Load Google Sheets Data to PostgreSQL Using Python and dlt
Library
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The dlt
library, an open-source Python tool, facilitates data transfer from Google Sheets
to PostgreSQL
. Google Sheets
is a versatile online spreadsheet application that enables real-time sharing and editing from any device, making it a convenient data source. On the other hand, PostgreSQL
is a robust open-source object-relational database system that extends SQL language, providing safe storage and scalability for complex data workloads. By using dlt
, you can effectively load your data from Google Sheets
into PostgreSQL
for further processing and analysis. More information on Google Sheets
can be found here.
dlt
Key Features
- Google Sheets API:
dlt
provides a verified source for Google Sheets, allowing you to load data from Google Sheets to your choice of destination. More details can be found here. - Governance Support:
dlt
pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about these features here. - Postgres Destination:
dlt
supports Postgres as a destination. It provides detailed guides on how to set up and use Postgres as your data warehouse. Find out more here. - Resource Grouping and Secrets Tutorial: After setting up, you can continue your journey with the Resource Grouping and Secrets tutorial. It provides advanced topics and guides on how to fully take advantage of the
dlt
library. Check it out here. - Airtable API:
dlt
also provides a verified source for Airtable. You can load data from Airtable to your choice of destination using the Airtable API. More details can be found here.
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 PostgreSQL
:
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 PostgreSQL
. You can run the following commands to create a starting point for loading data from Google Sheets
to PostgreSQL
:
# 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.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 PostgreSQL
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
can be easily deployed using Github Actions. This CI/CD runner allows you to schedule your pipeline runs and automate your data workflows. - Deploy with Airflow: With
dlt
, you can deploy your pipelines using Airflow, a platform designed to programmatically author, schedule and monitor workflows. - Deploy with Google Cloud Functions:
dlt
also supports deployment using Google Cloud Functions, a serverless execution environment for building and connecting cloud services. - Other Deployment Options:
dlt
supports various other deployment methods. You can explore more about these in the deployment documentation.
The running in production section will teach you about:
- Monitor Your Pipeline:
dlt
provides comprehensive monitoring capabilities to ensure your data pipeline runs smoothly in production. You can inspect and save load information, trace runtime, and alert on schema changes. Learn more about how to Monitor your pipeline. - Set Up Alerts: Stay informed about your pipeline's performance and any potential issues with
dlt
's alerting feature. You can set up alerts for schema changes, failed jobs, and more. Find out how to Set up alerts. - Enable Tracing: With
dlt
, you can trace your pipeline's runtime, including timing information on extract, normalize, and load steps, and all the config and secret values. This helps in debugging and understanding your pipeline's performance. Learn more about how to Set up tracing.
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