---
title: "Microsoft OneDrive"
slug: "microsoft-onedrive"
description: "Export data from any source to Microsoft OneDrive as CSV, JSON, JSONL, or Parquet files with Dataddo. No coding required."
tags: ["Data destination", "How-to guide"]
updated: 2026-07-26T12:58:18Z
published: 2026-07-26T12:58:18Z
canonical: "docs.dataddo.com/microsoft-onedrive"
---

> ## 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 OneDrive

Microsoft OneDrive is a cloud-based storage service provided by Microsoft that allows users to store and access their files and documents from anywhere. It offers seamless integration with various devices and applications, enabling users to easily sync, share, and collaborate on their files while providing a secure and reliable storage solution.

## Authorize Connection to Microsoft OneDrive

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

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.

Dataddo validates the connection when you save it.

## Create a Microsoft OneDrive Destination

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

| Field | Description |
| --- | --- |
| Drive ID (for OneDrive non-Business accounts only) | Use only for OneDrive non-business accounts. Shown when oAuthId is ``. |
| Path | Enter or select the directory where the data file will be created. Shown when oAuthId is ``. |

Click Save.

## Supported File Formats

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

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

1. Go to Flows and click Create Flow.
2. Add one or more sources.
3. Add Microsoft OneDrive 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 OneDrive

Dataddo cannot reach Microsoft OneDrive. Check the credentials, path, and permissions in the authorizer and destination, and make sure Microsoft OneDrive 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)