Skip to main content

Python Data Loading from mux to aws s3 using dlt Library

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Adrian.

Welcome to the technical documentation for loading data from mux to AWS S3 using the open-source Python library, dlt. mux is a comprehensive solution for software teams, simplifying the complex tasks involved in building video platforms, be it live-streaming or on-demand video catalogs. On the other hand, AWS S3 is a remote filesystem destination that stores data in bucket storages, making it an ideal staging point for other destinations or a quick way to build a data lake. The dlt library facilitates this data transfer, providing a straightforward interface for data extraction and loading. For more information about mux, visit Mux.

dlt Key Features​

  • Governance Support in dlt Pipelines: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices and overall data governance. More details can be found here.

  • Mux Verified Source: Mux.com is a video technology platform and dlt provides a verified source for it. This source loads data using the Mux API to the destination of your choice. Detailed setup guide and more information can be found 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 and can be used as a staging for other destinations or to quickly build a data lake. More details can be found here.

  • Scalability and Implicit Extraction DAGs: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. It also incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. More details can be found here.

  • Memory/Disk Management: dlt buffers data in memory to speed up processing and uses the file system to pass data between the extract and normalize stages. You can control the size of the buffers and the size and number of the files to fine-tune memory and CPU usage. More details can be found 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 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 my-mux-pipeline
cd my-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:

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.filesystem]
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!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 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='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: dlt allows you to easily deploy your pipeline using Github Actions. This is a CI/CD runner that you can use for free. You can specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your pipeline using Airflow. dlt will create an Airflow DAG for your pipeline script that you should customize. This is particularly useful for users of Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This allows you to execute your code in response to events without having to manage a server or runtime environment.
  • Other Deployment Options: For other deployment options, you can check out the deployment section in the dlt documentation.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive set of tools for monitoring your data pipeline. You can easily track the progress of your pipeline, inspect the loaded data, and identify any issues that may arise. Learn more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to be notified about any changes or issues in your pipeline. This feature allows you to respond quickly to any problems and ensure that your pipeline is running smoothly. Learn how to set up alerts here.
  • Set Up Tracing: dlt also offers a tracing feature, which provides detailed information about the execution of your pipeline. This can be incredibly useful for debugging and optimizing your pipeline. 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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.