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

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This page provides technical documentation on how to load data from mux, a platform that simplifies the complexities of building video streaming services, to redshift, Amazon's fully managed, scalable data warehouse service. The process is facilitated using dlt, an open-source Python library. Whether you're working with live-streaming platforms, on-demand video catalogs, or anything in between, mux provides the solution. With redshift, you can start with a few hundred gigabytes of data and scale to a petabyte or more. dlt ties these two powerful tools together, making the data transfer seamless and efficient. Further information about mux can be found at https://www.mux.com/.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, tracking data loads and facilitating data lineage and traceability. Read more about lineage.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more about how to Adjust a schema.
  • Schema Change Alerts: dlt enables proactive governance by alerting users to schema changes, allowing stakeholders to take necessary actions.
  • Scalability and Finetuning: dlt offers several mechanism and configuration options to scale up and finetune pipelines. Read more about performance.
  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Read more about tracing.

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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux redshift
# 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[redshift]>=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.redshift.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5439
connect_timeout = 15

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 Redshift 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='redshift', 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 Redshift 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: Github Actions is a CI/CD runner that you can use for free. dlt provides the functionality to deploy your pipeline using Github Actions. Read more about it here.
  • Deploy with Airflow: Airflow is a platform to programmatically author, schedule and monitor workflows. dlt can create an Airflow DAG for your pipeline script. Read more about it here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. dlt supports deploying your pipeline with Google Cloud Functions. Read more about it here.
  • Other Deployment Options: dlt provides various other deployment options to suit your needs. Check out the other deployment options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides comprehensive monitoring capabilities to ensure your data pipeline is functioning as expected. Check out the guide on how to monitor your pipeline for more details.
  • Set Up Alerts: Stay informed about any issues in your pipeline. dlt allows you to set up alerts to notify you about any errors or issues. Learn more about how to set up alerts.
  • Set Up Tracing: Understand the flow of data in your pipeline. With dlt, you can set up tracing to track the execution of your pipeline and identify any bottlenecks or issues. Read more about how to 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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