5-minute tutorial

Migrate Google Sheets to Microsoft SQL Server in 60 Seconds

Learn how to copy your Google Sheets data to Microsoft SQL Server with a single command using ingestr - no code required.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds

Connect to Google Sheets and Microsoft SQL Server with proper authentication

Copy entire tables or specific data with a single command

Set up incremental loading for continuous data synchronization

Prerequisites

Python 3.8 or higher installed
Google account with Sheets API enabled
Service account created in Google Cloud
Sheet shared with service account email
Sheets API enabled in GCP project
SQL Server instance running
SQL Server Authentication or Windows Authentication configured
TCP/IP protocol enabled in SQL Server Configuration Manager
Firewall rules for port 1433

Step 1: Install ingestr

Install ingestr in seconds using pip. Choose the method that works best for you:

Recommended: Using uv (fastest)

# Install uv first if you haven't already
pip install uv

# Run ingestr using uvx
uvx ingestr

Alternative: Global installation

# Install globally using uv
uv pip install --system ingestr

# Or using standard pip
pip install ingestr

Verify installation: Run ingestr --version to confirm it's installed correctly.

Step 2: Your First Migration

Let's copy a table from Google Sheets to Microsoft SQL Server. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Google Sheets connection format:

googlesheets://credentials_path@spreadsheet_id/sheet_name

Parameters:

• credentials_path: Service account JSON file
• spreadsheet_id: ID from sheet URL
• sheet_name: Name of specific sheet tab

Microsoft SQL Server connection format:

mssql://username:password@host:port/database

Parameters:

• username: SQL Server login
• password: Login password
• host: Server name or IP
• port: Port number (default 1433)
• database: Database name
• encrypt: Use encryption (true/false)
• trustServerCertificate: Trust certificate

BigQuery Setup Required

Before running the command:

Create a service account in Google Cloud Console
Grant it BigQuery Data Editor and Job User roles
Download the JSON key file
Use the path to this file in your connection string

Run your first copy

Copy the entire users table from Google Sheets to Microsoft SQL Server:

ingestr ingest \
    --source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --source-table 'Sheet1' \
    --dest-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --dest-table 'raw.Sheet1'

What this does:

• Connects to your Google Sheets database
• Reads all data from the specified table
• Creates the table in Microsoft SQL Server if needed
• Copies all rows to the destination

Command breakdown:

--source-uri Your source database
--source-table Table to copy from
--dest-uri Your destination
--dest-table Where to write data

Step 3: Verify your data

After the migration completes, verify your data was copied correctly:

Check row count in Microsoft SQL Server:

-- Run this in Microsoft SQL Server
SELECT COUNT(*) as row_count 
FROM raw.Sheet1;

-- Check a sample of the data
SELECT * 
FROM raw.Sheet1 
LIMIT 10;

Advanced Patterns

Once you've mastered the basics, use these patterns for production workloads.

Only copy new or updated records since the last sync. Perfect for daily updates.

ingestr ingest \
    --source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --source-table 'public.orders' \
    --dest-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key order_id

How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at has changed will be processed.

Common Use Cases

Ready-to-use commands for typical Google Sheets to Microsoft SQL Server scenarios.

Daily Customer Data Sync

Keep your analytics warehouse updated with the latest customer information every night.

# Add this to your cron job or scheduler
ingestr ingest \
    --source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --source-table 'public.customers' \
    --dest-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --dest-table 'analytics.customers' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key customer_id

Historical Data Migration

One-time migration of all historical records to your data warehouse.

# One-time full table copy
ingestr ingest \
    --source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --source-table 'public.transactions' \
    --dest-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development Microsoft SQL Server instance (with sensitive data excluded).

# Copy sample data to development
ingestr ingest \
    --source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --source-table 'public.products' \
    --dest-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from Google Sheets to Microsoft SQL Server.

Connection refused or timeout errors

Check your connection details:

Share sheet with service account email
Enable Google Sheets API in GCP
Verify spreadsheet ID from URL
Check service account permissions
Enable TCP/IP in SQL Server Configuration Manager
Check SQL Server Browser service is running
Verify Windows Firewall allows SQL Server
Ensure Mixed Mode authentication if using SQL login

Authentication failures

Common authentication issues:

Share sheet with service account email
Enable Google Sheets API in GCP
Verify spreadsheet ID from URL
Check service account permissions
Enable TCP/IP in SQL Server Configuration Manager
Check SQL Server Browser service is running
Verify Windows Firewall allows SQL Server
Ensure Mixed Mode authentication if using SQL login

Schema or data type mismatches

Handling data type differences:

ingestr automatically handles most type conversions
Google Sheets: All data is text by default
Google Sheets: Date formatting varies by locale
Google Sheets: Number formatting affects parsing
Google Sheets: Formula cells vs values
Microsoft SQL Server: NVARCHAR for Unicode support
Microsoft SQL Server: Hierarchyid for tree structures
Microsoft SQL Server: Spatial data types for geographic data
Microsoft SQL Server: XML and JSON support

Performance issues with large tables

Optimize large data transfers:

Use incremental loading to process data in chunks
Run migrations during off-peak hours
Split very large tables by date ranges using interval parameters

Ready to scale your data pipeline?

You've learned how to migrate data from Google Sheets to Microsoft SQL Server with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

Star ingestr on GitHub