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Python Guide: Load Mux Data to MS SQL Server using dlt Library

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This guide provides technical instructions on how to load data from Mux to Microsoft SQL Server using the open-source Python library, dlt. Mux is a comprehensive solution for software teams tackling the complexities of building live-streaming platforms, on-demand video catalogs, and more. On the other hand, Microsoft SQL Server is a relational database management system (RDBMS) that allows applications and tools to connect and communicate using Transact-SQL. By leveraging dlt, users can seamlessly transfer data between Mux and Microsoft SQL Server. More details about the source can be found at https://www.mux.com/.

dlt Key Features​

  • Scalability and Performance: dlt offers scalability through iterators, chunking, parallelization, and efficient API calls for data enrichments or transformations. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more about performance.

  • Data Governance: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These governance features contribute to better data management practices, compliance adherence, and overall data governance. Read more about governance.

  • Data Types Support: dlt supports a variety of data types including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. This wide range of data types allows dlt to handle diverse data structures and formats. Read more about data types.

  • Extraction and Loading: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This ensures data consistency and integrity. Also, dlt uses configurable, idempotent, atomic loads that ensure data safely ends up in the chosen destination. Read more about extraction and loading.

  • Support for Multiple Destinations: dlt supports multiple destinations including Microsoft SQL Server, DuckDB, and many more. This flexibility allows you to choose the most suitable destination for your data. Read more about Microsoft SQL Server and DuckDB.

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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux mssql
# 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[mssql]>=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.mssql.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 Microsoft SQL Server 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='mssql', 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 Microsoft SQL Server 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 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. Learn more about this feature here.
  • Deploy with Airflow: dlt also offers the option to deploy your pipeline with Airflow. This includes creating an Airflow DAG for your pipeline script that you should customize. The DAG uses dlt Airflow wrapper to make this process trivial. More details can be found here.
  • Deploy with Google Cloud Functions: You can deploy your pipeline using Google Cloud Functions. This allows you to run your pipeline in a serverless environment. Google Cloud Functions is a lightweight compute solution for developers to create single-purpose, stand-alone functions that respond to Cloud events without the need to manage a server or runtime environment. Learn more about this here.
  • Other deployment options: dlt provides various other deployment options for your convenience. You can explore all the options here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: dlt provides detailed monitoring capabilities to track the performance and status of your data pipeline. It offers insights into the load packages, job statuses, and other useful information. Check out the guide here.
  • Set up alerts: To ensure that you are promptly notified of any issues in your pipeline, dlt allows you to set up alerts. This feature enables you to stay on top of any potential problems and address them promptly. Learn how to set up alerts here.
  • Set up tracing: Tracing is a powerful feature in dlt that provides timing information on various steps in your pipeline. It also offers insights into config and secret values. Find out 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!

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