> For the complete documentation index, see [llms.txt](https://docs.syntho.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.syntho.ai/setup-workspaces/create-a-workspace/connect-to-a-database/azure-data-lake-storage-adls.md).

# Azure Data Lake Storage (ADLS)

<figure><img src="https://content.gitbook.com/content/U61B9DqtWCNO3Z30vnjh/blobs/pyO1QfwNkgVLRMAJnkwQ/AzureDataLakeStorage(ADLS).png" alt=""><figcaption><p>Source and Destination Databases</p></figcaption></figure>

{% hint style="info" %}
**Destination only**

This connector can only be used as a destination for writing your generated data.

* Supported File Types: Parquet
* Supported Partitioning: Horizontal partitioning based on the batch size. Each batch is written to a separate file.
  {% endhint %}

## Before you begin <a href="#before-you-begin" id="before-you-begin"></a>

Before you begin, review the items in the following list:

* Get the URL for your **Azure** storage endpoint.
* Get the storage container name and remote path.
* Choose one connection method:
  * **Storage account name and key**: Use separate Azure storage account credentials.
  * **Storage connection string**: Use an account-level connection string. Enter the container name separately.
  * **Container connection string** (`container_connection_string`): Use a connection string for the target container.
* Provide read/write permissions to the storage container that is used (i.e., the storage container name).

## Output format

Syntho's ADLS output connector will write all generated data to **Parquet** files as follows:

* Each generated table will be written to a Parquet file in the following format:\
  `{schema-name}-{table_name}_part_{part_number}.parquet`
* The number of rows in a single Parquet file (part) is defined by the batch size. All Parquet parts of a single table are stored in their own directory.
* Each folder name will use the following format:

  `{schema_name}.{table_name}`

## Connect and set up the workspace

Launch Syntho and select **Connect to a database**, or under **Create workspace > Destination Database**, select **ADLS**. For a complete list of data connections, select **More** under **From database**. Then do the following:

1. Enter the remote path.
2. Enter the storage container name.
3. Choose one connection method:
   * Enter the storage account name and storage account key, *or*
   * Enter the storage connection string.
   * Enter the container connection string in `container_connection_string`.
4. Select **Create Workspace**.

## Troubleshooting connection errors

* **Storage account unreachable**: Check the storage endpoint, network route, and firewall rules.
* **Authentication or permission failure**: Check the account key or connection string. Confirm write access to the container.
* **Storage account or container not found**: Check the account and container names.
* **Invalid container URL or connection details**: Check that the container URL and selected connection details are complete and valid.

## Limitations & considerations

Contact your Syntho contact person to discuss possible limitations regarding this connector.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.syntho.ai/setup-workspaces/create-a-workspace/connect-to-a-database/azure-data-lake-storage-adls.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
