5-minute tutorial

Migrate Microsoft SQL Server to Google BigQuery in 60 Seconds

Learn how to copy your Microsoft SQL Server data to Google BigQuery 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 Microsoft SQL Server and Google BigQuery 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
  • 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
  • Google Cloud project with BigQuery API enabled
  • Service account with BigQuery Data Editor and Job User roles
  • Downloaded service account JSON key file
  • Dataset created in BigQuery (or permissions to create one)

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 Microsoft SQL Server to Google BigQuery. This example shows a complete, working command you can adapt to your needs.

Set up your connections

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

Google BigQuery connection format:

bigquery://project-id?credentials_path=/path/to/service-account.json

Parameters:

  • • project-id: Your Google Cloud project ID
  • • credentials_path: Path to service account JSON key file
  • • location: Optional dataset location (e.g., US, EU)

BigQuery Setup Required

Before running the command:

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

Run your first copy

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

ingestr ingest \
    --source-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --source-table 'dbo.Customers' \
    --dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --dest-table 'raw.Customers'

What this does:

  • • Connects to your Microsoft SQL Server database
  • • Reads all data from the specified table
  • • Creates the table in Google BigQuery 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 Google BigQuery:

-- Run this in BigQuery console
SELECT COUNT(*) as row_count 
FROM `raw.Customers`;

-- Check a sample of the data
SELECT * 
FROM `raw.Customers` 
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 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --source-table 'public.orders' \
    --dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --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 Microsoft SQL Server to Google BigQuery 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 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --source-table 'public.customers' \
    --dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --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 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --source-table 'public.transactions' \
    --dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development Google BigQuery instance (with sensitive data excluded). 

# Copy sample data to development
ingestr ingest \
    --source-uri 'mssql://sa:MyPass123@localhost:1433/AdventureWorks' \
    --source-table 'public.products' \
    --dest-uri 'bigquery://dev-project?credentials_path=/path/to/credentials.json' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

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

Connection refused or timeout errors

Check your connection details:

  • 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
  • Verify the project ID matches your Google Cloud project
  • Ensure the service account has necessary permissions
  • Check if BigQuery API is enabled in your project
  • Confirm the credentials file path is absolute, not relative
Authentication failures

Common authentication issues:

  • 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
  • Verify the project ID matches your Google Cloud project
  • Ensure the service account has necessary permissions
  • Check if BigQuery API is enabled in your project
  • Confirm the credentials file path is absolute, not relative
Schema or data type mismatches

Handling data type differences:

  • ingestr automatically handles most type conversions
  • 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
  • Google BigQuery: JSON/JSONB types become STRING (use JSON functions to query)
  • Google BigQuery: Arrays are converted to JSON arrays
  • Google BigQuery: TIMESTAMP types are preserved with timezone information
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 Microsoft SQL Server to Google BigQuery with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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