Load multiple CSV files and write SQL

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As seen on: - Write SQL on CSV files (Desktop app) | Product Hunt
Show HN: Work with CSV files using SQL. For data scientists and engineers - Write SQL on CSV files (Desktop app) | Reddit


Load multiple CSVs and write SQL
SQL is the most efficient way to analyze and transform multiple CSVs.

No more custom and slow Python script. No more vlookup, no more pivot tables, and definitely no more reaching the row limit on Microsoft Excel or Google Sheets.

Export result to CSV
After you write SQL to retrieve the information that you want, you can export the result back into a CSV file.

Use cases

Clean up CSV files
Reformatting or changing one column in a CSV file is very troublesome. More than often, we would have to write a Python script to do so. With, it is much more convenient and faster.

Depending on how fast you can type, transforming a 1GB file can take less than 30 seconds in total.

Analyze data from multiple CSV files allows you to analyze multiple CSV files (e.g. join them by some columns).

This is particularly useful when you get CSV files from multiple sources like your Bank, Stripe, and Paypal, and you want to reconcile them together.


$40 USD/year

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If you would like to purchase a license (pay here), please pay and send an email to

Students can obtain a free individual license by emailing

Does your workplace reimburse software's cost?

If so, you should definitely try

While there are a few alternatives, is much more convenient and can save hours each month.

Let's compare it the alternatives:

Solution Compared to
Python script is much faster than a Python script and can load 1GB file within 10 seconds.

A python script loading a 1GB file is likely to take more than 1 minutes on any machine.
Excel, Google sheets Excel cannot handle a CSV file with more than 1M rows. Google Sheets has similar limitation.

If you are familiar with SQL, using SQL is much better than using Excel formula.
Loading CSV into a database Loading CSV into a database often involves using a command-line tool. It's inefficient and inconvenient.

Even if only saves you 30 minutes per month, it's already worth the minimal price tag that we will charge after beta.

What SQL-dialect is used in

Since is powered by DuckDB underneath, the DuckDB's SQL dialect is used.
You can read more about DuckDB's SQL here: