---
title: "Microsoft SharePoint"
slug: "microsoft-sharepoint-1"
description: "Connect Microsoft SharePoint to Dataddo and load CSV, JSON, XML, XLSX, or Parquet files into any dashboard, database, or data warehouse. No coding required."
updated: 2026-07-09T08:32:25Z
published: 2026-07-09T08:32:25Z
canonical: "docs.dataddo.com/microsoft-sharepoint-1"
---

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

# Microsoft SharePoint

**Microsoft SharePoint** is a collaboration and document management platform. The Microsoft SharePoint connector reads a file from a site's document library and turns it into a Dataddo data source, so you can send its content to any dashboard, database, or data warehouse.

## Authentication Methods

Microsoft SharePoint supports more than one way to connect. Pick one when you create the authorizer in Dataddo:

- **Microsoft SharePoint** - sign in with Microsoft and approve access (recommended).
- **Microsoft SharePoint Custom** - use your own app credentials (for advanced setups).
- **Microsoft Service Principal** - connect with a Microsoft Entra service principal (client ID, client secret, and tenant ID), without interactive sign-in.

## Authorize Connection to Microsoft SharePoint

To authorize this service, use **OAuth 2.0** to share specific data with Dataddo while keeping usernames, passwords, and other information private.

1. On the **Authorizers** page, click on [Authorize New Service](https://app.dataddo.com/service/new) and select your service.
2. Follow the on-screen prompts to grant Dataddo the necessary permissions to **access and retrieve** your data.
3. **[Optional]** Once your ***authorizer*** is created, click on it to **change the label** for easier identification.

Ensure that the account you're granting access to holds at least **admin-level permissions**. If necessary, assign a team member with the required permissions with the [authorizer role](https://docs.dataddo.com/docs/user-roles#authorizer) to authenticate the service for you.

For more information, see our article on [***authorizers***](https://docs.dataddo.com/docs/authorized-services).

## Supported File Formats

The connector reads one file per data source, in any of these formats:

| Format | Notes |
| --- | --- |
| CSV | Set the **CSV Delimiter**, whether the file has a **Header** row, an optional **Comment** character (lines starting with it are skipped), and **Lazy Quotes** for files with imperfect quoting. |
| JSON | An array of objects, one object per row. |
| XML | Repeated elements become rows. |
| XLSX | Select the **Sheet** to read, an optional cell **Range**, and whether the sheet has a header row. |
| Parquet | Columnar files exported from data platforms. |

Files can be plain or **GZIP**-compressed; set **File compression** accordingly.

## Selecting the File

When you create the source, point it at the file:

- **Site** - the SharePoint site.
- **Drive** - the document library to read from.
- **Path** - the directory that holds the file.
- **File name** - the name of the file to read.

The path and file name support **dynamic date placeholders**, so one source can follow files that are named by date. `{{today}}` and `{{yesterday}}` are replaced at run time; add a date format after `|` (the default is `Ymd`):

| File name mask | Resolves to |
| --- | --- |
| `report_{{today}}.csv` | `report_` followed by the current date, e.g. `report_20260709.csv` |
| `export_{{yesterday\\|Y-m-d}}.csv` | `export_` followed by yesterday's date, e.g. `export_2026-07-08.csv` |

## How Data Extraction Works

There is no date range for this connector. Every run downloads the file at the configured location (after resolving the date placeholders) and extracts its full current content.

The two most common setups:

- **One file that is updated in place**: schedule the source as often as the file changes and use the **replace** write mode in the flow, so the destination table mirrors the file.
- **A new date-stamped file every day**: use a `{{yesterday}}` mask in the file name, schedule the source daily, and use the **append** write mode. Each run adds one day's file, which builds the history over time.

## Transformation

Before the data reaches your destination, an **initial transformation** turns the parsed file into rows. Dataddo pre-fills it based on the selected file format and header settings. You can edit it in the **Transformation** editor when creating the source, for example to rename fields, unwind nested arrays, or drop columns.

## How to Create a Microsoft SharePoint Data Source

1. In Dataddo, open **Sources** and click **Create Source**.
2. Select **Microsoft SharePoint** and choose the **Authorizer** you created above.
3. Fill in the drive, path, and file name, using date placeholders if the file is named by date.
4. Select the **File Format** and its options (delimiter and header for CSV, sheet and range for XLSX), and the **File compression** if the file is gzipped.
5. (Optional) Adjust the pre-filled **Transformation**.
6. Click **Test Data** to preview the result, then click **Save**.

## Troubleshooting

### Data Preview Unavailable

No data preview when you click on **Test Data** might be caused by an issue with your ***source*** configuration. The most common causes are:

- **Date range**: Try a smaller date range. You can load the rest of your data afterward via [manual data load](https://docs.dataddo.com/docs/data-backfilling).
- **Insufficient permissions**: Please make sure your authorized account has at least admin-level permissions.

## Related Articles

Now that you have successfully created a ***data source***, see how you can connect your data to a dashboarding app or a data storage.

**Sending Data to Dashboarding Apps**

- [Simple Data Integration to Dashboards](https://docs.dataddo.com/docs/simple-data-integration-to-dashboards)
- [Data Backfilling to Dashboarding Apps](https://docs.dataddo.com/docs/data-backfilling-to-dashboarding-apps)

**Sending Data to Data Storages**

- [Batch Ingestion to Data Warehouses](https://docs.dataddo.com/docs/ingestion-to-data-warehouses)
- [Data Backfilling to Storages](https://docs.dataddo.com/docs/data-backfilling-to-storages)

**Other Resources**

- [Troubleshooting](https://docs.dataddo.com/docs/troubleshooting)
- [Extraction Logs](https://docs.dataddo.com/docs/extraction-logs)
- [Data Duplication](https://docs.dataddo.com/docs/data-duplication)