5-minute tutorial

Migrate Oracle Database to Google Sheets in 60 Seconds

Learn how to copy your Oracle Database data to Google Sheets 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 Oracle Database and Google Sheets 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
Oracle Database instance running
Listener configured and running
User with appropriate privileges
TNS configuration (optional)
Google account with Sheets API enabled
Service account created in Google Cloud
Sheet shared with service account email
Sheets API enabled in GCP project

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 Oracle Database to Google Sheets. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Oracle Database connection format:

oracle://username:password@host:port/service_name

Parameters:

• username: Oracle user
• password: User password
• host: Database server
• port: Listener port (default 1521)
• service_name: Service name or SID

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

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 Oracle Database to Google Sheets:

ingestr ingest \
    --source-uri 'oracle://scott:tiger@localhost:1521/ORCL' \
    --source-table 'HR.EMPLOYEES' \
    --dest-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --dest-table 'raw.EMPLOYEES'

What this does:

• Connects to your Oracle Database database
• Reads all data from the specified table
• Creates the table in Google Sheets 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 Sheets:

-- Run this in Google Sheets
SELECT COUNT(*) as row_count 
FROM raw.EMPLOYEES;

-- Check a sample of the data
SELECT * 
FROM raw.EMPLOYEES 
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 'oracle://scott:tiger@localhost:1521/ORCL' \
    --source-table 'public.orders' \
    --dest-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --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 Oracle Database to Google Sheets 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 'oracle://scott:tiger@localhost:1521/ORCL' \
    --source-table 'public.customers' \
    --dest-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --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 'oracle://scott:tiger@localhost:1521/ORCL' \
    --source-table 'public.transactions' \
    --dest-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

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

# Copy sample data to development
ingestr ingest \
    --source-uri 'oracle://scott:tiger@localhost:1521/ORCL' \
    --source-table 'public.products' \
    --dest-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from Oracle Database to Google Sheets.

Connection refused or timeout errors

Check your connection details:

Verify listener is running (lsnrctl status)
Check tnsnames.ora configuration
Ensure service is registered with listener
Verify firewall allows port 1521
Share sheet with service account email
Enable Google Sheets API in GCP
Verify spreadsheet ID from URL
Check service account permissions

Authentication failures

Common authentication issues:

Verify listener is running (lsnrctl status)
Check tnsnames.ora configuration
Ensure service is registered with listener
Verify firewall allows port 1521
Share sheet with service account email
Enable Google Sheets API in GCP
Verify spreadsheet ID from URL
Check service account permissions

Schema or data type mismatches

Handling data type differences:

ingestr automatically handles most type conversions
Oracle Database: NUMBER precision differences
Oracle Database: VARCHAR2 vs VARCHAR
Oracle Database: DATE includes time component
Oracle Database: CLOB/BLOB for large objects
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

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 Oracle Database to Google Sheets with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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