How to Connect Google Sheets to PostgreSQL
Query PostgreSQL databases directly from Google Sheets with AI-powered assistance.
Connecting Google Sheets to PostgreSQL unlocks powerful possibilities: create dashboards that display live database data, enable non-technical team members to query databases using AI, build reports that combine spreadsheet data with production databases, and analyze external data without leaving Google Sheets. ModelMonkey provides native PostgreSQL integration that lets you query databases using SQL, join sheet data with database tables, and leverage AI to help write queries - all without leaving your spreadsheet.
What You'll Need
- A Google Sheets account
- ModelMonkey add-on installed (free trial available)
- Access to a PostgreSQL database (with host, port, database name, username, and password)
- Permission to connect to your PostgreSQL database from external services (firewall/network access)
- Basic understanding of database concepts (tables, columns, queries) - helpful but not required
Step-by-Step Guide
Install ModelMonkey and Open External Connections
Set up ModelMonkey and access the external connections interface.
- If you haven't already, install ModelMonkey: go to Extensions > Add-ons > Get add-ons, search for "ModelMonkey", and click Install
- Open your Google Sheet and launch ModelMonkey: Extensions > ModelMonkey > Open
- In the ModelMonkey sidebar, click the settings icon (⚙️) or menu button
- Select "External Connections" or "Manage Connections" from the menu
- You'll see a list of your existing connections (empty if this is your first time) and a "New Connection" button
Pro Tip
ModelMonkey's external connections are user-scoped, meaning each user sets up their own database credentials. This keeps sensitive connection information private and secure.
Create a New PostgreSQL Connection
Add your PostgreSQL database credentials to ModelMonkey.
- Click the "New Connection" button to launch the connection wizard
- Enter a friendly name for your connection (e.g., "Production DB" or "Analytics Database")
- Select "PostgreSQL" as the connection type
- Fill in your database details: Host (e.g., db.example.com), Port (usually 5432), Database name, Schema (optional, defaults to "public")
- Enter your database credentials: Username and Password
- Optionally, select SSL mode (recommend "require" for production databases)
- Click "Next" to proceed to connection testing
Pro Tip
Always use read-only database credentials when connecting to production databases. Create a dedicated PostgreSQL user with SELECT-only permissions to prevent accidental data modifications.
Test and Verify the Connection
Ensure ModelMonkey can successfully connect to your PostgreSQL database.
- On the connection test screen, review your connection details to ensure accuracy
- Click "Test Connection" - ModelMonkey will attempt to connect to your database
- If successful, you'll see a green checkmark and confirmation message showing the PostgreSQL server version and available schemas
- If the test fails, you'll see an error message explaining what went wrong (authentication failed, network timeout, host unreachable, etc.)
- For failed connections, double-check your credentials, ensure your firewall allows external connections, and verify the host and port are correct
- Once the test succeeds, click "Next" to proceed to final setup
Pro Tip
Common connection issues: firewall blocking external connections, incorrect host/port, wrong credentials, or PostgreSQL not configured to accept remote connections. Check your pg_hba.conf and postgresql.conf files.
Complete Setup and Activate Your Connection
Finalize the connection and make it ready to use.
- On the final wizard screen, review the connection summary
- ModelMonkey will display your connection alias - a short identifier like "conn_a1b2c3d4" that uniquely identifies your connection
- This alias is automatically generated and stored for you
- Click "Create Connection" to save your connection - it will be activated immediately
- You'll be returned to the connections list where you can see your new connection marked as "Active"
- Your connection is now ready to use - simply ask for data in natural language and ModelMonkey will handle the rest
Pro Tip
Your connection alias is stored automatically - you won't need to reference it manually since ModelMonkey handles database connections behind the scenes when you make natural language requests.
Query PostgreSQL Data Using Natural Language
Import data from PostgreSQL into Google Sheets by simply asking for what you need.
- In the ModelMonkey chat interface, describe what data you want in plain English
- For example: "Show me the first 10 users from the users table" or "Get all orders from last month"
- ModelMonkey automatically generates the SQL query behind the scenes using your connection
- You'll see a preview of what the query will do before it executes
- Review and approve the query - results will be displayed directly in your Google Sheet
- No need to know SQL syntax, connection aliases, or schema names - just describe what you want
Pro Tip
Start with simple requests like "Show me 10 rows from the orders table" to verify the connection works. You can then ask for more complex data like filtered results, date ranges, or aggregations.
