Data Platform | Add dedicated K8s page and detailed descriptions (architecture/components)

site/content/data-platform/_index.md

@@ -21,6 +21,52 @@ AI solutions for GraphRAG, graph machine learning, data explorations, and more.
 run it on-premises or in the cloud yourself on top of Kubernetes to access all
 of the platform features with enterprise-grade automation and reliability.
 
+## What Makes Up the Arango Data Platform
+
+The Arango Data Platform is built on a layered architecture that combines powerful
+components into a unified solution:
+
+- **ArangoDB Enterprise Edition**: The multi-model database foundation supporting
+  graphs, documents, key-value, vector search, and full-text search capabilities.
+
+- **Graph Visualizer**: A sophisticated web-based interface for graph exploration,
+  smart search, and visual layouts.
+
+- **Arango Platform Suite**: Enterprise-grade features including high availability
+  and monitoring, the Graph Analytics Engine, comprehensive APIs and connectors,
+  and centralized orchestration and resource management.
+
+All components are orchestrated through Kubernetes, providing automated deployment,
+scaling, and management with enterprise-grade reliability.
+
+For a detailed breakdown of each component, see [Features and Architecture](features/).
+
+## Extend the Arango Data Platform with AI capabilities
+
+Extend the Arango Data Platform with the [**AI Suite**](../ai-suite/_index.md) 
+that offers advanced AI and machine learning capabilities that integrate seamlessly
+into the platform's unified web interface.
+
+What you get with the AI Suite:
+
+- [GraphRAG](../ai-suite/graphrag/): Generate knowledge graphs from documents and enable
+   conversational querying of your data.
+- [GraphML](../ai-suite/graphml/): Apply machine learning algorithms that leverage graph
+  structure for better predictions.
+- [Graph Analytics](../ai-suite/graph-analytics/): Run advanced algorithms like PageRank
+  to discover influential nodes and patterns.
+- [Jupyter notebooks](../ai-suite/notebook-servers.md): Run Jupyter Notebooks to build and
+  experiment with graph-powered data, AI, and machine learning workflows directly connected
+  to ArangoDB databases. 
+- Public and private LLM support: Use public LLMs such as OpenAI
+  or private LLMs with [Triton Inference Server](../ai-suite/reference/triton-inference-server.md).
+- [MLflow integration](../ai-suite/reference/mlflow.md): Use the popular MLflow as a model registry
+  for private LLMs or to run machine learning experiments as part of the Arango Data Platform.
+
+{{< tip >}}
+The AI Suite requires a separate license.
+{{< /tip >}}
+
 {{< cards >}}
 
 {{% card title="Get started with the Arango Data Platform" link="get-started/" %}}
@@ -32,8 +78,8 @@ Optionally add AI Suite to turn data into an AI-powered knowledge engine.
 Explore the Kubernetes-native architecture, unified interface, and enterprise-grade capabilities of the Arango Data Platform.
 {{% /card %}}
 
-{{% card title="ArangoDB Kubernetes Operator" link="../../arangodb/3.12/deploy/kubernetes/" %}}
-Learn about the official ArangoDB Kubernetes Operator that powers the Arango Data Platform.
+{{% card title="Kubernetes-Native Architecture" link="kubernetes/" %}}
+Learn about the Kubernetes-native foundation that the Arango Data Platform is purpose-built on.
 {{% /card %}}
 
 {{% card title="Graph Visualizer" link="graph-visualizer/" %}}

site/content/data-platform/features.md

@@ -1,10 +1,10 @@
 ---
-title: Feature list of the Arango Data Platform
-menuTitle: Features
+title: Architecture and Features of the Arango Data Platform
+menuTitle: Architecture and Features
 weight: 5
 description: >-
-  The Arango Data Platform is a scalable Kubernetes-native architecture that gets you all features
-  of ArangoDB as a single solution with a unified interface
+  Discover how the Arango Data Platform combines database, visualization, and enterprise
+  features into a unified, Kubernetes-native architecture
 ---
 ## Architecture
 
@@ -18,57 +18,76 @@ scaling, and management. Powered by the official
 enterprise-grade database management and high availability.
 {{< /tip >}}
 
-- **Core Database**: The ArangoDB database system forms the solid core
-  of the Arango Data Platform.
+### Kubernetes Integration
+
+At its core, the Arango Data Platform is purpose-built for Kubernetes environments, leveraging the 
+[official ArangoDB Kubernetes Operator](https://arangodb.github.io/kube-arangodb/docs/) 
+(`kube-arangodb`) to deliver enterprise-grade automation, scalability, and operational excellence.
+
+For detailed information about the Kubernetes foundation, see [Kubernetes Integration](kubernetes/).
+
+### Technical Infrastructure
 
 - **Helm**: A package manager for Kubernetes that enables consistent, repeatable
   installations and version control.
 
