---
title: "Microsoft SharePoint"
slug: "microsoft-sharepoint"
description: "Export data from any source to Microsoft SharePoint as CSV, JSON, JSONL, or Parquet files with Dataddo. No coding required."
updated: 2026-07-26T12:58:00Z
published: 2026-07-26T12:58:00Z
canonical: "docs.dataddo.com/microsoft-sharepoint"
---

> ## 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 web-based collaboration platform that allows organizations to store, organize, share, and manage documents and content securely. It’s commonly used for creating internal websites, managing workflows, and enabling team collaboration across departments.

## Authorize Connection to Microsoft SharePoint

Authorize the connection so Dataddo can write files to Microsoft SharePoint:

#### Microsoft SharePoint

| Field | Description |
| --- | --- |
| A name for this authorizer in Dataddo | A label so you can recognize the connection later. |
| Tenant ID | Tenant ID Default: `common`. |

#### Microsoft Service Principal

| Field | Description |
| --- | --- |
| A name for this authorizer in Dataddo | A label so you can recognize the connection later. |
| Client ID | Client ID |
| Client Secret (Shared Key) | Client Secret (Shared Key) |
| Tenant ID | Tenant ID |
| Scopes | Select the permissions (scopes) required by the Microsoft service you are connecting to, as specified in its official API documentation. |

Dataddo validates the connection when you save it.

## Create a Microsoft SharePoint Destination

Go to Destinations, click Create Destination, and select Microsoft SharePoint. Give the destination a name, choose the authorizer, then set:

| Field | Description |
| --- | --- |
| SharePoint Site | Select a Microsoft SharePoint Site from this dropdown list. Shown when oAuthId is ``. |
| SharePoint Drive | Pick a drive from those available in the previously selected SharePoint Site. Shown when siteId is ``. |
| Path | Enter or select the directory where the data file will be created. Shown when driveId is ``. |

Click Save.

## Supported File Formats

Each flow run writes the data as a file in the format you pick.

| Format | Description |
| --- | --- |
| `csv` | Microsoft SharePoint writes the data as CSV files. |
| `json` | Microsoft SharePoint writes the data as JSON files. |
| `jsonl` | Microsoft SharePoint writes the data as JSONL files. |
| `parquet` | Microsoft SharePoint writes the data as PARQUET files. |

For CSV you can set the delimiter, header row, and date formatting. For Parquet, JSON, and JSONL you can set the timestamp unit.

## File Naming

You can also build a custom filename with these placeholders (see [Dynamic File Naming Patterns](/docs/dynamic-file-naming-patterns) for the full list):

- `{{objectLabel}}` and `{{objectId}}`: the flow name and id.
- `{{today}}` and `{{yesterday}}`: the run date.
- `{{dateRangeStart}}` and `{{dateRangeEnd}}`: the bounds of the flow's date range.
- Date-range expressions such as `{{1d1}}` (yesterday) or `{{90d1}}` (the last 90 days through yesterday).
- Add a date format after a `|`, for example `{{today|Ymd}}` gives `20201231` and `{{1d1|Y-m-d}}` gives `2020-12-31`.

## File Partitioning

File partitioning splits a large dataset into smaller files based on a criterion such as date, which improves how a data lake organizes and queries the data (see [Data Lake Ingestion](/docs/data-lake-ingestion)). In Dataddo you partition by putting date variables from File Naming into the file name, so each flow run writes its own dated file.

For example, `events_{{1d1|Y-m-d}}.parquet` writes one Parquet file per day, so a lake engine such as Microsoft SharePoint can read the set of files as a date-partitioned dataset. Pick a file format like Parquet or CSV that your lake reads, and schedule the flow to match the partition period (for example daily for a daily date token).

## Write Modes

Each flow run writes a file at the resolved name. The default is `truncate_insert`: `insert` keeps writing new files, and `truncate_insert` replaces the file at the same name.

## How Data Is Delivered

Every run produces one file at the resolved name. A date-stamped name accumulates a new snapshot file per run, which is the pattern described in [Data Lake Ingestion](/docs/data-lake-ingestion).

## How to Create a Flow to Microsoft SharePoint

1. Go to Flows and click Create Flow.
2. Add one or more sources.
3. Add Microsoft SharePoint as the destination and pick the authorizer.
4. Choose the file format and the file name.
5. Set the schedule and click Save.

## Troubleshooting

### Cannot connect to Microsoft SharePoint

Dataddo cannot reach Microsoft SharePoint. Check the credentials, path, and permissions in the authorizer and destination, and make sure Microsoft SharePoint is reachable by Dataddo.

### File is not created

The account used by Dataddo lacks write permission on the target path. Grant write access to the folder or bucket and restart the flow.

## Related Articles
* [Data Backfilling to Storages](https://docs.dataddo.com/docs/data-backfilling-to-storages)
* [Write Modes](https://docs.dataddo.com/docs/data-storages#write-modes)
* [Implementation of Batch Ingestion to Data Warehouses](https://docs.dataddo.com/docs/ingestion-to-data-warehouses)
* [Network Access Control List \(ACL\) Configuration](https://docs.dataddo.com/docs/network-acl)
* [SSH Tunnelling](https://docs.dataddo.com/docs/ssh-tunnelling)
* [Data Transformations](https://docs.dataddo.com/docs/data-transformations)
* [Data Quality Firewall](https://docs.dataddo.com/docs/data-quality-firewall)