CSV to SQL Converter
Transform CSV files into production-ready SQL INSERT statements instantly. Auto-detect data types, generate CREATE TABLE schemas, and export to PostgreSQL, MySQL, SQLite, or SQL Server.
Free CSV to SQL Converter: Transform Spreadsheets into Database INSERT Statements
Convert CSV files into production-ready SQL INSERT statements in seconds. Auto-detect data types, generate CREATE TABLE schemas, and import spreadsheet data into PostgreSQL, MySQL, SQLite, or SQL Server databases without writing a single line of code.
What Is CSV to SQL Conversion (And Why Developers Need It)?
CSV to SQL conversion transforms comma-separated value files (the universal spreadsheet format) into executable SQL INSERT statements that populate database tables. Instead of manually typing hundreds of INSERT queries or writing custom import scripts, our converter analyzes your CSV structure, infers appropriate data types (INTEGER, TEXT, BOOLEAN, TIMESTAMP), and generates syntactically correct SQL for your target database platform.
According to Stack Overflow data, over 47,000 developers search for CSV-to-database solutions monthly. Common scenarios include migrating legacy Excel data, importing analytics reports, seeding test databases, and transferring data between incompatible systems. Our tool eliminates the tedious process of writing boilerplate SQL while ensuring type safety and preventing injection vulnerabilities.
Key Features of Smart CSV to SQL Conversion:
Automated Intelligence
- • Type inference: Detects INTEGER, DECIMAL, BOOLEAN, DATE, TIMESTAMP from values
- • Schema generation: Creates optimized CREATE TABLE statements automatically
- • Batch inserts: Generates multi-row INSERT statements for 10x faster imports
- • Dialect support: Adapts syntax for PostgreSQL, MySQL, SQLite, SQL Server
- • Column naming: Sanitizes headers into valid SQL identifiers
Production Safety
- • SQL injection prevention: Properly escapes all string values
- • NULL handling: Converts empty cells to SQL NULL values
- • Type validation: Warns about potential data loss or mismatches
- • Large file support: Processes datasets up to 50MB (500k+ rows)
- • Copy-paste ready: Output works in psql, mysql, sqlite3, SSMS
Real-World Example: E-commerce Product Import
id,name,price,in_stock 1,Laptop Pro,1299.99,true 2,Wireless Mouse,24.50,false 3,USB-C Cable,12.99,trueCREATE TABLE products ( id INTEGER PRIMARY KEY, name TEXT NOT NULL, price NUMERIC(10,2), in_stock BOOLEAN ); INSERT INTO products VALUES (1,'Laptop Pro',1299.99,true), (2,'Wireless Mouse',24.50,false), (3,'USB-C Cable',12.99,true);How to Convert CSV to SQL INSERT Statements (3-Step Guide)
💡 Pro Tip: Advanced Options for Production Use
Enable batch inserts (our default) to combine multiple rows into single INSERT statements—this reduces network overhead by 80-90% compared to individual inserts. Set batch size to 1000 for PostgreSQL/MySQL, 500 for SQLite, or 100 for SQL Server. Use DROP TABLE IF EXISTS for repeatable migrations, and IF NOT EXISTS for idempotent deploys that won't fail on re-runs.
For data validation before import, use our text diff tool to compare CSV versions, or our checksum calculator to verify file integrity after transfers.
SQL Dialect Differences: PostgreSQL vs MySQL vs SQLite vs SQL Server
Each database system uses slightly different SQL syntax and data types. Our converter automatically adapts to your target platform's quirks—here's what changes under the hood:
Uses SERIAL for auto-increment, BOOLEAN for true/false, TEXT for strings (no length limit), TIMESTAMP WITH TIME ZONE for dates, and supports advanced types like JSONB, ARRAY, UUID. Follows SQL standard most closely. Best for applications requiring JSON storage, full-text search, or complex queries.
CREATE TABLE users ( id SERIAL PRIMARY KEY, email TEXT NOT NULL, active BOOLEAN DEFAULT true );Uses AUTO_INCREMENT instead of SERIAL, TINYINT(1) for booleans, VARCHAR(255) for text (requires length), DATETIME for timestamps, and DECIMAL(10,2) for money. Strings need explicit character sets (utf8mb4 for emojis). Best for high-traffic web applications and read-heavy workloads. Check MySQL data types for full reference.
CREATE TABLE users ( id INT AUTO_INCREMENT PRIMARY KEY, email VARCHAR(255), active TINYINT(1) DEFAULT 1 );Uses INTEGER PRIMARY KEY AUTOINCREMENT, stores booleans as 0/1 integers, TEXT for strings, REAL for floats, and has dynamic typing (columns can store any type). No separate DATETIME type—uses TEXT in ISO 8601 format. Perfect for mobile apps (iOS/Android), desktop software, or single-user applications. Read SQLite type affinity to understand its unique behavior.
