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Python Data Loading from Mux to Azure Synapse with dlt Library

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Mux into Azure Synapse. Mux is a versatile platform that simplifies the complexities of building video applications, offering solutions for live-streaming, on-demand video catalogs, and more. On the other hand, Azure Synapse is a limitless analytics service that seamlessly integrates enterprise data warehousing and Big Data analytics. By leveraging dlt, you can efficiently transport data from Mux to Azure Synapse for robust analysis. More information about Mux can be found here.

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 the dependencies between data sources and their transformations automatically. A DAG represents a directed graph without cycles, where each node represents a data source or transformation step. Read more
  • Azure Synapse dlt destination: You can install the DLT library with Synapse dependencies using pip install dlt[synapse]. Read more
  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Mux dlt Verified Source: This Mux dlt verified source and pipeline example loads data using “Mux API” to the destination of your choice. 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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Azure Synapse 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='synapse', 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 Azure Synapse 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 provides a simple way to deploy your pipelines using Github Actions. This method is easy to use and free for most users.
  • Deploy with Airflow: If you prefer using Airflow for your deployments, dlt offers a straightforward way to deploy your pipelines using Airflow. This method is especially suitable for Google Composer users.
  • Deploy with Google Cloud Functions: You can also deploy your dlt pipelines using Google Cloud Functions. This method is ideal for users who prefer serverless deployments.
  • Other Deployment Methods: dlt supports several other deployment methods. You can find more information about these methods here.

The running in production section will teach you about:​

  • Monitor your Pipeline: dlt provides you with the ability to monitor your pipelines to ensure they are running smoothly and efficiently. You can learn more about this in the guide on how to monitor your pipeline.
  • Set up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipelines. This can help you to quickly address any problems and maintain the efficiency of your data processing. Check out the guide on how to set up alerts.
  • Set up Tracing: Tracing in dlt enables you to track the execution of your pipelines, providing valuable insights into their performance and any potential issues. You can learn more about this in the guide on 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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