4 Methods to Move Data From Google Sheets to Snowflake

Choosing the right method depends on how often your data changes, how much automation you need, and how much operational effort you’re willing to manage.

Is your data in Google Sheets becoming too large for on-demand analytics? Are you struggling to combine data from multiple Google Sheets into a single source of truth for reports and analytics? If that’s the case, then your business may be ready for a move to a mature data platform like Snowflake.

This post covers three approaches for migrating your data from Google Sheets to Snowflake. Snowflake Google Sheets integration facilitates data accessibility and collaboration by allowing information to be transferred and analyzed across the two platforms with ease. The following are the methods you can use to connect Google Sheets to Snowflake in a seamless fashion:

Prerequisites for Connecting Google Sheets to Snowflake

Before you connect Google Sheets to Snowflake using any of the methods covered in this guide, make sure you have the following in place:

• A Google Sheets account with access to the spreadsheet you want to load into Snowflake.
• A Snowflake account with an active warehouse, database, and schema where the data will be stored.
• Appropriate access permissions:
  • Read access to the Google Sheet
  • Write access to the target Snowflake database and schema
• Well-structured source data in Google Sheets, including:
  • Clear column headers
  • Consistent data types
  • No merged cells or hidden header rows
• Authentication credentials, depending on the method used:
  • Google API credentials or OAuth access (for Python or automated tools)
  • Snowflake user credentials or key-pair authentication

Methods to Connect Google Sheets to Snowflake

Before diving into step-by-step instructions, it’s helpful to understand the four main ways teams typically connect Google Sheets to Snowflake. These methods vary based on automation needs, technical effort, and scale.

• Method 1: Managed ELT Pipeline (Hevo)
  A fully managed, no-code approach that automates data movement from Google Sheets to Snowflake, handling scheduling, schema changes, and ongoing reliability with minimal setup.
• Method 2: Manual CSV Uploads
  Export Google Sheets as CSV files and load them into Snowflake manually using staging and the COPY INTO command, best suited for one-time or infrequent data transfers.
• Method 3: Automated / Programmatic Integration (Python)
  Build custom pipelines using Python libraries such as gspread, pandas, and the Snowflake connector to extract, transform, and load data programmatically.
• Method 4: Snowflake’s OpenFlow Connector
  Use Snowflake’s native, low-code OpenFlow connector to ingest data from Google Sheets directly into Snowflake within the Snowflake ecosystem.
• Method 5: Google Sheets Add-ons (No-Code)
  Use Google Sheets add-ons to sync data from Google Sheets to Snowflake directly from the Sheets interface, suitable for lightweight and analyst-driven workflows.

Method 1: Using Hevo Data to Connect Google Sheets to Snowflake

This method uses Hevo’s no-code data pipeline platform to automate the movement of Google Sheets data into Snowflake.

Prerequisites

• The Google Sheets and Google Drive APIs are enabled for the service account if connecting via it.
• You are assigned the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo to create the Pipeline.

Step 1: Set Up a Service Account for Google Sheets

Hevo’s Google Sheets source requires a service account with access to the spreadsheets you want to sync.

• Enable Google Sheets and Google Drive APIs for the project.
• Create a service account in Google Cloud and download the JSON key.
• Share the target Sheets with the service account email so Hevo can read them.

Step 2: Create a New Pipeline in Hevo

1. Log in to your Hevo workspace.
2. Go to the Pipelines section and click + Create Pipeline.

Step 3: Configure Google Sheets as the Source

1. Specify header row settings if needed.
2. Select Google Sheets as your source type.
3. Upload the JSON key for the service account you created earlier.
4. Choose the specific spreadsheet and tabs you want to ingest.

Step 4: Add Snowflake as the Destination

1. Choose Snowflake as the destination type.
2. Provide your Snowflake connection details:

• Account identifier
• Username and authentication method
• Warehouse, database, and schema

3. Hevo will validate the connection and prepare to load data.

Step 5: Configure Pipeline Settings

• Choose sync frequency (continuous or scheduled).
• Enable automatic schema handling so that changes in Sheets columns are reflected in Snowflake tables.
• Map fields or adjust transformations if needed within Hevo’s schema mapper.

