> ## Documentation Index
> Fetch the complete documentation index at: https://docs.summand.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CSV upload

> The fastest way to get a one-off analysis: drag a file in.

CSV upload is Summand's no-setup entry point. It's available on every tier, including Free.

## Limits

| Tier            | Maximum file size |
| --------------- | ----------------- |
| Free            | 50 MB             |
| Pro / Education | 1 GB              |
| Enterprise      | 4 GB              |

For files larger than 4 GB, point Summand at the source via a [database connector](/data-sources/databases) or [Fivetran](/data-sources/fivetran) instead — uploads aren't the right tool for very large data.

## File format

<Steps>
  <Step title="UTF-8 encoded text">
    Other encodings (UTF-16, ISO-8859-1, Windows-1252) often work but aren't guaranteed. If column names look garbled in the preview, re-export as UTF-8.
  </Step>

  <Step title="One header row">
    The first non-empty row must contain column names. Names should be unique and human-readable; Summand uses them in the UI verbatim.
  </Step>

  <Step title="Comma, tab, or semicolon delimited">
    Auto-detected from the file extension and the first line. `.csv`, `.tsv`, `.txt` are all accepted.
  </Step>

  <Step title="Long format, not wide">
    One row per observation, one column per variable. Pivot tables and crosstabs produce noisy analyses — un-pivot first.
  </Step>
</Steps>

## What happens when you upload

1. The file streams directly to Summand's encrypted S3 bucket via a presigned URL — it doesn't pass through any application server.
2. Summand parses the header, samples the first \~10,000 rows to infer column types, and creates the dataset.
3. The full file is converted to Parquet on the analysis pipeline's first run.
4. The analysis pipeline produces a [semantic-layer version](/concepts/semantic-layer) and the dataset becomes browseable.

For a 100 MB file, end-to-end is typically under a minute.

## Re-uploading

To analyze a new version of the same file, click **Replace data** on the dataset page and upload again. Summand creates a new semantic-layer version pinned to the new file; the previous version remains accessible for comparison.

The dataset's schema overrides, context, sharing grants, and any experiments configured on it are preserved across re-uploads — you don't redo configuration.

## Troubleshooting

<AccordionGroup>
  <Accordion title="Upload fails partway through">
    Large uploads are resumable up to 24 hours. Refresh the page; you'll be prompted to resume rather than restart. If the upload still fails, the file may be corrupted — try opening it in a text editor or spreadsheet first to confirm it's well-formed.
  </Accordion>

  <Accordion title="Columns show up as &#x22;string&#x22; when they should be numeric">
    Most often this is non-numeric content in the column — currency symbols, units, commas as thousands separators. Either clean the file, or override the column type from the **Configuration** sidebar after upload.
  </Accordion>

  <Accordion title="My Predictors experiment found no useful features">
    Three common causes: (1) the target is highly imbalanced — fewer than \~30 minority-class rows; (2) too many features are high-cardinality strings excluded from the model; (3) the file is too small — EBM needs roughly 500+ rows for stable shape functions.
  </Accordion>
</AccordionGroup>