---
title: "Microsoft Fabric"
slug: "microsoft-fabric"
description: "Export data from any source to Microsoft Fabric as CSV, JSON, JSONL, or Parquet files with Dataddo. No coding required."
updated: 2026-07-26T12:57:58Z
published: 2026-07-26T12:57:58Z
canonical: "docs.dataddo.com/microsoft-fabric"
---

> ## 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 Fabric

Microsoft Fabric is an AI-powered, unified analytics platform that brings together all the data tools an organization needs into a single Software-as-a-Service (SaaS) environment. It centralizes all data in its built-in data lake, OneLake, and features workloads like Data Factory and Power BI to simplify the entire data lifecycle and enable faster, data-driven decisions.

## Authorize Connection to Microsoft Fabric

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

#### Microsoft Fabric (OAuth)

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.

#### 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 Fabric Destination

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

| Field | Description |
| --- | --- |
| Workspace | Select the Workspace you would like to load data to. Shown when oAuthId is ``. |
| Lakehouse | Select the Lakehouse you would like to load data to. Shown when workspaceId is ``. |
| Path | Path |

Click Save.

## Supported File Formats

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

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

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

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