Step 6: Start the Pipeline

Activate the pipeline. Hevo will begin extracting data from Google Sheets and loading it into your Snowflake destination automatically, according to the schedule you configured.

Method 2: Manual / Semi-Automated CSV Uploads

This method involves exporting data from Google Sheets as a CSV file and loading it into Snowflake manually or with minimal automation. It is best suited for one-time imports or infrequent updates.

Step 1: Prepare Your Google Sheet

• Ensure the sheet has a single header row.
• Remove merged cells, formulas with errors, and inconsistent data types.
• Confirm column names are clear and analytics-ready.

Step 2: Export Google Sheets as a CSV

1. Open the Google Sheet.
2. Go to File → Download → Comma-separated values (.csv).
3. Save the file locally.

Each worksheet must be downloaded as a separate CSV file.

Step 3: Create or Identify a Target Table in Snowflake

• Create a Snowflake table that matches the CSV structure, or
• Allow Snowflake to infer the schema during loading (optional).

Example considerations:

• Column data types
• Null handling
• Date and timestamp formats

Step 4: Upload the CSV to a Snowflake Stage

You can upload the CSV file to:

• An internal Snowflake stage, or
• An external stage (such as cloud storage)

This is typically done using Snowflake’s PUT command or UI-based upload tools.

Step 5: Load Data Using COPY INTO

Use Snowflake’s COPY INTO command to load data from the stage into the target table.

Key configurations include:

• File format (CSV)
• Field delimiter and header row handling
• Error handling behavior

Step 6: Validate the Load

• Run SQL queries to confirm row counts and data accuracy.
• Check for rejected records or formatting issues.
• Re-run the process if corrections are needed.

Best For

• One-time or ad-hoc data transfers
• Small datasets
• Non-recurring reporting needs

Limitations

• Fully manual for recurring updates
• No automatic sync when Google Sheets data changes
• Higher risk of errors at scale
• Not suitable for production or real-time pipelines

Method 3: Automated / Programmatic Integration (Python Scripts)

To migrate your data from Google Sheets to Snowflake, you may opt for a custom-built data migration script to get the job done. We will demonstrate this process in the next paragraphs. To proceed, you will need the following requirements.

Step 1: Setting Up Google Sheets API Access for Google Sheets

1. Log in to Google Cloud Console.
2. Create a new project (or use an existing one).
3. Navigate to APIs & Services → Library.
4. Enable the following APIs:
   
   • Google Sheets API
   • Google Drive API
     
5. Go to APIs & Services → Credentials.
6. Click Create Credentials → Service Account.

7. Assign a name and create the service account.
8. Generate a JSON key file and download it securely.

This service account will be used by your Python script to read Google Sheets data.

Step 2: Grant the Service Account Access to Google Sheets

1. Open the Google Sheet you want to migrate.
2. Click Share.
3. Add the service account email as a viewer or editor.
4. Repeat this for every sheet you want to migrate.

The service account does not receive emails; ignore any delivery warnings.

Step 3: Prepare the Snowflake Environment

1. Create a Snowflake account (if not already available).
2. Log in to the Snowflake web UI.
3. Ensure you have:
   • A running warehouse
   • A target database
   • A schema for loading data
4. Create the destination table manually or allow schema creation via the script.
5. Assign required privileges:
   • USAGE on warehouse, database, and schema
   • INSERT privileges on target tables

Step 4: Set Up the Python Environment

1. Install Python 3.8+.
2. Create a virtual environment (recommended).
3. Install required libraries:
   • gspread or google-api-python-client for Google Sheets
   • pandas for data processing
   • snowflake-connector-python for Snowflake connectivity
4. Store credentials securely:
   • Google service account JSON file
   • Snowflake credentials via environment variables or secrets manager

Step 5: Extract Data from Google Sheets

1. Authenticate using the service account credentials.
2. Connect to the Google Sheets API.
3. Select the spreadsheet and worksheet.
4. Fetch data including header rows.
5. Load data into a Pandas DataFrame.
6. Perform preprocessing:
   • Normalize column names
   • Handle empty or null values
   • Convert data types as needed

Step 6: Connect to Snowflake

1. Configure Snowflake connection parameters:
   
   • Account identifier
   • User
   • Warehouse
   • Database and schema
     
2. Authenticate securely (password or key-pair authentication).
3. Establish a connection using the Snowflake Python connector.

