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Loading Data from Mux to EDB BigAnimal with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to EDB BigAnimal. You can get the connection string for your EDB BigAnimal database as described in the EDB BigAnimal Docs.

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Mux solves the hard problems software teams face when building video solutions, from live-streaming platforms to on-demand video catalogs and anything in between. EDB BigAnimal is a fully managed database-as-a-service that runs in your cloud account or BigAnimal's cloud account, operated by one of the builders of Postgres. It simplifies setting up, managing, and scaling databases, offering options like PostgreSQL or EDB Postgres Advanced Server with Oracle compatibility, and distributed high-availability cluster types for geographically distributed databases. This documentation explains how to load data from Mux to EDB BigAnimal using the open-source python library dlt. For more information about Mux, visit mux.com.

dlt Key Features​

  • Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more
  • Implicit extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and their transformations automatically. This ensures data consistency and integrity. Read more
  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data vaulting. Read more
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. This helps maintain data integrity and facilitates standardized data handling practices. Read more
  • Schema evolution: dlt enables proactive governance by alerting users to schema changes, allowing them to review and validate changes, update downstream processes, or perform impact analysis. Read 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 EDB BigAnimal:

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

# 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:

dlt[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.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 = 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 EDB BigAnimal 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='postgres', 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 EDB BigAnimal 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: Automate your pipeline deployment using GitHub's CI/CD runner. Follow the guide to deploy with GitHub Actions.
  • Deploy with Airflow and Google Composer: Utilize Google Composer's managed Airflow environment for your pipeline. Learn more about deploying with Airflow.
  • Deploy with Google Cloud Functions: Leverage serverless functions on Google Cloud to deploy your pipeline. Check out the instructions for Google Cloud Functions.
  • Explore other deployment options: Discover various methods to deploy your pipeline, including other cloud services and environments. Explore all options here.

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 operation. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified about any issues or important events in your dlt pipeline. Set up alerts
  • And set up tracing: Implement tracing to track the execution and performance of your dlt pipeline for better debugging and optimization. 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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