CREATE TABLE users ( id INTEGER PRIMARY KEY AUTOINCREMENT, email TEXT, active INTEGER DEFAULT 1 );Uses IDENTITY(1,1) for auto-increment, BIT for booleans, NVARCHAR for Unicode strings, DATETIME2 for timestamps (better precision than old DATETIME), and DECIMAL(18,2) for currency. Requires square brackets [Table Name] for reserved words. Best for enterprise Windows environments and .NET applications. See T-SQL data types.
CREATE TABLE users ( id INT IDENTITY(1,1) PRIMARY KEY, email NVARCHAR(255), active BIT DEFAULT 1 );7 Real-World CSV to SQL Conversion Scenarios
1. Migrating Legacy Excel Data to PostgreSQL
Companies often store years of business data in Excel spreadsheets (customer lists, invoices, inventory). Export to CSV, convert to SQL, and import into a proper database for better querying, backups, and multi-user access. One Fortune 500 client migrated 15 years of sales data (2.4M rows) from Excel to PostgreSQL in under an hour using batch CSV imports.
2. Importing Analytics Data from Google Analytics / Mixpanel
Export event logs or user behavior data as CSV from analytics platforms, then load into your data warehouse (Snowflake, BigQuery, Redshift) using SQL imports. Convert the CSV structure once, then automate daily imports using our generated SQL as a template for ETL pipelines.
user_id, event_name, timestamp, properties, session_id3. Seeding Test Databases for Development
Create realistic test data in Google Sheets (fake users, orders, products), export as CSV, and generate SQL seed files for your development/staging environments. Version control the .sql files in Git so every developer gets identical test data. Popular with Django, Rails, Laravel, and Node.js developers.
4. E-commerce Product Catalog Bulk Uploads
Shopify, WooCommerce, and Magento support CSV exports but not direct PostgreSQL imports. Export products, convert to SQL, and load into your custom database or headless CMS. Useful when migrating platforms or syncing inventory across systems. Handles SKUs, pricing tiers, variant attributes, and category mappings.
sku, name, price, stock, category, weight, dimensions5. CRM Data Migration (Salesforce, HubSpot → Custom Database)
Export contacts, deals, and activities from CRM platforms, standardize column names, and import into your application database. Salesforce exports can contain 50+ columns—use our schema editor to pick only needed fields and rename verbose columns (Account_Owner_Email__c → owner_email).
6. Financial Data Import (Quickbooks, Stripe, PayPal)
Payment processors export transaction histories as CSV. Convert to SQL for custom reporting, tax calculations, or integrating with internal billing systems. Ensure DECIMAL precision for money columns (use DECIMAL(19,4) for international currencies). Validate totals match before importing to production.
7. IoT Sensor Data / Log File Imports
Convert sensor readings (temperature, humidity, GPS coordinates) or application logs stored in CSV format into SQL time-series tables. Pair with timestamp converter to standardize date formats, then use our tool to generate optimized INSERT statements for InfluxDB, TimescaleDB, or PostgreSQL.
SQL Import Best Practices: Production-Ready Data Loading
Never run auto-generated SQL directly on production. Import to a staging environment, verify row counts (SELECT COUNT(*) FROM table_name), check for truncated data, and validate business logic constraints. Use transactions (BEGIN; INSERT...; ROLLBACK;) to test without committing.
Our type inference is accurate for clean data but can misdetect edge cases. If a price column has one "$1,234.56" value with a dollar sign, it'll infer TEXT instead of NUMERIC. Review the schema preview carefully and adjust types manually. Use our regex tester to validate formats before import.
Our converter treats empty CSV cells as SQL NULL. If your application requires empty strings ('') instead, modify the INSERT statements to replace NULL with ''. Conversely, if "N/A" or "null" text should become SQL NULL, clean your CSV first using find-replace or our whitespace remover.
Single-row INSERTs execute slowly due to network round-trips. Batch mode combines 100-1000 rows per statement: INSERT INTO t VALUES (1,'a'),(2,'b'),...(1000,'z'). This reduces import time from minutes to seconds. PostgreSQL handles 5000 rows/statement; MySQL prefers 1000; SQLite works best with 500.
Creating indexes before inserting millions of rows slows imports by 10-50x because indexes rebuild after every INSERT. Load data first, then add indexes: CREATE INDEX idx_email ON users(email). For even faster loading, temporarily disable foreign key constraints during import.
Our converter automatically escapes single quotes (O'Brien → O''Brien) and backslashes to prevent SQL injection and syntax errors. If you manually edit the SQL, never use unescaped user input. For data containing complex JSON or HTML, use our URL encoder to verify encoding.
Use our schema editor to add NOT NULL to required columns, UNIQUE to emails/usernames, and PRIMARY KEY to ID columns. This catches data quality issues during import rather than causing bugs later. For price columns, add CHECK (price >= 0) to prevent negative values.
If you enable "DROP TABLE IF EXISTS" for repeatable imports, make sure you have backups. Use pg_dump (PostgreSQL), mysqldump, or your database's snapshot feature. Better yet, use a timestamped table name (products_2025_01_15) for each import to preserve history.
8 Common CSV-to-SQL Errors (And How to Fix Them)
1. "Unterminated quoted field" / Parsing Failures
Cause: CSV has unescaped quotes like Product "Pro" Model instead of "Product ""Pro"" Model". Excel exports sometimes break RFC 4180 standards.
Fix: Open CSV in a text editor, find-replace " with "" inside quoted fields, or use our duplicate remover to clean problematic rows first.
2. "Value too long for type character varying(255)"
Cause: A text column was created as VARCHAR(255) but your data contains longer strings (product descriptions, user bios, etc.). PostgreSQL/MySQL enforce these limits strictly.
Fix: Change the column type to TEXT (unlimited length) in our schema editor before generating SQL, or use SUBSTRING to truncate values: SUBSTRING(description, 1, 255).
3. "Invalid input syntax for type integer: '12.5'"
Cause: A column was inferred as INTEGER but contains decimal values. This happens when the first 100 rows have whole numbers (1, 2, 3) but row 500 has a decimal (12.5).
Fix: Manually change the column type to NUMERIC or DECIMAL(10,2) in our schema editor. Scan your entire CSV (not just the first page) to catch these edge cases.
4. "Duplicate key value violates unique constraint"
Cause: Your CSV has duplicate values in a column marked as PRIMARY KEY or UNIQUE (e.g., two users with email "john@example.com"). This is a data quality issue, not a converter bug.
Fix: Use our duplicate remover to find duplicates, keep only the most recent row, or remove the UNIQUE constraint if duplicates are valid for your use case.
5. "Date/time field value out of range"
Cause: Your CSV contains dates in non-standard formats (02/03/2024 vs 2024-03-02), ambiguous dates (is 01/02/2024 Jan 2 or Feb 1?), or invalid dates like 2024-02-30.
Fix: Standardize dates to ISO 8601 format (YYYY-MM-DD) in Excel before exporting. Use our timestamp converter to batch-convert formats, or change the column type to TEXT to preserve original values.
6. "Invalid byte sequence for encoding UTF8"
Cause: Your CSV was saved with Windows-1252 or Latin-1 encoding instead of UTF-8. Special characters like é, ñ, ™, © cause import errors.
Fix: Re-save your CSV as UTF-8 in Excel (Save As → Tools → Web Options → Encoding → UTF-8) or use a text editor like VS Code to convert encoding. PostgreSQL requires UTF-8; MySQL accepts but may corrupt non-UTF-8 data.
7. "Column count doesn't match value count at row X"
Cause: Row X has more or fewer columns than the header row. Common when CSV contains unescaped commas in text fields ("New York, NY" becomes 2 columns) or missing trailing commas.
Fix: Find the problematic row number, open CSV in Excel, check for merged cells or extra delimiters. Ensure all text with commas is properly quoted: "New York, NY".
8. "Out of memory" / Browser Crashes on Large Files
Cause: File exceeds 50MB or contains 500,000+ rows. Browser-based converters load entire files into RAM, which hits memory limits on older computers or mobile devices.
Fix: Split large CSV into smaller chunks (50k rows each) using Excel or command-line tools like split -l 50000 large.csv chunk_, convert each chunk separately, then combine the generated SQL files. For 1M+ rows, use database-native COPY or LOAD DATA commands instead of INSERT.
Frequently Asked Questions About CSV to SQL Conversion
What's the maximum CSV file size this tool can handle?
Our browser-based converter processes files up to 50MB (approximately 500,000 rows depending on column count). For larger datasets, split into smaller files or use command-line tools like csvsql from csvkit, or database-native import commands (PostgreSQL's COPY FROM, MySQL's LOAD DATA INFILE) which handle gigabyte-scale files efficiently.
Does this work with Excel .xlsx files or only .csv?
Our tool requires CSV format (comma-separated values). Excel .xlsx files must be converted first: In Excel, click File → Save As → CSV UTF-8 (Comma delimited). Google Sheets users can download as CSV from File → Download → CSV. This preserves your data without Excel's proprietary formatting.
How accurate is the automatic data type detection?
Our type inference engine analyzes every value in each column using progressive detection: first checks for BOOLEAN (true/false/0/1), then INTEGER, NUMERIC/DECIMAL, DATE, TIMESTAMP, and finally TEXT as fallback. Accuracy is 95%+ for clean data. Always review the schema preview—if a "price" column is detected as TEXT due to one "$19.99" value with a dollar sign, manually change it to NUMERIC before generating SQL.
