Singdata Lakehouse Multi-Cloud Multi-Environment Management Guide

Overview

This document describes how to achieve unified management of multi-cloud and multi-environment setups through Claude Desktop and Singdata Lakehouse MCP Server, providing enterprise users with a simple and efficient data lakehouse management experience and enabling end-to-end natural language conversational interaction.


What Lakehouse environments do I have?
Help me check which workspaces are in each instance of the production environment
Help me switch to the Tencent Cloud Shanghai production environment
What is my current context?
What workspaces are available?
Help me switch to test_ws
What compute clusters and schemas do I have?
Help me switch to DEFAULT

Use Cases

In the process of enterprise digital transformation, the following multi-environment management challenges are common:

Multi-cloud deployment: Simultaneously using Alibaba Cloud, Tencent Cloud, AWS, and other cloud providers
Environment isolation: Development, testing, and production environments need independent management
Regional distribution: Data centers in different regions such as Shanghai, Beijing, Guangzhou
Resource switching: Frequent switching between different environments for operations

Traditional approaches require logging into multiple consoles, which is complex and error-prone. This solution provides a unified approach.

Technical Architecture

Core Components

Claude Desktop: AI assistant client providing a natural language interaction interface
Singdata Lakehouse MCP Server: Model Context Protocol server connecting Claude with Singdata Lakehouse
Singdata Lakehouse: Cloud-native data lakehouse platform supporting multi-cloud deployment

Technical Advantages

Natural language interaction: Manage complex multi-cloud environments through conversation
Unified management interface: One client to manage all cloud environments
Real-time status monitoring: Check environment health status at any time
Intelligent switching: Automatically handle connection establishment and environment switching

Quick Start

Step 1: Environment Configuration

First, configure the connection information for multiple Lakehouse instances (simplified):


{
  "connections": {
    "aliyun_shanghai_prod": {
      "service": "cn-shanghai-alicloud.api.singdata.com",
      "workspace": "quick_start",
      "schema": "mcp_demo"
    },
    "tencent_shanghai_prod": {
      "service": "ap-shanghai-tencentcloud.api.singdata.com", 
      "workspace": "quick_start",
      "schema": "mcp_demo"
    },
    "aws_north-1_prod": {
      "service": "cn-north-1-aws.api.singdata.com",
      "workspace": "quick_start", 
      "schema": "public"
    }
  }
}

Step 2: Launch Claude Desktop

Install and launch Claude Desktop, ensuring the Singdata MCP Server is configured.

Step 3: Start Managing

Interact with Claude using natural language to begin multi-cloud environment management.

Real-World Use Case

The following is a real multi-cloud environment management conversation example:

Environment Discovery

User: "What Lakehouse environments do I have?"

System Response: Automatically scans and displays all configured environments:


✅ Found 9 connection configurations

🟢 Currently Active Environment
aliyun_shanghai_prod (Default environment)

📋 All Available Environments
Production Environments:
1. aliyun_shanghai_prod ⭐ (Currently in use)
2. tencent_shanghai_prod  
3. tencent_beijing_prod
4. tencent_guangzhou_prod
5. aws_north-1_prod

Test Environments:
6. uat
7. uat_1  
8. uat_1_1
9. uat_2

Batch Environment Exploration

User: "Help me check which workspaces are in each instance of the production environment"

System Operations:

Automatically traverses all production environments
Connects to each cloud provider instance one by one
Collects workspace information
Generates a unified report

Results Display:

Environment	Workspace Count	Details
Alibaba Cloud Shanghai	4	quick_start, lakehouse_quick_experience, ns227206, spark_migration
Tencent Cloud Shanghai	2	quick_start, test_ws
Tencent Cloud Beijing	1	quick_start
Tencent Cloud Guangzhou	1	quick_start
AWS North-1	2	quick_start, iceberg_lakehouse_s3

Intelligent Environment Switching

User: "Help me switch to the Tencent Cloud Shanghai production environment"

System Processing:


✅ Switch successful!
Original environment: aliyun_shanghai_prod  
New environment: tencent_shanghai_prod
Service endpoint: ap-shanghai-tencentcloud.api.singdata.com
Processing time: 1.13 seconds

📍 Current Environment Configuration
Workspace: quick_start
Schema: mcp_demo  
Virtual cluster: default_ap

Context Awareness

User: "What is my current context?"

System Response:


📍 Your Current Context Information
Connected environment: Tencent Cloud Shanghai production environment

🔧 Environment Configuration
Instance ID: 270738
Workspace: quick_start  
Data schema: mcp_demo
Virtual cluster: default_ap

👤 User Information
Username: qiliang
User ID: 2162115

Resource Management

User: "What compute clusters and schemas do I have?"

System Analysis:


⚡ Compute Clusters (VCLUSTERS) - 2
1. DEFAULT (General-purpose) - SUSPENDED 
2. DEFAULT_AP (Analytical) - RUNNING ⭐

📁 Schemas (Data schemas) - 2  
1. information_schema (System schema)
2. public (Public schema)

Core Feature Details

1. Environment Discovery and Management

Automatic scanning: Discover all configured Lakehouse instances
Health check: Real-time monitoring of environment connection status
Intelligent classification: Automatically group by cloud provider, region, and purpose

2. Seamless Environment Switching

One-click switching: Switch environments with natural language commands
Connection management: Automatically handle connection establishment and disconnection
State persistence: Maintain context information after switching

3. Unified Resource View

Workspace management: View and switch workspaces
Compute resources: Manage virtual clusters (VCluster)
Data organization: View schemas and table structures

4. Intelligent Operation Suggestions

Context awareness: Understand the current environment
Operation hints: Provide suggestions for next steps
Error handling: Automatically handle connection exceptions

Management Tool Introduction

Singdata Lakehouse MCP Server

Features:

50+ data operation tools
Multi-cloud environment connection management
Real-time status monitoring
Intelligent error recovery

Core Tools:

Tool Category	Main Functions	Typical Tools
Environment Management	Instance switching, context viewing	`switch_lakehouse_instance`, `switch_workspace`, `get_current_context`
Resource Management	Object viewing, creation, deletion	`show_object_list`, `create_table`
Data Operations	Query, import, export	`read_query`, `import_data_src`
Compute Management	Cluster management, task monitoring	`alter_vcluster`, `show_job_history`

Claude Desktop

Interaction Advantages:

Natural language understanding
Context memory
Intelligent suggestions
Visual display

Enterprise Application Scenarios

Scenario 1: Development and Operations


Developer: "Switch to the development environment and check today's task execution status."
System: Automatically switches to the development environment and displays task history and status.

Scenario 2: Data Analysis


Analyst: "Switch to the production environment and view the sales data table structure."
System: Switches environment and displays table schema and data preview.

Scenario 3: Cross-Cloud Comparison


Architect: "Compare resource configurations across different cloud environments."
System: Generates a cross-cloud environment resource comparison report.

Best Practices

1. Environment Naming Conventions

Cloud provider identifier: aliyun, tencent, aws
Region identifier: shanghai, beijing, guangzhou
Environment type: prod, test, dev

Example: tencent_shanghai_prod

2. Permission Management

Production environment: Restrict access to specific users
Test environment: Shared by the development team
Development environment: Personal use

Conclusion

The Singdata Lakehouse multi-cloud multi-environment management solution provides enterprises with a unified, intelligent, and efficient data lakehouse management experience. Through natural language interaction, it significantly reduces the complexity of multi-cloud environment management and improves operational efficiency. As AI technology continues to evolve, we will keep optimizing the product experience to create greater value for users.