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Loading Data from Google Sheets to Redshift using Python dlt Library

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This page provides technical documentation on how to use dlt, an open-source Python library, to load data from Google Sheets into Redshift. Google Sheets is a versatile online spreadsheet application that allows for secure, real-time sharing and editing from any device. On the other hand, Redshift is Amazon's fully managed, petabyte-scale data warehouse service in the cloud, capable of scaling from a few hundred gigabytes of data to a petabyte or more. With dlt, you can leverage the flexibility of Google Sheets and the power of Redshift for your data management needs. For more information on Google Sheets, visit https://www.google.com/sheets/about/.

dlt Key Features​

  • Google Sheets API: Comprehensive guide to using the Google Sheets API with dlt to load data from Google Sheets to the destination of your choice.
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts.
  • Preparing Your Data: Detailed instructions on how to prepare your data for loading into dlt, including how to share Google Sheets and provide spreadsheet ID/URL and explicit range names.
  • Getting Started with dlt: Comprehensive guide to getting started with dlt, including a quick-start guide, a Google Colab demo, and resources for learning how to build a pipeline that loads data from an API.
  • Amazon Redshift Destination: Detailed guide on how to set up Amazon Redshift as a destination in your dlt pipeline, including how to initialize the dlt project, set up a Redshift cluster, and add credentials.

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 Redshift:

pip install "dlt[redshift]"

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

# 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 redshift
# 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[redshift]>=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.redshift.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 = 5439
connect_timeout = 15

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 Redshift 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 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='redshift',
        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='redshift',
        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='redshift',
        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='redshift',
        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='redshift',
        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 Redshift 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: dlt allows you to use Github Actions as a CI/CD runner to deploy your pipelines. It provides a step-by-step guide on how to use the dlt deploy command with Github Actions.
  • Deploy with Airflow: You can also deploy dlt pipelines using Airflow, a powerful workflow management platform. The guide provides detailed instructions on how to deploy a pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions, enabling you to execute your pipelines in response to events without having to manage a server.
  • Other Deployment Options: For more ways to deploy dlt pipelines, you can explore other deployment options provided in the dlt documentation.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring feature that allows you to keep track of your data pipeline's performance. It offers insights into the pipeline's operation, which can help you identify any issues that may arise. Check out this guide on How to Monitor your pipeline.
  • Set Up Alerts: Stay informed about the status of your pipeline with dlt's alerting feature. You can set up alerts to notify you about any changes or issues with your pipeline, ensuring you can respond quickly to any potential problems. Learn more in the Set up alerts guide.
  • Implement Tracing: dlt allows you to trace your pipeline's operation, providing detailed information about each step of the process. This feature can be extremely useful for debugging and optimizing your pipeline. Get started with the Set up tracing guide.

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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