Excel / Google Sheets to SQL

Select cells in your spreadsheet, press Ctrl+C, paste here, and get SQL INSERT statements instantly. Auto-detects column types.

1Copy cells from spreadsheet

2Paste into the input box

3Copy or download the SQL

Table name

Include CREATEInclude DROP

Spreadsheet data

6 cols · 4 rows

Ctrl+C from Excel or Google Sheets, then paste here

SQL output

DROP TABLE IF EXISTS "my_table";

CREATE TABLE "my_table" (
  "id" INTEGER,
  "name" TEXT,
  "email" TEXT,
  "age" INTEGER,
  "country" TEXT,
  "active" BOOLEAN
);

INSERT INTO "my_table" ("id", "name", "email", "age", "country", "active")
VALUES
  (1, 'Alice Johnson', '[email protected]', 28, 'USA', TRUE),
  (2, 'Bob Smith', '[email protected]', 34, 'UK', FALSE),
  (3, 'Charlie Brown', '[email protected]', 22, 'Canada', TRUE),
  (4, 'Diana Prince', '[email protected]', 31, 'Australia', TRUE);

Data preview (first 4 of 4 rows)

idINTEGER	nameTEXT	emailTEXT	ageINTEGER	countryTEXT	activeBOOLEAN
1	Alice Johnson	[email protected]	28	USA	true
2	Bob Smith	[email protected]	34	UK	false
3	Charlie Brown	[email protected]	22	Canada	true
4	Diana Prince	[email protected]	31	Australia	true

How to Convert Excel or Google Sheets Data to SQL

When you copy cells in Microsoft Excel or Google Sheets, the clipboard contains tab-separated values (TSV) — one row per line, columns separated by tabs. This tool parses that format directly, so there is no need to export a CSV file first.

Step-by-step guide

Open your spreadsheet and select the cell range including the header row.
Press Ctrl+C (Windows / Linux) or Cmd+C (Mac).
Click inside the input area above and press Ctrl+V to paste.
Set your target table name and toggle CREATE / DROP as needed.
Copy the generated SQL with the Copy button or download it as a .sql file.

Automatic type detection

The tool scans every value in each column and applies the most specific SQL type that fits all non-empty cells:

Detected pattern	SQL type	Example values
All whole numbers	`INTEGER`	1, 42, -7
All decimal numbers	`REAL`	3.14, 0.5, -1.2
true / false / yes / no / 1 / 0	`BOOLEAN`	true, FALSE, yes, 0
Anything else	`TEXT`	Alice, 2024-01-15, N/A

Empty cells are always emitted as NULL regardless of column type.

Batched INSERT statements

For efficiency, INSERT statements use multi-row VALUES syntax with up to 100 rows per statement. This is significantly faster than one INSERT per row, especially in PostgreSQL and MySQL.

INSERT INTO "users" ("id", "name", "email")
VALUES
  (1, 'Alice Johnson', '[email protected]'),
  (2, 'Bob Smith', '[email protected]'),
  (3, 'Charlie Brown', '[email protected]');

Handling special characters

Single quotes inside text values are automatically escaped by doubling them (O'Brien → 'O''Brien'), following the ANSI SQL standard supported by PostgreSQL, MySQL, and SQLite.

Google Sheets tips

Formatted numbers: If a cell shows 1,234 but the underlying value is 1234, the clipboard sends the unformatted number. Type detection will correctly classify it as INTEGER.
Date columns: Dates are typically pasted as text (e.g. 2024-01-15) and will be typed as TEXT. Cast them in SQL: CAST(created_at AS DATE).
Merged cells: Avoid merging cells before copying — merged cells paste as empty values in the non-primary cells.

Running the generated SQL

After downloading the .sql file, import it with your database client:

-- PostgreSQL
psql -U postgres -d mydb -f users.sql

-- MySQL
mysql -u root -p mydb < users.sql

-- SQLite
sqlite3 mydb.db < users.sql

Use this tool as part of an import workflow

Spreadsheet imports are usually messy at the edges. The goal here is to get a reliable first SQL script quickly, then refine the model once the data is safely inside a database.

Move spreadsheet data into a dev database

Fastest path when product, ops, or marketing hands you a sheet and you just need rows in SQL.

1Copy the spreadsheet range including the header row.
2Paste it here and verify the detected column names and types in the preview table.
3Run the generated SQL in a local or staging database before touching production.

Prepare a one-off seed script

Useful for demos, QA fixtures, and ad hoc imports that should still be reproducible.

1Set a stable table name and keep CREATE or DROP enabled if you want a resettable script.
2Download the generated SQL and commit it with the spreadsheet source when needed.
3Use Schema Diff later if the table shape changes and you need to compare versions.

Import notes that matter in practice

Most spreadsheet-to-SQL problems are not parser bugs. They come from inconsistent headers, human formatting, and dirty columns. These are the checks worth making before you run the script.

Headers become SQL column names

If the first row contains labels like "Customer Name" or "Order Total ($)", those exact values become quoted SQL identifiers. Clean headers first if you want simpler schema names.

Dates usually arrive as text

Excel and Google Sheets often paste formatted dates as strings. The tool keeps them safe as TEXT; cast them later if your target schema expects DATE or TIMESTAMP.

Preview catches type mistakes early

Use the preview table to spot columns that were inferred as TEXT because of a stray value like N/A, -, or a formatted currency string.

Frequently Asked Questions

Choose the next guide based on what the spreadsheet import exposed

A paste-to-SQL tool gets the rows into the database quickly. These paths connect that first import to the modeling or cleanup work that usually follows.

Spreadsheet import followed by cleanup and typing

Use this path when copied cells need trimming, casting, and stronger type decisions after the initial SQL script is generated.

Data Cleaning with SQL SQL Data Types

The sheet is really the first version of a schema

Relevant when a copied worksheet is acting as an informal data model and needs to be reshaped into tables and relationships.

Designing Your First Database Schema SQL Table Relationships Explained

Imports for demos, QA, and non-production data

Choose this route when the spreadsheet is being turned into reusable fixtures rather than a one-time manual load.

Generating Test Data with SQL Data Masking and Anonymization

Learn the concepts behind cleaner imports

These guides help when the spreadsheet is only the first step and the real work is cleaning, typing, and modeling the data after import.

Data Cleaning with SQL

Best follow-up when pasted spreadsheet data needs trimming, casting, or deduplication after import.

SQL Data Types

Helpful when the inferred INTEGER, REAL, BOOLEAN, and TEXT types need to be tightened for a real schema.

Generating Test Data with SQL

Useful when spreadsheet imports are part of a broader fixture or seeding workflow.

Designing Your First Database Schema

Helpful when a spreadsheet import is the first draft of a table design rather than the final schema you want to keep.