Loading Data from Google Sheets
to Neon Serverless Postgres
with dlt
in Python
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.
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
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
- Load data from Stripe to AWS S3 in python with dlt
- Load data from Google Sheets to PostgreSQL in python with dlt
- Load data from Looker to CockroachDB in python with dlt
- Load data from Chargebee to Neon Serverless Postgres in python with dlt
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- Load data from Mux to Dremio in python with dlt
- Load data from Apple App-Store Connect to Redshift in python with dlt
- Load data from Google Cloud Storage to MotherDuck in python with dlt
- Load data from Bitbucket to Timescale in python with dlt
- Load data from IFTTT to PostgreSQL in python with dlt