How to Connect BigQuery to Snowflake? Top 3 Methods

Published: May 24, 2024 - 8 min read

Julian Alvarado

Integrating BigQuery and Snowflake can significantly enhance data operations within any organization. This blog will guide you on the best methods to connect BigQuery to Snowflake, explaining the advantages and giving detailed, step-by-step tutorials on using Coefficient, Hevo Data, and Airbyte.

Advantages of Connecting BigQuery to Snowflake

  • Simplify data architecture: Consolidate your data warehousing and analytics tools by moving data from BigQuery to Snowflake
  • Enhance data security: Take advantage of Snowflake’s advanced security features to better protect your sensitive data
  • Improve query performance: Leverage Snowflake’s optimized query processing to analyze large datasets more efficiently

Connecting BigQuery and Snowflake: 3 Methods

Connecting BigQuery and Snowflake is crucial for businesses looking to consolidate their data and gain valuable insights. With a range of solutions catering to different needs and user profiles, organizations can choose the best approach to streamline their data integration process.


Best For


Data analysts and business users who need to combine BigQuery and Snowflake data for ad-hoc analysis and reporting using a no-code spreadsheet connector.


Companies that require continuous replication of large volumes of data from BigQuery to Snowflake, ensuring data consistency and enabling near real-time analytics.


Organizations seeking an easy-to-use, affordable solution for moving data from BigQuery to Snowflake, with pre-built connectors and a straightforward interface for configuring data replication jobs.

How to Connect BigQuery to Snowflake

Method 1: Coefficient – No-Code and User-Friendly

Coefficient connects your spreadsheet to all your business data without a single line of code. It provides a user-friendly interface and automatic data updates, making it a reliable option for both technical and non-technical users.

Relevant Features:

  • No-code 2-way sync functionality
  • Automatic data updates
  • Affordable pricing plans

Pros and Cons:


  • Simple, no-code setup
  • Automatic data updates
  • Affordable pricing plans


  • Scheduled automations are not included in the free version

Step-by-Step Guide:

Step 1. Install Coefficient

For Google Sheets

Open a new or existing Google Sheet, navigate to the Extensions tab, and select Add-ons > Get add-ons.

Select Get Add-ons option in Google Sheets Menu Bar

In the Google Workspace Marketplace, search for “Coefficient.”

Search and Choose Coefficient from Google Workspace

Follow the prompts to grant necessary permissions.

Allow Coefficient to be Installed in your Google Sheets

Launch Coefficient from Extensions > Coefficient > Launch.

Launch Coefficient Data Connector in Google Sheets

Coefficient will open on the right-hand side of your spreadsheet.

Coefficient Sidebar in Google Sheets

For Microsoft Excel

Open Excel from your desktop or in Office Online. Click ‘File’ > ‘Get Add-ins’ > ‘More Add-Ins.’

Find Coefficient Add-in from Microsoft Office Store

Type “Coefficient” in the search bar and click ‘Add.’

Add Coefficient Add-in to Excel from Microsoft Office Store

Follow the prompts in the pop-up to complete the installation.

Once finished, you will see a “Coefficient” tab in the top navigation bar. Click ‘Open Sidebar’ to launch Coefficient.

Coefficient Sidebar in Microsoft Excel

Step 2. Import BigQuery Data into Your Spreadsheet

Select ‘Import from…’

  • Image28

Scroll to find and select BigQuery from the dropdown menu.

Choose BigQuery as your data source in Coefficient sidebar

Step 2. Authorize Connection

If this is your first time linking Coefficient to BigQuery, you’ll need to authorize the connection.

Enter your GCP Project ID.

Authorize  BigQuery connection through Coefficient

Follow the prompts to grant Coefficient access to your BigQuery data.

As a note: Coefficient is SOC II compliant and makes data security the #1 priority. You can learn more about security here.


Step 3. Setting Up Your Data Import

After authorizing your account, select ‘Import from Tables’ in the Coefficient menu.

Choose Import from Tables to start Importing

The Data Preview window will appear and show all the table schemas from your BigQuery database. Select the table for your import.

Select the Table you want to import data from

Once the table has been selected, the fields within that table will appear in a list on the left side of the Data Preview window.

Select the fields you need for your import by checking or unchecking the corresponding boxes.

Select data to be connected from BigQuery to Google Sheets using Coefficient

You can further customize your import by adding filters, sorting, or limiting the data import results.

Preview for BigQuery Data Export to Google Sheets using Coefficient add-on

When you’re finished, click the ‘Import’ button.

Your data will populate your spreadsheet in a few seconds!

BigQuery Data Exported into Google Sheets

Step 3. Export Data to Snowflake

Before starting, make sure you’ve connected to Snowflake.

Coefficient Excel Google Sheets Connectors
314,000 Pros Sync Live Data from Their Business Systems into Spreadsheet

Stop exporting data manually. Sync data from your business systems into Google Sheets or Excel with Coefficient and set it on a refresh schedule.

Then, select “Export to…” from the menu.

Choose Export option in Coefficient sidebar

Choose Snowflake from the list of available data sources.

Choose Snowflake as the data source in Coefficient sidebar

Select the tab and header rows in your spreadsheet that contains the data you want to export.

Select tab and headers rows of the data exported to Snowflake from Google Sheets using Coefficient

Click “Next” to continue.

Select the table in your Snowflake database that you want to update. Then, select the type of action you want to perform—Update, Insert, Upsert, or Delete.

Select the table in Snowflake database to be imported into snowflake

Map the fields from your spreadsheet to the corresponding Snowflake fields.

Map the fields between Google Sheets and Snowflake

Note: Primary Keys (ID fields) are required for Update and Delete actions. For Insert actions, the Primary Key field can be set to auto populate if it’s configured in Snowflake.

(Optional) Specify additional settings such as batch size, whether to export empty cells on an update, and the column for results.

Select the necessary formatting options if required

Confirm your settings.

Confirm the settings to push data into Snowflake from Google Sheets in Coefficient Add-on

Then, highlight the rows you want to update or export. You can choose to export all rows or specific rows.

Highlight the google sheet rows to be updated

After you’re finished, review your settings and click “Export.”

Follow the prompts to confirm your changes.

Click on "Update Rows in Snowflake" to push the data from Google Sheets to Snowflake

In a few seconds, data from your spreadsheet will be pushed to Salesforce.

Data exported to Snowflake successfully using Coefficient add-on

Coefficient will then update the records in Snowflake, displaying the status, record ID, and timestamp of the updates in your spreadsheet.

Method 2: Hevo


Hevo is a powerful no-code platform designed for automated data pipelines.

Relevant Features:

  • Automated, real-time data replication
  • No-code setup
  • Robust monitoring and auto-schema management

Pros and Cons:


  • Automated, real-time data replication
  • No-code setup
  • Robust monitoring and auto-schema management


  • Advanced features may require a subscription

Step-by-Step Guide:

1. Getting Started with Hevo Data:

  1. Sign In to Hevo Data:
    • Go to the Hevo Data website and sign in or sign up for an account if you don’t have one.
  2. Create a Pipeline:
    • Once logged in, click on the “Create Pipeline” button.
    • Select BigQuery as the source from the list of available data sources.
  3. Configure Source Settings:
    • Enter the required credentials for your BigQuery account.
    • Provide the necessary details such as the project ID, dataset ID, and other required configurations.
    • Test the connection to ensure Hevo can access your BigQuery data.

2. Configuring Snowflake as the Destination:

  1. Select Snowflake as the Destination:
    • After configuring the BigQuery source, proceed to select Snowflake as the destination.
  2. Enter Snowflake Account Details:
    • Provide the necessary Snowflake account details, including the warehouse, database, and schema where you want the data to be loaded.
    • Enter the required authentication credentials for Snowflake.
  3. Set Up Data Mappings:
    • Define how the data from BigQuery should map to the corresponding tables and columns in Snowflake.
    • Configure any transformations or data processing steps as needed.

3. Executing Data Transfer:

  1. Enable the Pipeline:
    • Once the mappings and configurations are set, enable the pipeline to start the data transfer process.
    • Hevo will begin replicating data from BigQuery to Snowflake in real-time.
  2. Monitor the Data Transfer:
    • Use Hevo’s dashboard to monitor the status and performance of the data transfer.
    • Check for any errors or issues and make adjustments if necessary.

Method 3: Airbyte


Airbyte is an open-source data integration platform with robust customizable features.

Relevant Features:

  • Open-source
  • Extensive connector support
  • Customizable pipelines

Pros and Cons:


  • Open-source
  • Extensive connector support
  • Customizable pipelines


  • Initial setup might require technical expertise
  • Community-driven support

Step-by-Step Guide:

1. Setting Up Airbyte:

  1. Download and Install Airbyte:
    • Visit the Airbyte website and download the installation package.
    • Follow the installation instructions for your operating system to set up Airbyte on your machine.
  2. Create an Account:
    • Once installed, open Airbyte and create an account.
    • Log in to the Airbyte dashboard.

2. Configuring Source and Destination:

  1. Add BigQuery as a Source:
    • In the Airbyte dashboard, click on “New Connection” and select BigQuery as the source.
    • Provide the necessary credentials and select the dataset you want to sync.
  2. Configure Snowflake as the Destination:
    • Next, select Snowflake as the destination.
    • Enter the account details, including the Snowflake warehouse, database, and schema information.
    • Authenticate and test the connection to ensure it is properly configured.

3. Running the Data Pipeline:

  1. Map the Fields:
    • Define the mapping between the fields in BigQuery and the corresponding fields in Snowflake.
    • Configure any necessary transformations or data processing steps.
  2. Schedule the Data Sync:
    • Set up the synchronization schedule according to your requirements (e.g., hourly, daily).
    • Save the configuration and start the sync process.
  3. Monitor the Pipeline:
    • Use Airbyte’s interface to monitor the data pipeline.
    • Check logs and status updates to ensure the data is being transferred correctly.

Troubleshooting Common Issues

Data Sync Delays:

  • Solution: Ensure network stability and check the capacity and resource allocation in Snowflake and BigQuery.

Data Mapping Errors:

  • Solution: Double-check field mappings and ensure schema consistency between the source and destination tables.

Authentication Failures:

  • Solution: Verify access credentials and permissions for both BigQuery and Snowflake. For Airbyte, ensure the correct service account roles are assigned.


Connecting BigQuery to Snowflake can be done efficiently using the above methods. Each method has unique benefits tailored to various use cases and technical expertise. For a simple and affordable solution, give Coefficient a try today.

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Julian Alvarado Content Marketing
Julian is a dynamic B2B marketer with 8+ years of experience creating full-funnel marketing journeys, leveraging an analytical background in biological sciences to examine customer needs.
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