diff --git a/docs/content/en/latest/pipelines/provisioning/user_data_filters.md b/docs/content/en/latest/pipelines/provisioning/user_data_filters.md
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+---
+title: "User Data Filters"
+linkTitle: "User Data Filters"
+weight: 4
+---
+
+User Data Filter (UDF) provisioning lets you manage UDFs in your GoodData environment.
+
+UDFs are currently managed only in full load mode, meaning your input overwrites existing UDFs for each workspace present in the input.
+
+This tool currently supports only the `{column} IN (udf_value)` MAQL pattern. UDFs using more complex MAQL expressions must be set up manually.
+
+{{% alert color="info" %}} Visit [Set Up Data Filters for Users](https://www.gooddata.com/docs/cloud/workspaces/user-data-filters/) to learn more about User Data Filters setup and use cases in GoodData. {{% /alert %}}
+
+## Usage
+
+Start by importing and initializing the `UserDataFilterProvisioner`.
+
+```python
+from gooddata_pipelines import UserDataFilterProvisioner
+
+host = "http://localhost:3000"
+token = "some_user_token"
+
+# Initialize the provisioner with GoodData credentials
+provisioner = UserDataFilterProvisioner.create(host=host, token=token)
+
+```
+
+Then, set the LDM and MAQL column names used by the UDF:
+
+```python
+provisioner.set_ldm_column_name("ldm_column_name")
+provisioner.set_maql_column_name("{attribute/dataset.field_name}")
+
+```
+
+
+Next, validate the input data using the `UserDataFilterFullLoad` model.
+
+The model expects the following fields:
+
+| name          | description                                                                                  |
+|---------------|---------------------------------------------------------------------------------------------|
+| workspace_id  | ID of the workspace where the UDF will be applied.                                          |
+| udf_id        | ID of the UDF to be created. Should be equal to the ID of the user the UDF will be applied to. |
+| udf_value     | Value for the UDF.                                                                          |
+
+{{% alert color="info" title="Note on IDs"%}}
+Each ID can only contain allowed characters. See [Workspace Object Identification](https://www.gooddata.com/docs/cloud/create-workspaces/objects-identification/) to learn more about object identifiers.
+{{% /alert %}}
+
+Add the model to your imports and create validated instances:
+
+```python
+# Add the model to the imports
+from gooddata_pipelines import UserDataFilterFullLoad, UserDataFilterProvisioner
+
+host = "http://localhost:3000"
+token = "some_user_token"
+
+# Initialize the provisioner with GoodData credentials
+provisioner = UserDataFilterProvisioner.create(host=host, token=token)
+
+# Validate your data
+validated_data = [
+        UserDataFilterFullLoad(
+            workspace_id="workspace_id_1",
+            udf_id="user_id_1",
+            udf_value="udf_value_1",
+        )
+    ]
+
+```
+
+Now, with the provisioner initialized and your data validated, run the provisioner:
+
+```python
+# Import, initialize, validate...
+...
+
+# Run the provisioning method
+provisioner.full_load(validated_data)
+
+```
+
+## Examples
+
+Here is a complete example of a full load UDF provisioning workflow:
+
+```python
+import logging
+
+from gooddata_pipelines import UserDataFilterFullLoad, UserDataFilterProvisioner
+
+host = "http://localhost:3000"
+token = "some_user_token"
+
+# Initialize the provisioner
+provisioner = UserDataFilterProvisioner.create(host=host, token=token)
+
+# Set the column names to be used in the UDF
+provisioner.set_ldm_column_name("ldm_column_name")
+provisioner.set_maql_column_name("{attribute/dataset.field_name}")
+
+# Optional: set up logging and subscribe to logs emitted by the provisioner
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+provisioner.logger.subscribe(logger)
+
+# Prepare your data
+raw_data = [
+    {
+        "workspace_id": "workspace_id_1",
+        "udf_id": "user_id_1",
+        "udf_value": "udf_value_1"
+    },
+    {
+        "workspace_id": "workspace_id_1",
+        "udf_id": "user_id_2",
+        "udf_value": "udf_value_2"
+    },
+]
+
+# Validate the data
+validated_data = [
+    UserDataFilterFullLoad(
+        workspace_id=item["workspace_id"],
+        udf_id=item["udf_id"],
+        udf_value=item["udf_value"],
+    )
+    for item in raw_data
+]
+
+# Run the provisioning with the validated data
+provisioner.full_load(validated_data)
+
+```
diff --git a/docs/content/en/latest/pipelines/provisioning/workspace-permissions.md b/docs/content/en/latest/pipelines/provisioning/workspace-permissions.md
index 79aa455b3..d71757322 100644
--- a/docs/content/en/latest/pipelines/provisioning/workspace-permissions.md
+++ b/docs/content/en/latest/pipelines/provisioning/workspace-permissions.md
@@ -1,7 +1,7 @@
 ---
 title: "Workspace Permissions"
 linkTitle: "Workspace Permissions"
-weight: 4
+weight: 5
 ---
 
 Workspace permission provisioning allows you to create, update, or delete user permissions. See [Manage Workspace Permissions](https://www.gooddata.com/docs/cloud/manage-organization/manage-permissions/set-permissions-for-workspace/) to learn more about workspace permissions in GoodData Cloud.