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Download Bendit for Windows.

Get the local DuckDB workbench for importing, cleaning and transforming Excel, CSV and Parquet files. No installer, no account, no sign-in.

How to install

1. Download the zip. Click "Download latest build" above to get Bendit-Windows-x64.zip.
2. Extract it first. Bendit won't run correctly straight out of the zip. Right-click the downloaded file and choose Extract All… (or use your preferred unzip tool), and pick a folder you're happy to keep it in — there's nothing to install, so wherever you extract it is where it'll live.
3. Open the extracted folder and double-click Bendit.exe.
4. If Windows shows a SmartScreen warning ("Windows protected your PC"), this happens because the build isn't code-signed yet, not because anything is wrong. Click More info, then Run anyway.
5. That's it. Bendit is portable — no installer, no registry entries, no background services. To update later, just download a new build and repeat these steps in a new folder.

Quick start: import and query in a minute

1. Open Bendit and import a file — drag sample-orders.csv or sample-orders.parquet onto the window, or use the Import button and pick either one. Bendit reads the columns automatically; there's no schema to define up front, and the steps are identical whether the file is CSV or Parquet.
2. Find the table name. Bendit names the imported table after the file (shown in the left-hand panel, e.g. sample_orders) — that's what you'll use in the SQL editor.
3. Look at the raw data first:

   select * from sample_orders limit 10;

4. Clean it up with SQL. The sample data has padded names, mixed date formats and comma-decimal amounts on purpose — this is what a normal cleanup query looks like:

   select
     trim(customer_name) as customer_name,
     try_cast(order_date as date) as order_date,
     replace(amount, ',', '.')::decimal(18,2) as amount
   from sample_orders
   where customer_name is not null
   order by order_date desc;

5. Export the result once the grid looks right — same export step regardless of whether you started from CSV or Parquet.

See more SQL examples →

Good first test

Use a messy operational export: customers, orders, invoices, stock items, planning data, or any CSV/Excel/Parquet file that normally requires manual cleanup.

Download sample CSV →
Download sample Parquet →