How to Import Large CSV Files into MySQL & Postgres Without Errors

Every developer has been there. You have a 200MB CSV file full of critical data, and you just need to get it into your database.

You try a GUI tool like DBeaver or Workbench. It freezes. You try LOAD DATA INFILE. It fails on row 4,000 because of a random comma in a text field. You try writing a Python script. Now you’re debugging utf-8 encoding errors instead of migrating data.

Importing CSVs should be the easiest part of the job, but it’s often the most frustrating. In this guide, we’ll break down why standard imports fail and how to generate production-ready SQL scripts that just work—even for massive files.

The Problem: Why “Auto-Import” Fails

Most database management tools (and even simple online converters) guess your data structure. They scan the first few rows and assume:

• “This looks like a number” -> INT
• “This looks like a date” -> DATE

But data is messy. If row 50,000 has a typo (e.g., “N/A” inside a Price column), the entire import crashes, and you have to start over.

Additionally, standard imports often lack Transaction Safety. If your import fails halfway through a 100,000-row file, you are left with 50,000 “ghost rows” that you now have to manually delete before trying again.

The Solution: Generate Robust SQL Scripts

The safest way to move data isn’t to “import” the raw CSV directly, but to convert it into a strictly validated .sql file containing INSERT statements.

This approach gives you:

1. Portability: You can version control the script.
2. Safety: You can wrap the entire operation in a BEGIN...COMMIT transaction.
3. Control: You can handle duplicates with INSERT IGNORE or UPSERT logic.

Step-by-Step: The “Strict Mode” Workflow

We built a Free CSV to SQL Studio to automate this process without uploading your data to a server. Here is how to use it to generate a bulletproof import script in under 60 seconds.

1. Configuration & Dialect

First, stop guessing the syntax. SQL Server uses brackets [ID], MySQL uses backticks `id`, and Postgres uses quotes "id".

In the CSV to SQL Studio:

1. Select your Dialect (MySQL, Postgres, SQL Server, or SQLite).
2. Set your Batch Size.
   • Pro Tip: Don’t do 1 row per insert. That’s too slow. A batch size of 500-1000 is the sweet spot for speed vs. stability.

2. Map & Validate Your Schema

Unlike basic tools that “hope for the best,” you need to define your rules upfront.

• Map Columns: Rename that messy header Cust_Email_Addr to a clean email column.
• Set Constraints: Mark critical fields like IDs or Emails as UQ (Unique) and NN (Not Null).
• Enable Strict Mode: This is the game-changer.If you check “Strict Type Validation,” the tool scans every single row locally in your browser before generating code. If it finds text in a number column, it flags it immediately—saving you from a database crash later.

3. Handle Duplicates Gracefully

What happens if you try to import a User ID that already exists?

• Standard Import: Crash.
• Smart Import: Use the “Conflict Strategy” dropdown to select UPSERT.
  • MySQL: Generates ON DUPLICATE KEY UPDATE
  • Postgres: Generates ON CONFLICT DO UPDATE

4. Generate & Run

Click Generate SQL. Because the tool uses Web Workers, it processes the file in a background thread on your machine. You can convert a 100MB file without your browser lagging.

The result is a single .sql file wrapped in a Transaction.

SQL

BEGIN;
INSERT INTO `users` (`id`, `email`) VALUES 
(1, 'raaj@example.com'),
(2, 'sarah@test.com');
...
COMMIT;

If any part of this script fails, the database rolls back automatically. Zero corruption. Zero “ghost rows.”

Try It Yourself (No Uploads Required)

You don’t need to write custom Python scripts just to move a spreadsheet. We built this tool because we were tired of the “Import Error” screen.

[Try the Enterprise CSV to SQL Converter Here]

• 100% Free
• Local Processing (Privacy First)
• Supports MySQL, Postgres, SQL Server & SQLite

Stop fighting with your data and start migrating it.