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A namespace is a container that helps organize related datasets within DataLinks. Think of it as a workspace or project folder where your datasets live and connect to one another. Namespaces help keep your data organized, secure, and contextually grouped for easier querying and AI analysis. If you’re new to this concept, read the explanatory article Datasets and Namespaces to see how they work together.

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Create a namespace in the web platform

Namespaces cannot be created on their own. A new namespace is automatically created when you create your first dataset within it. To create a namespace:
  1. Log in to your DataLinks account.
  2. In the left navigation menu, click Create a New Dataset.
  3. Enter a name for your dataset.
  4. In the Namespace field, type the name of the new namespace you’d like to create.
    • If the namespace does not already exist, DataLinks will create it automatically.
    • If it already exists, the dataset will be added to that namespace instead.
  5. Click Create dataset and upload data to finish.
Your new namespace will now appear in the list on your homepage, containing the dataset you just created.

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Create a namespace using the API or SDK

Namespaces are created through dataset creation, not as standalone objects. When you create a dataset with a new namespace name, DataLinks automatically provisions that namespace. To create via API: Use the POST Create New Dataset endpoint: POST Create new dataset In your request body, include a field such as: "namespace": "your_namespace_name" If the namespace does not already exist, it will be created during this request. To confirm creation: Use GET List user namespaces to view your namespaces: With the SDK: You can achieve the same effect by calling the dataset creation method and passing a new namespace name in your parameters. The SDK wraps the API behavior and is ideal for scripting or integrating namespace creation into automated data pipelines.

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Manage namespaces

  • You cannot create an empty namespace. Every namespace must contain at least one dataset.
  • You cannot delete a namespace that still contains datasets. To remove a namespace, first delete all datasets within it.
  • Once all datasets in a namespace are deleted, the namespace will no longer appear on your homepage.

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Best practices

  • Use clear, descriptive names for namespaces (for example: Finance_2025, Customer_Data, or Compliance_Reports).
  • Group datasets by business domain, department, or project to make navigation and querying easier.
  • Keep in mind that datasets within the same namespace can connect and share context more easily.

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Next Steps

After you’ve created your first namespace:
  • You can add more datasets to it at any time.
  • Begin ingesting data using the Ingest Data API or directly through the web platform.
  • Use the Connections view to see how datasets in your namespace link together through DataLinks’ Knowledge Map.
Creating a namespace is your starting point for turning fragmented data into a structured, connected foundation for AI.