Split Huge CSV Files Online

Divide large CSV files (1GB+) into smaller, manageable chunks (e.g., 1M rows) for Excel. Free splitter tool, no signup required.

Last Updated: January 2026 • Version 2.1

✂️

Drop your large CSV here

or click to browse

Supports huge .csv files

Rows per file (Max limit)

Default is 1,000,000 (Excel limit).

Optimized for large files. Processed in chunks to save memory.

🛠️ How to Use

1
Upload your large .csv file (supports files >1GB).
2
Set the maximum number of rows per file (default is 1,000,000 for Excel compatibility).
3
Click 'Split CSV Now'. The server processes the file in streams to save memory.
4
Download a ZIP file containing all your split parts.

? FAQ

Why split a CSV file?

Excel has a limit of 1,048,576 rows. If your CSV is larger, Excel won't open it completely. Splitting it allows you to view all data across multiple files.

Does it work with 5GB+ files?

Yes. We use stream processing, so we don't load the whole file into RAM at once. It's extremely efficient for massive datasets.

Is it free?

Yes, 100% free and open for everyone.

The "Excel Row Limit" Problem

Microsoft Excel has a hard limit of 1,048,576 rows per sheet. In the era of Big Data, it's common to export CSVs from databases (SQL, Salesforce, HubSpot) that exceed this limit. When you try to open a 2 million row CSV in Excel, it simply truncates the data, showing an error: "File not loaded completely".

Chunking Strategy: How We Solve It

DataMani uses a technique called Stream Chunking. Instead of loading your 5GB file into memory (which would crash most browsers and computers), we read the file line-by-line using Python generators.

Memory Efficiency: We only hold a small "buffer" of data in RAM at any given second.
Precision Splitting: You define the limit (e.g., 1,000,000 rows). We count exactly that many rows, save a file (Part_1.csv), clear the memory, and start Part_2.csv.
ZIP Compression: All parts are automatically zipped to make the download fast and organized.

Why Not Use Python Locally?

While you can write a script to do this, setting up a Python environment, installing Pandas, and handling encoding errors (UTF-8 vs Latin-1) takes time. DataMani provides that robust Python backend in a simple web interface, saving you the setup time.