Skip to main content

Python Data Loading from chess.com to snowflake with dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Chess.com into Snowflake. Chess.com is an online platform that caters to chess enthusiasts, offering a variety of services including online chess games, tournaments, and lessons. On the other hand, Snowflake is a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. By using dlt, users can effectively transfer data between Chess.com and Snowflake. For more information about the source, please visit Chess.com at https://www.chess.com/.

dlt Key Features​

  • Snowflake Destination: dlt supports Snowflake as a destination for your data. It includes different authentication methods to securely connect to your Snowflake instance. Learn more
  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more
  • Project Initialization: dlt allows you to initialize a project with a pipeline that loads to snowflake by running dlt init chess snowflake. Learn more
  • Data Extraction: Extracting data with dlt is simple. It offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Learn more
  • Database User and Permissions Setup: dlt provides a guide on how to set up the database user and permissions on Snowflake. 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 Snowflake:

pip install "dlt[snowflake]"

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

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess snowflake
# 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[snowflake]>=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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # 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 Snowflake 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='snowflake',
        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='snowflake',
        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 Snowflake 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 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.

  • Deploy with Airflow: You can also deploy your pipeline with dlt using Airflow. This is a platform used to programmatically author, schedule and monitor workflows. dlt makes it easy to create an Airflow DAG for your pipeline script.

  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services.

  • Other Deployment Options: There are several other ways to deploy your pipeline with dlt. You can find more information on these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to easily monitor your pipeline by inspecting and saving load information, trace information, and schema changes. You can also inspect your pipeline using the command line. For more details, check out the guide on how to monitor your pipeline.
  • Set Up Alerts: With dlt, you can set up alerts to keep track of any changes or issues in your pipeline. This feature allows you to stay updated and respond quickly to any potential problems. Learn more about how to set up alerts.
  • Set Up Tracing: dlt also provides the capability to set up tracing, which is crucial for debugging and understanding the performance of your pipeline. Check out the guide on how to set up tracing for more information.

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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.