 - **Envoy**: A high-performance service proxy that acts as the gateway for the
   Arango Data Platform for centralizing authentication and routing.
 
-- **Web interface**: The Platform includes a unified, browser-based UI that lets
-  you access its features in an intuitive way. Optional products like the
-  AI Suite seamlessly integrate into the UI if installed.
+## Platform Components
 
-## Kubernetes Integration
+The Arango Data Platform consists of multiple integrated components that work together
+to provide a complete, enterprise-ready solution.
 
-At its core, the Arango Data Platform is purpose-built for Kubernetes environments, leveraging the 
-[official ArangoDB Kubernetes Operator](https://arangodb.github.io/kube-arangodb/docs/) 
-(`kube-arangodb`) to deliver enterprise-grade automation, scalability, and operational excellence.
+### ArangoDB Enterprise Edition
 
-## Features
+At the foundation is [**ArangoDB Enterprise Edition**](../../arangodb/3.12/), the 
+powerful multi-model database that provides:
 
-The Arango Data Platform provides these core capabilities out of the box:
+- **Graph**: Native graph database capabilities with efficient traversals and pattern matching
+- **Document**: Flexible JSON document storage with schema validation
+- **Key-Value**: High-performance key-value operations
+- **Vector**: Vector embeddings and similarity search for AI applications
+- **Search**: Full-text search and complex query capabilities
 
-- [**ArangoDB Core**](../arangodb/3.12/_index.md): The ArangoDB database system with support for
-  graphs, documents, key-value, full-text search, and vector search.
+### Graph Visualizer
 
-- [**Graph Visualizer**](graph-visualizer.md):
-  A web-based tool for exploring your graph data with an intuitive interface and
-  sophisticated querying capabilities.
+The [**Graph Visualizer**](graph-visualizer/) provides an intuitive web-based interface
+that brings your data to life with:
 
-## Extend the Arango Data Platform with AI capabilities
+- **Interactive Graph Exploration**: Visualize named graphs with node expansion,
+  shortest path discovery, and AQL-powered queries including Canvas Actions that
+  work with your selection to discover related data
+
+- **Visual Customization and Layouts**: Customize node colors, icons, and labels
+  with saveable themes, and apply automatic layout algorithms (force-directed,
+  hierarchical, circular) with zoom controls and minimap navigation
+
+- **Direct Graph Editing**: Create, modify, and delete nodes and edges directly
+  from the canvas with an intuitive properties dialog supporting both form and
+  JSON editing modes
 
-Take your Arango Data Platform to the next level with the [**AI Suite**](../ai-suite/_index.md) that offers advanced AI and machine learning capabilities that integrate seamlessly into the platform's unified web interface.
+The Graph Visualizer seamlessly integrates with the ArangoDB database and provides the
+primary interface for data exploration and analysis.
 
-What you get with the AI Suite:
+### Arango Platform Suite
 
-- [GraphRAG](../ai-suite/graphrag/): Generate knowledge graphs from documents and enable
-   conversational querying of your data.
-- [GraphML](../ai-suite/graphml/): Apply machine learning algorithms that leverage graph
-  structure for better predictions.
-- [Graph Analytics](../ai-suite/graph-analytics/): Run advanced algorithms like PageRank
-  to discover influential nodes and patterns.
-- [Jupyter notebooks](../ai-suite/notebook-servers.md): Run Jupyter Notebooks to build and
-  experiment with graph-powered data, AI, and machine learning workflows directly connected
-  to ArangoDB databases. 
-- Public and private LLM support: Use public LLMs such as OpenAI
-  or private LLMs with [Triton Inference Server](../ai-suite/reference/triton-inference-server.md).
-- [MLflow integration](../ai-suite/reference/mlflow.md): Use the popular MLflow as a model registry
-  for private LLMs or to run machine learning experiments as part of the Arango Data Platform.
+The **Arango Platform Suite** adds enterprise-grade capabilities such as:
 
-{{< tip >}}
-The AI Suite integrate directly into the existing platform interface, no need for
-separate systems to manage or learn. A separate license is required.
-{{< /tip >}}
+- **High Availability and Monitoring**: Comprehensive health checks, metrics collection,
+  alerting, and automatic failover mechanisms ensure your data platform stays operational.
+  Real-time monitoring dashboards provide visibility into cluster performance,
+  resource utilization, and query patterns.
+
+- **APIs, Drivers and Connectors**: Comprehensive programmatic access through
+  RESTful APIs, native drivers for popular programming languages (Java, Python,
+  JavaScript, Go, PHP, and more), and connectors for data integration tools
+  and BI platforms.
+
+- **Centralized Orchestration and Resource Management**: Unified control plane
+  for managing all platform resources, deployments, and configurations.
+  Kubernetes-powered orchestration handles scaling, updates, and resource
+  allocation automatically across all components.
+
+These enterprise features are orchestrated through Kubernetes and the ArangoDB
+Kubernetes Operator, providing automated management and enterprise-grade reliability.

site/content/data-platform/get-started.md

@@ -21,10 +21,9 @@ of the platform features.
 