Join Google Sheets Data with PostgreSQL Tables
Combine spreadsheet data with database data using natural language.
- ModelMonkey automatically detects tables and ranges in your current sheet - no need to specify cell ranges manually
- You can also select any range directly in your Google Sheet, and ModelMonkey will reference it in queries
- Simply describe what you want in plain English: "Join my Orders data with the users table in PostgreSQL to add email addresses"
- The AI assistant will automatically identify the sheet data, construct the appropriate query, and determine the right join conditions
- Example request: "Enrich my customer list with email addresses from the PostgreSQL customers table"
- ModelMonkey handles all the technical details: detecting your sheet data, referencing the correct PostgreSQL table, identifying matching columns, and handling data type conversions
Pro Tip
You don't need to know SQL syntax or cell ranges. Just select your data in Google Sheets (or let ModelMonkey detect it automatically) and describe what you want to achieve in plain English.
Use AI to Help Write Complex Queries
Leverage ModelMonkey's AI assistant to generate SQL queries in natural language.
- Instead of writing SQL yourself, describe what you want in plain English
- Example: "Show me the top 10 customers by total purchase amount from the orders table, along with their contact information from the customers table"
- The AI will generate the appropriate query with JOINs, aggregations (SUM, COUNT), GROUP BY, and ORDER BY clauses
- For filtering: "Get all orders from last month where the total was over $500"
- For aggregations: "Calculate the average order value by customer segment from the database"
- Review the proposed query, ask for modifications if needed, then approve to execute
Pro Tip
The AI assistant understands your database schema and can suggest relevant tables and columns. It's perfect for users who understand data but aren't SQL experts.
Easily Extract and Refresh Data from PostgreSQL
Pull the latest data from your database whenever you need it.
- Once you've successfully queried your PostgreSQL database, extracting fresh data is simple
- Just ask ModelMonkey to run the same query again: "Get the latest orders data" or "Refresh the customer list"
- The AI assistant remembers your previous queries and can re-run them with updated data
- You can also modify queries on the fly: "Show me the same data but only for last week"
- Each time you run a query, it pulls the current state of data from PostgreSQL
- This makes it easy to keep your spreadsheets up-to-date with your production database
Pro Tip
Save frequently-used queries by documenting them in a separate sheet tab for easy reference. You can simply copy the natural language request and paste it into ModelMonkey chat whenever you need fresh data.
Explore Advanced Query Patterns
Learn powerful query techniques for data analysis and reporting.
- Create summary reports: "Create a monthly sales summary from the orders table, grouped by product category"
- Time-series analysis: "Show daily revenue trends for the last 90 days from PostgreSQL"
- Data validation: "Highlight any customers in my sheet who don't exist in the PostgreSQL customers table"
- Incremental updates: "Import only orders created since yesterday using WHERE created_at > CURRENT_DATE - 1"
- Multi-table joins: "Combine orders, customers, and products tables to create a complete order report"
- Use CASE statements for categorization: "Label orders as Small, Medium, or Large based on total amount"
Pro Tip
DuckDB (the SQL engine ModelMonkey uses) supports almost all PostgreSQL query syntax, plus additional analytical functions. It's extremely fast at processing both database and sheet data together.
Manage and Secure Your Connections
Best practices for maintaining secure and reliable database connections.
- Regularly review your connections in the External Connections manager
- Update credentials when they change: click a connection, select "Edit", and update the password
- Remove unused connections to reduce security exposure
- Test connections periodically: click "Test Connection" to verify they're still working
- Use different connections for different environments (Production, Staging, Development)
- Monitor query performance: if queries become slow, consider adding indexes in PostgreSQL or filtering data more aggressively
- Never share connection credentials: each user should set up their own connections with appropriate permissions
Pro Tip
Enable audit logging in PostgreSQL to track which queries are being run from Google Sheets. This helps with compliance and security monitoring.
