Moving data from Google Sheets to BigQuery can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Google Sheets as a source connector (using Auth, or usually an API key)
2. set up BigQuery as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Google Sheets

Google Sheets is a cloud-based spreadsheet program that allows users to create, edit, and share spreadsheets online. It is a free alternative to Microsoft Excel and can be accessed from any device with an internet connection. Google Sheets offers a range of features including formulas, charts, and conditional formatting, making it a powerful tool for data analysis and organization. Users can collaborate in real-time, making it easy to work on projects with others. Additionally, Google Sheets integrates with other Google apps such as Google Drive and Google Forms, making it a versatile tool for personal and professional use.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

Prerequisites

1. A Google Sheets account to transfer your customer data automatically from.
2. A BigQuery account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Google Sheets and BigQuery, for seamless data migration.

When using Airbyte to move data from Google Sheets to BigQuery, it extracts data from Google Sheets Surce using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Google Sheets data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Google Sheets as a source connector

1. Open your Google Sheets account and create a new project or select an existing one.
2. Go to the Google Cloud Console and select your project.
3. Click on the "APIs & Services" tab and then select "Credentials".
4. Click on the "Create Credentials" button and select "Service Account Key".
5. Fill in the required fields and select "JSON" as the key type.
6. Click on "Create" and your JSON key file will be downloaded.
7. Open the JSON key file and copy the "client_email" and "private_key" values.
8. Go to Airbyte and select your workspace.
9. Click on "Sources" and then select "Google Sheets".
10. Paste the "client_email" and "private_key" values into the respective fields.
11. Enter the name of the spreadsheet you want to connect to.
12. Click on "Test Connection" to ensure that the connection is successful.
13. If the test is successful, click on "Create Source" to save the connection.
14. You can now use the Google Sheets source connector to extract data from your spreadsheet and integrate it with other tools and platforms.

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery.

Step 3: Set up a connection to sync your data from Google Sheets to BigQuery

Once you've successfully connected Google Sheets as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Google Sheets from the dropdown list of your configured sources.
3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Google Sheets objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Google Sheets to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Google Sheets data.

Use Cases to transfer your Google Sheets data to BigQuery

Integrating data from Google Sheets to BigQueryprovides several benefits. Here are a few use cases:

1. Advanced Analytics: BigQuery's powerful data processing capabilities enable you to perform complex queries and data analysis on your Google Sheets data, extracting insights that wouldn't be possible within Google Sheets alone.
2. Data Consolidation: If you're using multiple other sources along with Google Sheets, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Google Sheets has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: BigQuery provides robust data security features. Syncing Google Sheets data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Google Sheets data.
6. Data Science and Machine Learning: By having Google Sheets data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Google Sheets provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Google Sheets table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Google Sheets account as an Airbyte data source connector.
2. Configure BigQuery as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Google Sheets to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!