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Python Data Loading from chess.com to azure synapse using dlt

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This guide provides step-by-step instructions on how to use dlt, an open-source Python library, to load data from Chess.com into Azure Synapse. Chess.com is a popular online platform that caters to chess enthusiasts, offering a range of services such as online chess games, tournaments, lessons, and more. On the other hand, Azure Synapse is a limitless analytics service that unifies enterprise data warehousing and Big Data analytics. By using dlt, you can efficiently extract data from Chess.com and load it into Azure Synapse for further analysis. More information about Chess.com can be found at https://www.chess.com/.

dlt Key Features​

  • Azure Synapse Integration: dlt provides seamless integration with Azure Synapse, allowing for efficient data processing and loading. Find out how to install the DLT library with Synapse dependencies here.
  • Robust Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. More details can be found here.
  • Staging Support: Synapse supports Azure Blob Storage as a file staging destination. dlt first uploads Parquet files to the blob container, and then instructs Synapse to read the Parquet file and load its data into a Synapse table. Learn how to configure credentials for the staging destination here.
  • Additional Destination Options: dlt provides additional configuration settings for Synapse destination such as default_table_index_type, create_indexes, staging_use_msi, port, and connect_timeout. Learn more about these options here.

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

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess 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]>=0.3.25

You now have the following folder structure in your project:

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

[sources.chess]
config_int = 0 # please set me up!

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.chess]
secret_str = "secret_str" # please set me up!

[sources.chess.secret_dict] # please set me up!
key = "value"

[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 Chess.com 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 chess_pipeline.py, as well as a folder chess 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 chess import source


def load_players_games_example(start_month: str, end_month: str) -> None:
    """Constructs a pipeline that will load chess games of specific players for a range of months."""

    # configure the pipeline: provide the destination and dataset name to which the data should go
    pipeline = dlt.pipeline(
        pipeline_name="chess_pipeline",
        destination='synapse',
        dataset_name="chess_players_games_data",
    )
    # create the data source by providing a list of players and start/end month in YYYY/MM format
    data = source(
        ["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"],
        start_month=start_month,
        end_month=end_month,
    )
    # load the "players_games" and "players_profiles" out of all the possible resources
    info = pipeline.run(data.with_resources("players_games", "players_profiles"))
    print(info)


def load_players_online_status() -> None:
    """Constructs a pipeline that will append online status of selected players"""

    pipeline = dlt.pipeline(
        pipeline_name="chess_pipeline",
        destination='synapse',
        dataset_name="chess_players_games_data",
    )
    data = source(["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"])
    info = pipeline.run(data.with_resources("players_online_status"))
    print(info)


def load_players_games_incrementally() -> None:
    """Pipeline will not load the same game archive twice"""
    # loads games for 11.2022
    load_players_games_example("2022/11", "2022/11")
    # second load skips games for 11.2022 but will load for 12.2022
    load_players_games_example("2022/11", "2022/12")


if __name__ == "__main__":
    # run our main example
    load_players_games_example("2022/11", "2022/12")
    load_players_online_status()

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

python chess_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline chess_pipeline 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 chess_pipeline 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 an easy way to deploy your pipeline using Github Actions. You can set a schedule for the Github Action to run your pipeline, and even add additional flags. Learn more about it here.
  • Deploy with Airflow: You can also deploy your pipeline using Airflow. dlt will create an Airflow DAG for your pipeline script, which you can customize to suit your needs. This method is particularly useful if you're using Google Composer, a managed Airflow environment provided by Google. Find out more here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is another option for deploying your pipeline. This serverless execution environment allows you to build and connect cloud services with code. Learn how to deploy with Google Cloud Functions here.
  • Other Deployment Methods: dlt supports a variety of other deployment methods. You can find more information on how to deploy your pipeline using these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides powerful monitoring capabilities to keep track of your pipeline's performance and progress. You can check out the guide on How to Monitor your pipeline for detailed information.
  • Alerts Setup: It's important to be notified of any issues in your pipeline as soon as they occur. dlt allows you to set up alerts to stay on top of potential problems. Learn how to Set up alerts with dlt.
  • Tracing Setup: Tracing is a crucial part of understanding your pipeline's behavior and identifying potential bottlenecks or errors. dlt provides comprehensive tracing capabilities that you can set up following the guide on how to set up tracing.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source chess​

The Chess.com source provides data on player profiles, online statuses, and historical game details.

Resource NameWrite DispositionDescription
players_gamesappendThis resource retrieves players' games that happened between a specified start and end month. It includes various details like accuracy, ratings, results, time control, tournament details, etc. for both the black and white players in each game.
players_online_statusappendThis resource checks the current online status of multiple chess players. It retrieves their username, status, last login date, and check time.
players_profilesreplaceThis resource retrieves player profiles for a list of player usernames. It includes details like the player's avatar, country, followers, streaming status, join date, last online time, league, location, name, player ID, status, title, URL, username, and verification status.

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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