NSOL-5617 Finalize Dataset Manager documentation.

Author: mboglesby

README.md

@@ -9,6 +9,14 @@ The NetApp DataOps Toolkit is a collection of Python-based client tools that sim
 
 The toolkit includes [MCP Servers](mcp_servers.md) that expose many of these capabilities as "tools" that can be utilized by AI agents.
 
+## Highlighted Features
+
+### 🗂️ Dataset Manager
+
+The **Dataset Manager** is a powerful module in the Traditional Environments toolkit that provides a simplified, intuitive interface for managing datasets backed by NetApp ONTAP storage. It abstracts away volume management complexity and exposes datasets as simple directories, with built-in support for instant cloning, snapshots, and space efficiency — all through a clean Python API.
+
+➡️ See the [Dataset Manager README](netapp_dataops_traditional/docs/dataset_manager_readme.md) to get started.
+
 ## Getting Started
 
 The NetApp DataOps Toolkit includes the following client tools:

netapp_dataops_traditional/README.md

@@ -3,6 +3,14 @@ NetApp DataOps Toolkit for Traditional Environments
 
 The NetApp DataOps Toolkit for Traditional Environments is a Python library that makes it simple for developers, data scientists, DevOps engineers, and data engineers to perform various data management tasks, such as provisioning a new data volume, near-instantaneously cloning a data volume, and near-instantaneously snapshotting a data volume for traceability/baselining. This Python library can function as either a command line utility or a library of functions that can be imported into any Python program or Jupyter Notebook. The toolkit also includes [MCP Servers](../mcp_servers.md) that expose many of the capabilities as "tools" that can be utilized by AI agents.
 
+## Highlighted Features
+
+### 🗂️ Dataset Manager
+
+The **Dataset Manager** is a powerful module that provides a simplified, intuitive interface for managing datasets backed by NetApp ONTAP storage. It presents datasets as plain directories on your local filesystem while leveraging the full power of ONTAP under the hood — instant clones, point-in-time snapshots, and space efficiency — all through a clean Python API purpose-built for data scientists and data engineers.
+
+➡️ See the [Dataset Manager README](docs/dataset_manager_readme.md) to get started.
+
 ## Compatibility
 
 The NetApp DataOps Toolkit for Traditional Environments supports Linux and macOS hosts.

netapp_dataops_traditional/docs/dataset_manager_readme.md

@@ -7,6 +7,7 @@
 - [Key Features](#key-features)
 - [Architecture Overview](#architecture-overview)
 - [Prerequisites](#prerequisites)
+- [Installation](#installation)
 - [Getting Started](#getting-started)
   - [Initial Configuration](#initial-configuration)
   - [Creating Your First Dataset](#creating-your-first-dataset)
@@ -129,6 +130,10 @@ Root Volume (e.g., "dataset_mgr_root")
 - NetApp Cloud Volumes ONTAP (ONTAP 9.7+)
 - Amazon FSx for NetApp ONTAP
 
+**Python:**
+- Python 3.8–3.13
+- `pip` (usually bundled with Python; verify with `pip --version`)
+
 **Required Utilities:**
 - `mount` (for checking mount status)
 - `mountpoint` (for validating mount points)
@@ -155,6 +160,66 @@ Your ONTAP user account needs permissions to:
 - NFS protocol enabled on the ONTAP SVM
 - Appropriate export policy rules for your host's IP address
 
+## Installation
+
+### Step 1: Install NFS Client Utilities (if not already installed)
+
+Dataset Manager mounts ONTAP volumes via NFS, so the NFS client utilities must be installed on your system.
+
+**Ubuntu / Debian:**
+```bash
+sudo apt-get update && sudo apt-get install -y nfs-common
+```
+
+**RHEL / CentOS / Fedora:**
+```bash
+sudo dnf install -y nfs-utils
+```
+
+**macOS:**
+
+NFS client support is built in to macOS — no additional installation is required.
+
+### Step 2: Install the Package
+
+It is recommended to install the toolkit inside a Python virtual environment to keep dependencies isolated.
+
+**Create and activate a virtual environment:**
+```bash
+python3 -m venv ~/netapp-dataops-venv
+source ~/netapp-dataops-venv/bin/activate
+```
+
+**Install the toolkit:**
+```bash
+pip install netapp-dataops-traditional
+```
+
+This installs the package with support for NetApp ONTAP (AFF, FAS, Cloud Volumes ONTAP, Amazon FSx for NetApp ONTAP, and ONTAP Select).
+
+> **Tip:** Add `source ~/netapp-dataops-venv/bin/activate` to your shell's startup file (e.g., `~/.bashrc` or `~/.zshrc`) so the environment is activated automatically in new terminal sessions.
+
+### Step 3: Verify Installation
+
+Confirm that the toolkit was installed correctly:
+
+```bash
+netapp_dataops_cli.py --help
+```
+
+You should see the toolkit's help output. If the command is not found, ensure the virtual environment is activated and that its `bin` directory is on your `PATH`.
+
+You can also verify the Python library is importable:
+
+```python
+from netapp_dataops.traditional.datasets import Dataset
+print("Installation successful!")
+```
+
+> **Note:** Python 3.8–3.13 is required.
+
+---
+
 ## Getting Started
 
 ### Initial Configuration

netapp_dataops_traditional/docs/ontap_readme.md

@@ -2,6 +2,14 @@
 
 The NetApp DataOps Toolkit for Traditional Environments is a Python library that makes it simple for developers, data scientists, DevOps engineers, and data engineers to perform various data management tasks, such as provisioning a new data volume, near-instantaneously cloning a data volume, and near-instantaneously snapshotting a data volume for traceability/baselining. This Python library can function as either a [command line utility](#command-line-functionality) or a [library of functions](#library-of-functions) that can be imported into any Python program or Jupyter Notebook. The toolkit also includes an [MCP Server](mcp_server.md) that exposes many of the capabilities as "tools" that can be utilized by AI agents. The ONTAP module provides this functionality for standard ONTAP instances.
 
+## Highlighted Features
+
+### 🗂️ Dataset Manager
+
+The **Dataset Manager** is a powerful module that provides a simplified, intuitive interface for managing datasets backed by NetApp ONTAP storage. It presents datasets as plain directories on your local filesystem while leveraging the full power of ONTAP under the hood — instant clones, point-in-time snapshots, and space efficiency — all through a clean Python API purpose-built for data scientists and data engineers.
+
+➡️ See the [Dataset Manager README](dataset_manager_readme.md) to get started.
+
 ## Compatibility
 
 The NetApp DataOps Toolkit for Traditional Environments supports Linux and macOS hosts.