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Loading Data from Google Sheets to The Local Filesystem in Python

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Google Sheets allows users to create and edit online spreadsheets with real-time, secure sharing from any device. Using the open-source Python library dlt, you can load data from Google Sheets and store it in The Local Filesystem. This setup enables the creation of data lakes in a local folder, with the option to store data in formats like JSONL, Parquet, or CSV. For more information on Google Sheets, visit here.

dlt Key Features​

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go.
  • Seamless Integration with Google Sheets: dlt offers a verified source for Google Sheets, allowing you to easily load data from Google Sheets to your desired destination. Learn more at Google Sheets.
  • Flexible Configuration Management: dlt supports a robust configuration management system, enabling you to manage your credentials and configurations effortlessly. See more details in Configuration.
  • Support for Multiple Destinations: Whether you need to store data in S3, Google Storage, or Azure Blob Storage, dlt has you covered. Check out the Filesystem & Buckets page for more information.
  • Dynamic Resource Generation: Create dynamically generated resources with ease using dlt. This feature allows you to tailor your data pipelines to specific needs. Learn how to do this in the Google Sheets Minimal Example.

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 The Local Filesystem:

pip install "dlt[filesystem]"

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 Google Sheets to The Local Filesystem. You can run the following commands to create a starting point for loading data from Google Sheets to The Local Filesystem:

# create a new directory
mkdir google_sheets_pipeline
cd google_sheets_pipeline
# initialize a new pipeline with your source and destination
dlt init google_sheets filesystem
# 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:


google-api-python-client
dlt[filesystem]>=0.3.25

You now have the following folder structure in your project:

google_sheets_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── google_sheets/                   # folder with source specific files
│   └── ...
├── google_sheets_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.google_sheets]
spreadsheet_url_or_id = "spreadsheet_url_or_id" # please set me up!
range_names = 
["a", "b", "c"] # 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.google_sheets.credentials]
client_id = "client_id" # please set me up!
client_secret = "client_secret" # please set me up!
refresh_token = "refresh_token" # please set me up!
project_id = "project_id" # please set me up!

[destination.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 Google Sheets source in our docs.
  • Read more about setting up the The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at google_sheets_pipeline.py, as well as a folder google_sheets 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:


from typing import Sequence

import dlt

from google_sheets import google_spreadsheet


def load_pipeline_with_ranges(
    spreadsheet_url_or_id: str, range_names: Sequence[str]
) -> None:
    """
    Loads explicitly passed ranges
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='filesystem',
        full_refresh=True,
        dataset_name="test",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=range_names,
        get_sheets=False,
        get_named_ranges=False,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_sheets(spreadsheet_url_or_id: str) -> None:
    """
    Will load all the sheets in the spreadsheet, but it will not load any of the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='filesystem',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=True,
        get_named_ranges=False,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_named_ranges(spreadsheet_url_or_id: str) -> None:
    """
    Will not load the sheets in the spreadsheet, but it will load all the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='filesystem',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=False,
        get_named_ranges=True,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_sheets_and_ranges(spreadsheet_url_or_id: str) -> None:
    """
    Will load all the sheets in the spreadsheet and all the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='filesystem',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=True,
        get_named_ranges=True,
    )
    info = pipeline.run(data)
    print(info)


def load_with_table_rename_and_multiple_spreadsheets(
    spreadsheet_url_or_id: str, range_names: Sequence[str]
) -> None:
    """Demonstrates how to load two spreadsheets in one pipeline and how to rename tables"""

    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='filesystem',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )

    # take data from spreadsheet 1
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=[range_names[0]],
        get_named_ranges=False,
    )

    # take data from spreadsheet 2
    data_2 = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=[range_names[1]],
        get_named_ranges=False,
    )
    # apply the table name to the existing resource: the resource name is the name of the range
    data.resources[range_names[0]].apply_hints(table_name="first_sheet_data")
    data_2.resources[range_names[1]].apply_hints(table_name="second_sheet_data")

    # load two spreadsheets
    info = pipeline.run([data, data_2])
    print(info)
    # yes the tables are there
    user_tables = pipeline.default_schema.data_tables()
    # check if table is there
    assert {t["name"] for t in user_tables} == {
        "first_sheet_data",
        "second_sheet_data",
        "spreadsheet_info",
    }


if __name__ == "__main__":
    url_or_id = "1HhWHjqouQnnCIZAFa2rL6vT91YRN8aIhts22SUUR580"
    range_names = ["hidden_columns_merged_cells", "Blank Columns"]

    load_pipeline_with_ranges(url_or_id, range_names)
    load_pipeline_with_sheets(url_or_id)
    load_pipeline_with_named_ranges(url_or_id)
    load_pipeline_with_sheets_and_ranges(url_or_id)
    load_with_table_rename_and_multiple_spreadsheets(url_or_id, range_names)

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

python google_sheets_pipeline.py

4. Inspecting your load result​

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

dlt pipeline google_sheets_pipeline info

You can also use streamlit to inspect the contents of your The Local Filesystem destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline google_sheets_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: Learn how to deploy your pipeline using Github Actions, a CI/CD runner that you can use for free.
  • Deploy with Airflow and Google Composer: Follow this guide to deploy your pipeline with Airflow and Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Explore Other Deployment Options: Check out the other deployment options available for deploying your dlt pipeline.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipelines to ensure they run smoothly and efficiently. How to Monitor your pipeline
  • Set up alerts: Set up alerts to stay informed about the status of your pipelines and quickly address any issues that arise. Set up alerts
  • Set up tracing: Implement tracing to gain detailed insights into the execution of your pipelines, including timing and configuration details. And set up tracing

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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