Skip to main content

Python Guide: Loading Data to BigQuery using dlt

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 utilize the open-source Python library, dlt, to load data from to BigQuery. is a comprehensive online platform catering to chess enthusiasts, offering a variety of services including online games, tournaments, and lessons. On the other hand, BigQuery is a serverless, cost-effective enterprise data warehouse that seamlessly scales with your data across multiple clouds. The focus here is to guide you on leveraging dlt to effectively bridge these two platforms. For more information about the source, please visit

dlt Key Features

  • Google BigQuery Support: dlt supports Google BigQuery as a data destination. It provides a detailed setup guide to install dependencies, initialize a project, and configure credentials for BigQuery.

  • DuckDB Support: dlt provides support for DuckDB, a simple and fast analytical database. The setup guide provides instructions on how to install dependencies, initialize a project, and run a pipeline with DuckDB as the destination.

  • Filesystem Support: dlt allows data to be saved in a local or a cloud-based filesystem. The setup guide provides a step-by-step process on how to initialize a project that saves data to a filesystem.

  • Schema Enforcement and Curation: dlt supports schema enforcement and curation to maintain data consistency and quality. Read more about how to adjust a schema.

  • Performance Tuning: dlt offers mechanisms and configuration options to scale up and fine-tune pipelines. It provides details on how to improve performance by running extraction, normalization, and load in parallel, among other techniques.

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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess bigquery
# 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:


You now have the following folder structure in your project:

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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

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

secret_str = "secret_str" # please set me up!

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

location = "US"

project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 source in our docs.
  • Read more about setting up the BigQuery 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, 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(
# create the data source by providing a list of players and start/end month in YYYY/MM format
data = source(
["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"],
# load the "players_games" and "players_profiles" out of all the possible resources
info ="players_games", "players_profiles"))

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

pipeline = dlt.pipeline(
data = source(["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"])
info ="players_online_status"))

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")

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


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 BigQuery 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: Github Actions is a CI/CD runner that can be used to deploy your dlt pipeline. To learn more about how to use it, check out the Github Actions guide.
  • Deploy with Airflow: Google Composer provides a managed Airflow environment. You can use dlt to easily create an Airflow DAG for your pipeline script. Read more about deploying with Airflow in the Airflow guide.
  • Deploy with Google Cloud Functions: Google Cloud Functions allow you to run your code without provisioning or managing servers. You can deploy your dlt pipeline to Google Cloud Functions following the steps in the Google Cloud Functions guide.
  • Other Deployment Methods: There are other ways to deploy your dlt pipeline such as using AWS Lambda, Kubernetes, and more. You can find additional deployment methods in the deployment guide.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring system to help you keep track of your pipeline's performance and status. This feature allows you to detect any potential issues or bottlenecks in your pipeline early on, ensuring smooth and efficient operation. Learn more about how to monitor your pipeline.
  • Set Up Alerts: dlt allows you to set up alerts that notify you of any significant events or changes in your pipeline. This feature enables you to respond swiftly to any potential issues, ensuring the continuity and reliability of your data pipeline. Check out the guide on how to set up alerts.
  • Enable Tracing: With dlt, you can enable tracing to gain detailed insights into the execution of your pipeline. Tracing can help you identify performance bottlenecks, debug issues, and understand the flow of data through your pipeline. Learn more about how to set up tracing.

Available Sources and Resources

For this verified source the following sources and resources are available

Source chess

The 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!


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.