docs(gooddata-pipelines): public documentation

Author: janmatzek

CONTRIBUTING.md

@@ -60,6 +60,8 @@ The project documentation is done in hugo. To contribute:
 
 2. Run `make new-docs`
 
+3. Open [http://localhost:1313/latest/](http://localhost:1313/latest/) in your browser to see the preview.
+
 The documentation is deployed using manually triggered GitHub workflows.
 
 One logical change is done in one commit.

docs/content/en/latest/api-reference/_index.md

@@ -1,7 +1,7 @@
 ---
 title: "API Reference"
 linkTitle: "API Reference"
-weight: 60
+weight: 70
 navigationLabel: true
 ---
 

docs/content/en/latest/pipelines-overview.md

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+---
+title: "Pipelines Overview"
+linkTitle: "Pipelines Overview"
+weight: 14
+---
+
+GoodData Pipelines contains tools for automation of GoodData life cycle. Built over GoodData Python SDK, it allows you to conveniently manage user profiles, workspace permissions and more.
+
+For further information, refer to the PIPELINES section in the left navigation menu.
+
+## Installation
+
+
+Run the following command to install the ``gooddata-pipelines`` package on your system:
+
+```bash
+pip install gooddata-pipelines
+```
+
+### Requirements
+
+- Python 3.10 or newer
+- GoodData.CN or GoodData Cloud
+
+
+
+## Examples
+Here are a couple of introductory examples how to manage GoodData resources using Pipelines:
+
+### Provision Child Workspaces
+```python
+from gooddata_pipelines import WorkspaceProvisioner, WorkspaceFullLoad
+
+# GoodData.CN host in the form of uri eg. "http://localhost:3000"
+host = "http://localhost:3000"
+
+# GoodData.CN user token
+token = "some_user_token"
+
+# Initialize the provisioner
+provisioner = WorkspaceProvisioner.create(host=host, token=token)
+
+# Gather the definitions of the workspaces you want to create
+raw_data: list[dict] = [
+        {
+            "parent_id": "parent_workspace_id",
+            "workspace_id": "child_workspace_0",
+            "workspace_name": "Child Workspace 0",
+            "workspace_data_filter_id": "data_filter_id",
+            "workspace_data_filter_values": ["value_0"],
+        },
+        ...
+    ]
+
+# Validate the data
+validated_data = [WorkspaceFullLoad(**item) for item in raw_data]
+
+# Run the provisioning
+provisioner.full_load(validated_data)
+```
+
+### Workspace Backup
+```python
+from gooddata_pipelines import BackupManager, BackupRestoreConfig
+
+# GoodData.CN host in the form of uri eg. "http://localhost:3000"
+host = "http://localhost:3000"
+
+# GoodData.CN user token
+token = "some_user_token"
+
+# Define a list with IDs of workspaces you want to back up
+workspaces_to_back_up = ["workspace_id_1", "workspace_id_2"]
+
+# Configure backup options
+config = BackupRestoreConfig(storage_type="local")
+
+# Initialize the backup manager
+backup_manager = BackupManager.create(config, host, token)
+
+# Run the backup
+backup_manager.backup_workspaces(workspace_ids=workspaces_to_back_up)
+
+```

docs/content/en/latest/pipelines/_index.md

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+---
+title: "GOODDATA PIPELINES"
+linkTitle: "GOODDATA PIPELINES"
+weight: 60
+navigationLabel: true
+---

docs/content/en/latest/pipelines/provisioning/_index.md

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+---
+title: "Provisioning"
+linkTitle: "Provisioning"
+weight: 1
+no_list: true
+---
+
+Manage resources in GoodData.
+
+
+## Supported Resources
+
+Resources you can provision using GoodData Pipelines:
+
+- [Workspaces](./workspaces.md)
+- [Users](users.md)
+- [User Groups](user_groups.md)
+- [Workspace Permissions](workspace-permissions.md)
+
+
+## Workflow Types
+
+There are two types of provisioning supported by GoodData Pipelines:
+
+- [Full load](#full-load)
+- [Incremental load](#incremental-load)
+
+The provisioning types employ different algorithms and expect different structure of input data. For details about the expected inputs, check out the documentation page of individual provisioned resource.
+
+### Full Load
+
+Full load provisioning aims to fully synchronize the state of your GoodData instance with the provided input. This workflow will create new resources and update existing ones based on the input. Any resources existing on GoodData Cloud not inluded in the input will be deleted.
+
+### Incremental Load
+
+During incremental provisioning, the algorithm will only interact with resources specified in the input. During the incremental load, the input data expects an extra parameter: `is_active`. Resources with `True` value will be updated. On the other hand, by setting it to `False`, you can mark resources for deletion. Any other resources already existing in GoodData will not be altered.

docs/content/en/latest/pipelines/provisioning/user_groups.md

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+---
+title: "User Groups"
+linkTitle: "User Group"
+weight: 3
+---

docs/content/en/latest/pipelines/provisioning/users.md

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+---
+title: "Users"
+linkTitle: "Users"
+weight: 2
+---

docs/content/en/latest/pipelines/provisioning/workspace-permissions.md

@@ -0,0 +1,5 @@
+---
+title: "Workspace Permissions"
+linkTitle: "Workspace Permissions"
+weight: 4
+---

docs/content/en/latest/pipelines/provisioning/workspaces.md

@@ -0,0 +1,5 @@
+---
+title: "Workspaces"
+linkTitle: "Workspaces"
+weight: 1
+---