo or from another repo. For more information, follow this link.
  • Provider key formats: dlt translates the standard format where sections and key names are separated by "." into provider-specific formats. It offers two providers: Environment provider and TOML provider. For more details, visit this page.

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 AWS S3:

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 AWS S3. You can run the following commands to create a starting point for loading data from Mux to AWS S3:

# 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 AWS S3 destination in our docs.

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 AWS S3 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: Use GitHub Actions for CI/CD to automate your pipeline deployment. Follow the guide here.
  • Deploy with Airflow and Google Composer: Leverage Google Cloud's managed Airflow environment to deploy your pipeline. Learn more here.
  • Deploy with Google Cloud Functions: Use serverless functions on Google Cloud to deploy your pipeline. Detailed instructions can be found here.
  • Explore other deployment options: Check out other methods to deploy your dlt pipeline by visiting this page.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth and reliable data processing. Read more
  • Set up alerts: Configure alerts to get notified about critical events or issues in your dlt pipeline, ensuring timely interventions. Read more
  • And set up tracing: Implement tracing to gain deeper insights into the execution and performance of your dlt pipeline, helping you diagnose and resolve issues efficiently. Read more

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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