---
schema_version: "1"
id: 51d11cf336be8daf3b740b5ff2ac8227400654bab767bae20e8f8a8864e8a9a3
title: Azure Data Lake Output Connection
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_integrate_connectors_output_azure_data_lake.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_connectors_output_azure_data_lake.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 69a3d53e2dd3e7018a7957155afa4dcd2e3825fc9c316d1e08cf1f219a39ab8e
description: Create a remote connection using the Azure Data Lake output connector to write .csv data from CRM Analytics to Microsoft Azure Data Lake Storage Gen2 when using Data Prep.
products:
  - CRM Analytics
  - Cross Cloud
release: "264"
---
# Azure Data Lake Output Connection

Create a remote connection using the Azure Data Lake output connector to write .csv data from CRM Analytics to Microsoft Azure Data Lake Storage Gen2 when using Data Prep.

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_note.png)

Note If you want to sync data from Microsoft Azure Synapse Analytics or dedicated SQL pool to CRM Analytics, use an [Azure Synapse Analytics Connection](https://help.salesforce.com/s/articleView?id=bi_integrate_connectors_microsoft_azure_sql_dw_settings.htm&language=en_US) instead. Or, to sync data from Microsoft Azure SQL Database to CRM Analytics, use an [Azure SQL Database Connection](https://help.salesforce.com/s/articleView?id=bi_integrate_connectors_microsoft_azure_sql_db_settings.htm&language=en_US).

## How Azure Data Lake Output Connection Works

With CRM Analytics connectors, you can write the outcome of a recipe to an external system for further analysis, business automation, and storage. After you configure an output connector to the data source and folder path using the steps in this guide, create a recipe using Data Prep. Add an Output node to the recipe and select to write to the output connection. Select the Azure Data Lake connection you configured, and choose which object will contain the output data. When the recipe runs, CRM Analytics writes data to the specified object in the folder path within the directory. For example, if you specified the folder path support/servicedata and chose the object APAC we write to the folder `APAC` at the path ...support/servicedata/APAC. The folder contains these files:

- The output dataset .csv file, with the naming standard `folderpath`\_0.csv. In our example, the output is `APAC_0.csv`. For larger datasets, CRM Analytics automatically partitions the data into multiple .csv files with increasing numbers appended to the name. In our example, there could be a `APAC_1.csv`, a `APAC_2.csv`, and more until all data is written. This file doesn’t have a header row.
- The metadata file schema.json, with information on each column including the name, data type, and precision.
- The \_SUCCESS file, generated when the .csv is completely written.

When you run the recipe again, the files previously generated are deleted and the new version of the files are written.

## Enable and Add the Azure Data Lake Output Connector

1. From Setup, in the Quick Find box, enter Analytics, select **Analytics**, and then **Settings**.
2. Select **Enable Azure Data Lake output connection** and then save your changes.
3. In the Data Manager, click the **Connections** tab.
4. Click **New Connection**.
5. Click **Output**.
6. Click the connector’s icon and enter its settings, as described in the Connection Settings section.
7. Click **Save & Test**. Save & Test validates your settings by attempting to connect to the source. If the connection fails, CRM Analytics shows possible reasons.

All settings require a value, unless otherwise indicated.

 
| Connection Setting | Description |
| --- | --- |
| Connection Name | Identifies the connection. Use a convention that lets you easily distinguish between different connections. |
| Developer Name | API name for the connection. The developer name can’t include spaces, and you can’t change it after you create the connection. |
| Description | Description of the connection for internal use. |
| Access Key | Your Azure Data Lake access key. To learn more, see Microsoft’s [Manage storage account access keys](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal). |
| Account | Your Azure Data Lake account name. |
| Container | The name of the Azure Data Lake container that the connector will write to. |
| Folder Path | Path to the folder that you want to connect. There must be a subfolder within the specified folder. For example, to write to `support/servicedata`, specify `support` for the folder path property in the connection then choose **servicedata** when configuring the recipe output node. |

Keep these behaviors in mind when working with the Azure data lake output connector.

- You can use an output connection more than once per recipe, but each output node must use a different object. Each output node can use up to the connector's per-run limit independently, but is still subject to the rolling 24 hour limit.
- Output connections are only available for recipes built with Data Prep.
- Each output connection only supports one folder path. To output to another folder path, create another output connection.
- When the prior run’s files are deleted in preparation for the current run, the earlier version of an output dataset is inaccessible. Set up a process to copy or use the output after each run, and use the \_SUCCESS file as an indication that the write is complete.