Step 7: Load Data into Snowflake

There are two common approaches:

Option A: Direct Inserts (Small Datasets)

• Insert DataFrame rows directly using the connector.
• Suitable only for low-volume loads.

Option B: Staged Loads (Recommended)

1. Write the DataFrame to a CSV or Parquet file.
2. Upload the file to a Snowflake stage.
3. Use the COPY INTO command to load data into the target table.
4. Validate row counts and rejected records.

Step 8: Handle Incremental Loads (Optional)

1. Track previously loaded rows using:
   
   • Timestamps
   • Row counts
   • Change markers
     
2. Modify the script to load only new or updated rows.
3. Implement deduplication logic if required.

Step 9: Schedule the Script

1. Use cron jobs, workflow schedulers, or orchestration tools.
2. Define run frequency based on data freshness needs.
3. Ensure retry logic is in place for transient failures.

Step 10: Monitor and Maintain

1. Add logging for:
   
   • API failures
   • Load errors
   • Schema mismatches
     
2. Monitor Google API quotas and Snowflake load performance.
   
3. Update the script when:
   
   • Sheet structure changes
   • Credentials rotate
   • Data volume increases

Best For

• Engineering-led teams
• Businesses requiring custom transformations

Limitations

• High setup and maintenance effort
• Manual handling of schema changes
• Monitoring and scaling are engineering responsibilities
• Slower time to value compared to managed pipelines

Method 4: Using Snowflake’s OpenFlow Connector

Snowflake’s OpenFlow Connector provides a native, low-code way to ingest data from Google Sheets directly into Snowflake. This method is designed for teams that want to manage ingestion entirely within Snowflake, without external ETL platforms or custom scripts.

Step 1: Access Snowflake OpenFlow

1. Log in to your Snowflake account.
2. From the Snowflake UI, navigate to OpenFlow (availability depends on account edition and region).
3. Choose to create a new data flow or connector.

Step 2: Add Google Sheets as a Source

1. Select Google Sheets from the list of available OpenFlow connectors.
2. Authenticate using:
   • Google OAuth, or
   • A service account (depending on configuration options).
3. Grant Snowflake permission to read the required spreadsheet(s).

Step 3: Select the Google Sheets Data

• Choose the specific spreadsheet and worksheet.
• Define the sheet range or tab to ingest.
• Confirm that the sheet includes a header row and consistent column structure.

Step 4: Configure the Snowflake Destination

1. Select the target Snowflake resources:
   • Warehouse
   • Database
   • Schema
2. Choose whether to:
   • Create a new table automatically, or
   • Load data into an existing table.
3. Review the inferred schema and column mappings.

Step 5: Configure Load Settings

• Define how data should be loaded:
  • One-time ingestion, or
  • Scheduled refresh (if supported).
• Choose append or overwrite behavior.
• Review basic error-handling options provided by OpenFlow.

Step 6: Run the OpenFlow Job

• Start the data flow.
• Monitor execution status directly from the OpenFlow interface.
• Review logs for any ingestion or schema-related issues.

