Skip to main content

Loading Data from Google Sheets to Neon Serverless Postgres with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Neon Serverless Postgres. You can get the connection string for your Neon Serverless Postgres database as described in the Neon Serverless Postgres Docs.

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.

Loading data from Google Sheets to Neon Serverless Postgres is a seamless process using the dlt library. Google Sheets allows you to create and edit spreadsheets online, offering real-time collaboration and secure sharing from any device. On the other hand, Neon Serverless Postgres provides a reliable and scalable serverless platform for your database needs. By leveraging the open-source dlt library, you can efficiently transfer data from Google Sheets to Neon Serverless Postgres, enabling faster and more reliable application development. For more details on Google Sheets, visit here.

dlt Key Features​

  • Automated Maintenance: With schema inference and evolution, alerts, and short declarative code, maintenance becomes simple. Learn more
  • Run Anywhere: dlt can run on Airflow, serverless functions, notebooks, and more. No external APIs, backends, or containers are needed, and it scales on both micro and large infrastructures. Learn more
  • User-Friendly Interface: The declarative interface of dlt removes obstacles for beginners while empowering senior professionals. Learn more
  • Governance Support: dlt pipelines offer robust governance through pipeline metadata, schema enforcement and curation, and schema change alerts. Learn more
  • Scaling and Finetuning: dlt provides mechanisms and configuration options to scale up and finetune pipelines, including parallel execution and memory buffer adjustments. 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 Neon Serverless Postgres:

pip install "dlt[postgres]"

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

# 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 postgres
# 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[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.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 = 5432
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 Neon Serverless Postgres 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='postgres',
        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='postgres',
        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='postgres',
        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='postgres',
        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='postgres',
        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 Neon Serverless Postgres 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: Follow this guide to deploy your dlt pipeline using Github Actions. Learn more
  • Deploy with Airflow: This guide explains how to deploy your dlt pipeline with Airflow and Google Composer. Learn more
  • Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions. Learn more
  • Explore Other Deployment Options: Check out additional methods for deploying your dlt pipeline. Learn more

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production by following the guidelines provided here.
  • Set up alerts: Ensure you are promptly notified of any issues by setting up alerts for your dlt pipeline. Detailed instructions can be found here.
  • And set up tracing: Trace the execution of your pipeline to diagnose issues and optimize performance. Follow the setup guide here.

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.