 ## Use the Arango Data Platform as a managed service
 
-The Arango Data Platform is not available as a managed service yet, but it will
-become available for the [Arango Managed Platform (AMP)](../../amp/_index.md)
-in the future. Until then, you can request early access to the self-hosted
-Arango Data Platform for testing.
+You can request the Arango Data Platform as a managed service for the
+[Arango Managed Platform (AMP)](../../amp/_index.md).
+[Get in touch](https://arango.ai/contact-us/) with the Arango team to learn more.
 
 ## Self-host the Arango Data Platform
 

site/content/data-platform/kubernetes.md

@@ -0,0 +1,81 @@
+---
+title: Kubernetes-Native Architecture
+menuTitle: Kubernetes
+weight: 15
+description: >-
+  The Arango Data Platform is purpose-built for Kubernetes, leveraging
+  container orchestration for automated deployment, scaling, and management
+---
+The Arango Data Platform is **Kubernetes-native** by design, meaning it is built
+from the ground up to run on [Kubernetes](https://kubernetes.io/) and requires
+it to function. This is not an optional feature, Kubernetes is the foundation
+that powers the entire platform architecture.
+
+{{< info >}}
+**Kubernetes Required**: The Arango Data Platform cannot operate without Kubernetes.
+It relies on Kubernetes orchestration and the 
+[ArangoDB Kubernetes Operator](https://arangodb.github.io/kube-arangodb/) for
+all deployment, scaling, and management operations.
+{{< /info >}}
+
+## Why Kubernetes?
+
+By building exclusively on Kubernetes, the Arango Data Platform delivers
+enterprise-grade capabilities that would be difficult or impossible to achieve
+with traditional deployment approaches:
+
+- **Automated Management and Self-Healing**: Kubernetes handles deployment,
+  scaling, node failures, and rolling updates automatically, with self-healing
+  capabilities that restart failed containers and maintain high availability
+  without manual intervention.
+
+- **Dynamic Scalability and Resource Optimization**: Scale your database cluster
+  up or down based on workload demands, with efficient resource allocation and
+  scheduling ensuring optimal utilization of CPU, memory, and storage.
+
+- **Declarative Configuration and Zero-Downtime Updates**: Define your desired
+  state using Kubernetes manifests and deploy updates with zero downtime through
+  controlled rolling updates and easy rollback capabilities.
+
+- **Cloud and On-Premises Flexibility**: Run on any Kubernetes-compatible
+  environment—public cloud providers (AWS, Azure, GCP), private cloud, or
+  on-premises infrastructure—with consistent deployment across all environments.
+
+## The ArangoDB Kubernetes Operator
+
+The Arango Data Platform is powered by the official
+[ArangoDB Kubernetes Operator](https://arangodb.github.io/kube-arangodb/)
+(`kube-arangodb`), which provides the following features:
+
+- **Custom Resource Definitions (CRDs)**: Extend Kubernetes with ArangoDB-specific
+  resources like `ArangoDeployment`, `ArangoBackup`, and more.
+
+- **Intelligent Orchestration**: The operator understands ArangoDB's architecture
+  and requirements, ensuring deployments follow best practices automatically.
+
+- **Backup and Restore**: Automated backup management integrated directly into
+  the Kubernetes workflow.
+
+- **High Availability**: Built-in support for multi-datacenter replication,
+  automatic failover, and disaster recovery scenarios.
+
+- **Enterprise Features**: Full support for ArangoDB Enterprise Edition features
+  including encryption, auditing, and advanced security controls.
+
+For detailed information about the operator, see the
+[ArangoDB Kubernetes Operator documentation](https://arangodb.github.io/kube-arangodb/docs/).
+
+## Platform Services as Kubernetes Resources
+
+All components of the Arango Data Platform, from the core database to the optional
+AI Suite, are deployed and managed as native Kubernetes resources. This
+means you can do the following:
+
+- Use standard Kubernetes tools (`kubectl`, Helm, etc.) to manage your deployment
+- Monitor platform health using Kubernetes-native observability tools
+- Integrate with existing Kubernetes infrastructure and workflows
+- Apply your organization's Kubernetes policies and security controls
+
+This Kubernetes-native approach ensures the Arango Data Platform fits naturally
+into modern cloud-native environments and DevOps practices.
+