> For the complete documentation index, see [llms.txt](https://biggeo.gitbook.io/datalab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://biggeo.gitbook.io/datalab/my-library/import-data/import-from-cloud-providers.md).

# Import From Cloud Providers

Datalab supports importing data directly from your organization’s cloud storage accounts. Whether you're using Azure, AWS, GCP, or others, you can connect to your cloud, select your files, and bring your datasets into Datalab for use in indexing, visualization, product creation, or marketplace listings.

> **Note:** The exact connection and credential requirements vary by provider. Datalab supports major cloud providers with secure authentication methods.

***

## Import Data From Device

{% stepper %}
{% step %}

### Go to Data Management

* In the Datalab sidebar, click **Data Management**.
* At the top right, click **Add Data Source**.
  {% endstep %}

{% step %}

### Select Your Cloud Provider

* Choose your cloud platform from the options (e.g., **Azure**, **AWS**, **Google Cloud**, etc).
* You’ll be guided through the specific connection process for the provider you select.
  {% endstep %}

{% step %}

### Authenticate and Connect

* **Enter the necessary credentials** for your cloud storage account:
  * For **Azure**, this may include Active Directory details or a Shared Access Signature.
  * For **AWS**, you may be asked to input IAM credentials or S3 bucket access keys.
  * For **Google Cloud**, you may upload a JSON service account key.
* Follow the prompts to complete a secure authentication connection.
  {% endstep %}

{% step %}

### Configure Source Details

* **Name your dataset**: Give your data source a name that’s clear and easily recognizable.
* (Optional) Add a **short description** to provide context about the dataset contents.
  {% endstep %}

{% step %}

### Select Files or Folders

* Once your storage is connected, browse your cloud storage directories.
* **Choose the files or folders** you want to import (e.g., `.csv`, `.parquet`).
  {% endstep %}

{% step %}

### Create the Data Source

* After confirming your files and authentication details, click **“Create Data Source”**.
* Your cloud dataset will be imported into your Datalab workspace and available for indexing or further configuration.
  {% endstep %}
  {% endstepper %}

***

## What Happens Next?

Once imported, you can:

* **Index** the data for geospatial use
* **Enable hot compute** to use it in Datascape
* **Create data products** or add it to **marketplace listings**
* **Set up automated ETL scheduling** to pull in fresh data updates (Pro tier feature)

***

## FAQs

<details>

<summary><strong>Which cloud providers are supported?</strong></summary>

Datalab supports Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), and more.

</details>

<details>

<summary><strong>Are there different steps depending on the provider?</strong></summary>

Yes. While the flow is similar, authentication methods and required fields differ depending on the provider.

</details>

<details>

<summary><strong>Can I save and reuse credentials?</strong></summary>

Yes. Once you've added credentials, you can reuse them for future data imports.

</details>

<details>

<summary><strong>Can I automate the import process?</strong></summary>

Yes, with Pro tier plans you can enable scheduled ETL processes that sync your cloud data automatically.

</details>