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Loading Data from Mux to AlloyDB with dlt in Python


We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.

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Loading data from Mux to AlloyDB can be challenging due to the complexities involved in handling video data and managing high-performance databases. Mux helps software teams build robust video solutions, from live-streaming to on-demand video catalogs. On the other hand, AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. By using the open-source python library dlt, you can streamline this process, ensuring efficient data extraction, transformation, and loading. This documentation will guide you through the steps required to seamlessly transfer your video data from Mux to AlloyDB using dlt. For more information on Mux, visit Mux.

dlt Key Features

  • Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques, allowing efficient processing of large datasets by breaking them down into manageable chunks. Learn more
  • Implicit extraction DAGs: dlt incorporates implicit extraction DAGs to handle dependencies between data sources and transformations automatically, ensuring data consistency and integrity. Learn more
  • Schema enforcement and curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality by defining the structure of normalized data and guiding its processing and loading. Learn more
  • Governance support: dlt pipelines offer robust governance through pipeline metadata utilization, schema enforcement, and schema change alerts, promoting data consistency, traceability, and control. Learn more
  • Automatic schema evolution: dlt alerts users to schema changes in source data, enabling proactive governance and allowing stakeholders to review, validate changes, and update downstream processes as necessary. Learn 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 AlloyDB:

pip install "dlt[postgres]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux postgres
# 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── mux/ # folder with source specific files
│ └── ...
├── # 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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
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

mux_api_access_token = "mux_api_access_token" # please set me up!
mux_api_secret_key = "mux_api_secret_key" # please set me up!

dataset_name = "dataset_name" # please set me up!

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 = 5432
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 AlloyDB 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, 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='postgres', dataset_name="mux_data"
load_info =

if __name__ == "__main__":

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


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 AlloyDB 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 use GitHub Actions to deploy your dlt pipeline with a simple cron schedule. Read more

  • Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline using Airflow managed by Google Composer. Read more

  • Deploy with Google Cloud Functions: Understand how to deploy your dlt pipeline using Google Cloud Functions for serverless execution. Read more

  • Other Deployment Options: Explore various other methods to deploy your dlt pipeline. Read more

The running in production section will teach you about:

  • How to Monitor your pipeline: Learn to effectively monitor your dlt pipeline in production to ensure smooth operation and quick troubleshooting. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified of any issues or anomalies in your dlt pipeline, ensuring you can take timely action. Set up alerts
  • And set up tracing: Implement tracing to get detailed insights and track the flow of data through your dlt pipeline for better debugging and performance analysis. 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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