> For the complete documentation index, see [llms.txt](https://support.sayprimer.com/primer/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.sayprimer.com/primer/primer-support-review/data-sources/uploading-via-s3.md).

# Uploading via S3

Drop people and company CSVs to your Primer S3 location for automated loading.

**Beta — contact** [**support@sayprimer.com**](mailto:support@sayprimer.com) **to request access.**

You can load people and companies into Primer by dropping CSV files to an S3 location Primer reads. This is the automated equivalent of [importing a CSV](/primer/primer-support-review/data-sources/importing-a-csv.md) and works well with pipelines like Snowflake, Hightouch or Fivetran.

## Where files go

Put each record type in its own folder under your dataset prefix:

* People — `{prefix}/datasets/people/`
* Companies — `{prefix}/datasets/companies/`

Your `prefix` comes from your sync configuration; support provides it when you're set up.

## File format

Each file is a [CSV with a header row](/primer/primer-support-review/data-sources/importing-a-csv.md) using the canonical people or company columns. Each file is a **complete snapshot** of that dataset — records absent from a new file are dropped, not merged.

## What happens after you drop a file

Primer picks up the file, processes it, and moves it into an `archived/` subfolder on success or a `failed/` subfolder on failure, so you can tell whether a drop was ingested.

## Exporting to Primer's S3 from your warehouse

Any tool that can write a CSV to an S3 prefix can feed this drop. The pattern is the same whatever you use: produce a CSV with the [canonical columns](/primer/primer-support-review/data-sources/importing-a-csv.md) and land it in the right `people/` or `companies/` folder under your prefix.

* **Snowflake** — `COPY INTO '@your_stage/...'` (or an external stage pointed at the prefix) to unload a query result as CSV directly to the S3 location.
* **Hightouch** — configure an **Amazon S3** destination targeting your prefix and sync a model to it on a schedule.
* **Fivetran** — use an S3 destination / transformation that writes the CSV to the prefix.

Because each file is a **complete snapshot**, schedule the export to write the *whole* current dataset each run, not just changed rows — anything absent from the newest file is dropped. Your prefix and the exact folders come from your sync configuration; support provides them when you're set up.


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