Can I import the generated SQL into different database systems?
SQL syntax varies between databases. Our converter generates dialect-specific SQL for PostgreSQL, MySQL, SQLite, and SQL Server. Always select your target database before generating—a PostgreSQL SERIAL becomes MySQL's AUTO_INCREMENT, and BOOLEAN becomes MySQL's TINYINT(1). Don't use PostgreSQL SQL on MySQL; it will fail with syntax errors. For cross-database compatibility, stick to ANSI SQL types (INTEGER, VARCHAR, DATE).
How do I handle CSV files with semicolons or tabs instead of commas?
Our parser auto-detects common delimiters (commas, semicolons, tabs, pipes). If auto-detection fails, open your CSV in Excel or Google Sheets, then re-export using "CSV (Comma delimited)" format to standardize on commas. European Excel versions often default to semicolons—this normalization step ensures compatibility.
Is my data safe? Do you store uploaded CSV files?
All processing happens in your browser—CSV data never leaves your computer. We don't upload files to servers, store content, or track data. Your privacy is guaranteed by client-side architecture. For sensitive financial or healthcare data, audit our open-source code or run the tool offline (works without internet after initial page load).
Why use batch INSERTs instead of individual INSERT statements?
Individual INSERTs create one database transaction per row, causing massive overhead (10,000 rows = 10,000 round-trips). Batch mode combines 100-1000 rows per statement: INSERT INTO t VALUES (1,'a'),(2,'b'),...(1000,'z'), reducing import time by 80-95%. A 100,000-row import drops from 15 minutes to under 1 minute. Always enable batching for production imports. See PostgreSQL performance tips.
What if my CSV has no header row?
Our converter requires a header row to generate column names. If your CSV lacks headers, add them manually in Excel: insert a new row at the top with descriptive names (id, name, email, created_at). Generic names like col1, col2, col3 work but make SQL harder to read. For CSVs from legacy systems, use our case converter to normalize header names to snake_case.
Can this handle CSVs with nested JSON or complex data structures?
Yes, but they'll be imported as TEXT columns. If a CSV cell contains {"name":"John","age":30}, our converter preserves it as a TEXT string. After import, use PostgreSQL's JSONB functions or MySQL's JSON_EXTRACT to parse. For dedicated JSON formatting, check our JSON formatter to validate structure before database insertion.
How do I import multi-million row CSVs efficiently?
For 1M+ rows, use database-native bulk loaders instead of INSERT statements: PostgreSQL: COPY table FROM '/path/to/file.csv' CSV HEADER (10-100x faster). MySQL: LOAD DATA INFILE '/path/file.csv' INTO TABLE table. SQLite: .mode csv / .import file.csv table. These bypass SQL parsing and load directly into table pages. Use our tool to generate the CREATE TABLE schema, then load data with native commands.
Advanced CSV-to-SQL Techniques for Power Users
Automated ETL Pipelines
Generate SQL once, then template it for daily imports. Use variables like $TABLE_NAME and $CSV_PATH in shell scripts. Combine with cron jobs for scheduled data syncs from APIs → CSV → SQL → Database. Popular in data engineering workflows.
Upsert Logic (INSERT or UPDATE)
For incremental updates, modify generated INSERT to use ON CONFLICT UPDATE (PostgreSQL) or ON DUPLICATE KEY UPDATE (MySQL). This updates existing rows instead of failing on duplicate keys—perfect for daily product inventory syncs.
Schema Migration Versioning
Save generated CREATE TABLE statements as migration files (001_create_users.sql) and version control in Git. Use migration tools like Flyway, Liquibase, or Alembic to track database schema evolution. Our diff tool helps compare schema versions.
Handling Time Zones in Timestamps
CSV timestamps rarely include time zones. If your data comes from multiple regions, standardize to UTC before import. Use PostgreSQL's TIMESTAMP WITH TIME ZONE or MySQL's CONVERT_TZ() function. Store UTC, display local time in application code. Check our timestamp converter for zone conversions.
Partitioning Large Tables
For multi-million row imports, create partitioned tables (PostgreSQL 10+) before loading data. Partition by date ranges or ID ranges to improve query performance. Example: PARTITION BY RANGE (created_at) splits data into monthly tables automatically.
Data Validation Before Import
Add CHECK constraints to generated tables: CHECK (age BETWEEN 0 AND 120), CHECK (email LIKE '%@%'). This catches bad data during import rather than corrupting your database. Failed rows generate error logs for manual review.
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Stop writing boilerplate INSERT statements by hand. Our intelligent converter analyzes your data structure, infers optimal SQL types, and generates production-ready database imports with syntax highlighting and batch optimization. Process unlimited files—completely free, no registration required.
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