Loading Data from google sheets
to databricks
using dlt
in Python
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This page provides technical documentation on how to utilize dlt
, an open-source Python library, to load data from Google Sheets
into Databricks
. Google Sheets
is a versatile online spreadsheet application that enables secure sharing and real-time insights from any device. On the other hand, Databricks
is a unified data analytics platform developed by the original creators of Apache Spark™, designed to accelerate innovation by integrating data science, engineering, and business. By using dlt
, you can effectively bridge these two platforms, transferring data seamlessly from Google Sheets
to Databricks
. For more information on Google Sheets
, visit https://www.google.com/sheets/about/.
dlt
Key Features
- Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more here.
- Governance Support:
dlt
pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Find out more here. - Google Sheets Data Pipeline: Learn how to load Google Sheets data using Python and the
dlt
library. It covers working with Google API, using built in credentials, using union of credentials, and creating dynamically generated resources. Check out the guide here. - Databricks Destination:
dlt
supports Databricks as a destination for your data pipelines. Learn how to set up and use Databricks withdlt
here. - Loading Data from an API:
dlt
makes it easy to load data from an API into your data stack. Learn how to build a pipeline that loads data from an API 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 Databricks
:
pip install "dlt[databricks]"
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 Databricks
. You can run the following commands to create a starting point for loading data from Google Sheets
to Databricks
:
# 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 databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # please set me up!
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='databricks',
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='databricks',
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='databricks',
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='databricks',
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='databricks',
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 Databricks
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 deploy your pipelines with Github Actions. It provides a step by step guide for setting up a Github Actions workflow that runs your pipeline. Check out the guide here. - Deploy with Airflow: You can also deploy your pipelines with Airflow.
dlt
provides a guide to help you set up your Airflow environment and deploy your pipeline. Learn more about it here. - Deploy with Google Cloud Functions:
dlt
supports deployment on Google Cloud Functions. This guide will walk you through the process of setting up your Google Cloud Functions environment and deploying your pipeline. Check it out here. - Other Deployment Options:
dlt
supports a variety of other deployment options. You can explore more about them here.
The running in production section will teach you about:
- Monitor Your Pipeline:
dlt
provides detailed insights about your pipeline, including load information and runtime trace. This information is useful to understand the status and performance of your pipeline. Learn more about how to monitor your pipeline. - Set Up Alerts: With
dlt
, you can set up alerts to be notified about any changes in your pipeline, such as schema changes. This ensures that you are always informed about the state of your pipeline. Read about how to set up alerts. - Set Up Tracing: Tracing provides detailed information about the execution of your pipeline. It includes timing information on extract, normalize, and load steps and also all the config and secret values. Learn how to set up tracing.
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