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

# Custom & Non-Conforming Data

> Learn how Provalytics ingests custom data sources that do not come from a native API connector.

## Overview

Not every useful dataset comes from a standard platform connector.

Provalytics is built to flex around that reality. If your data is valuable and can be delivered in a structured way, Provalytics can usually ingest it programmatically regardless of where it originates.

These delivery paths are designed for custom, warehouse-derived, operational, or otherwise non-conforming datasets that do not fit a native API connection. They are commonly used for custom marketing data, custom conversion data, website data, offline data, curated warehouse extracts, and recurring files maintained by a client or partner team.

This flexibility is not an afterthought. Provalytics was built around the ability to create custom data layers because large enterprise clients rarely operate with perfectly uniform source systems. Since 2008, that reality has shaped the platform.

In practice, most client data is already structured somewhere. The challenge is not whether the data exists. The challenge is getting it into the platform in a repeatable, programmatic way so it can be organized for automatic ingestion, reporting, planning, and modeling.

## When to use these options

These routes are the right fit when:

* the source system does not have a native connector
* the data is already curated outside the platform
* the source is operational, custom, or warehouse-derived
* the delivery pattern is based on files, sheets, shared storage, or warehouse tables

## Available delivery paths

Depending on the source and operating model, Provalytics can ingest custom data through:

* [Snowflake Delivery](/integrations/snowflake-delivery)
* [BigQuery Delivery](/integrations/shared-bigquery)
* [Google Sheets Delivery](/integrations/google-sheets)
* [SharePoint Delivery](/integrations/sharepoint)
* [S3 Bucket Delivery](/integrations/s3-source-access)
* [Recurring Email Delivery](/integrations/helpscout-file-feeds)
* [Manual File Uploads](/integrations/manual-uploads)

The goal is flexibility without sacrificing repeatability. Whether the source lives in a warehouse, shared sheet, recurring file process, or managed storage location, Provalytics can help turn it into a stable input for reporting, planning, and modeling.

That is where custom data layers matter. They let Provalytics take structured client data from many different origins and organize it into a form the platform can ingest automatically over time.

## How to choose the right one

* Use a warehouse route when the data already lives in a structured environment and should be read directly.
* Use shared storage or sheet-based delivery when the client already maintains the data in those tools.
* Use recurring email delivery when a repeatable file workflow exists but a more automated connector is not practical yet.
* Use manual uploads for one-time files, backfills, or exceptions.

Work with your CSR to choose the best path for your account and source system.