Step 7: Validate and Monitor

• Query the target Snowflake table to confirm data was loaded correctly.
• Monitor subsequent runs from the OpenFlow dashboard.
• Update configuration if:
  • Sheet structure changes
  • Destination schema changes

Best For

• Teams are already standardized on Snowflake
• Simple to moderately complex ingestion use cases
• Snowflake-first data architectures

Limitations

• Limited customization compared to Python-based pipelines
• Feature availability depends on OpenFlow maturity and account setup
• Not suited for complex transformations or multi-source workflows

Method 5: Using Google Sheets Add-ons (No-Code)

Google Sheets add-ons allow you to push data from Google Sheets to Snowflake directly from the Sheets interface. This is a no-code approach designed for analysts and business users who want quick, spreadsheet-driven data syncs without building pipelines.

Step 1: Install a Google Sheets Add-on

1. Open the Google Sheet you want to sync.
2. Go to Extensions → Add-ons → Get add-ons.
3. Search for a Snowflake-compatible add-on.
4. Install the add-on from the Google Workspace Marketplace.

Step 2: Authenticate Snowflake Access

• Launch the add-on inside Google Sheets.
• Enter your Snowflake connection details:
  • Account identifier
  • User credentials
  • Warehouse, database, and schema
• Authorize the add-on to access your Snowflake account.

Step 3: Select Source Data

• Choose the worksheet or cell range to sync.
• Confirm that the data has:
  • A single header row
  • Consistent column structure
  • No merged cells

Step 4: Configure the Destination Table

• Select an existing Snowflake table or create a new one.
• Map Google Sheets columns to Snowflake fields.
• Choose whether data should be appended or overwritten.

Step 5: Configure Sync Behavior

• Choose how data should be transferred:
  • One-time push
  • Manual refresh
  • Scheduled sync (if supported by the add-on)
• Some add-ons also support limited bi-directional sync.

Step 6: Run and Validate

• Trigger the sync directly from the Google Sheets UI.
• Query Snowflake to verify data was loaded correctly.
• Review any logs or status messages provided by the add-on.

Best For

• Non-technical users working primarily in Google Sheets
• Small datasets and simple table structures
• Quick, ad-hoc or lightweight scheduled syncs

Limitations

• Not suitable for large datasets or high-frequency updates
• Limited control over schema evolution and error handling
• Scheduling and reliability depend on the add-on
• Not ideal for production-grade or long-term pipelines

Learn More About: How to Connect Google Search Console to Snowflake

What are the benefits of migrating data from Google Sheets to Snowflake?

Teams often start with Google Sheets for flexibility, but move to Snowflake as data and analytics needs grow.

• Scalability limits: Google Sheets is not designed for large datasets, frequent updates, or concurrent analytical queries. Performance degrades as row counts and formulas increase.
• Data reliability issues: Manual edits, broken formulas, and accidental overwrites can lead to inconsistent or inaccurate data, especially in collaborative environments.
• Lack of schema enforcement: Google Sheets does not enforce data types or schemas, making it harder to maintain clean, analytics-ready data over time.
• Limited analytics performance: Complex calculations and joins are slow and difficult to manage in spreadsheets compared to a cloud data warehouse.
• No centralized data layer: Spreadsheet data remains siloed and cannot easily be joined with application, SaaS, or database data sources.
• Manual refresh workflows: Keeping Sheets data up to date in downstream systems often requires repeated exports and uploads.

Snowflake addresses these challenges by providing a scalable, secure, and high-performance data warehouse that supports automated data loading, structured analytics, and enterprise-grade governance.

Conclusion: Choosing the Right Way to Connect Google Sheets to Snowflake

There’s no single best way to move data from Google Sheets to Snowflake, only the method that best fits your scale, frequency, and operational needs.

As data volumes grow and refresh frequency increases, most teams eventually need a solution that is reliable, automated, and easy to operate over time.

This is where Hevo Data fits naturally. Hevo provides a fully managed, no-code ELT pipeline that automates Google Sheets to Snowflake data movement end to end. It handles scheduling, incremental loads, schema changes, and monitoring out of the box, so teams don’t have to maintain scripts, connectors, or cron jobs.

Ready to simplify your Google Sheets to BigQuery data flow? Try Hevo for free and start streaming data in minutes.

FAQs to migrate from Google Sheets to Snowflake