# Singdata Lakehouse MCP Server User Case

## End-to-End Natural Language Conversational Approach from Data Acquisition to Visual Analytics

```
Can you help me create a volume? Name it NYC Green Taxi Data
The bucket is mcp-data-hangzhou, path is /nyc-green-taxi/
Download each month's data from 2025 from the NYC website into this volume
Please analyze it and generate a dashboard
```

### Case Overview

This case demonstrates how to use **Claude Desktop** with **Singdata Lakehouse MCP Server** to complete an end-to-end data science workflow from data acquisition, storage, and analysis to visualization through natural language conversation.

**Case Scenario**: Analyze New York City Green Taxi 2025 operational data and build a business insights dashboard

**Technologies Involved**:
* Claude Desktop (AI assistant interface)
* Singdata Lakehouse (cloud-native data lakehouse)
* MCP Server (Model Context Protocol service)
* NYC TLC public dataset

***

## Solution Highlights

### **Zero-Code Data Analysis**

* Describe requirements in natural language, and AI automatically executes all complex data operations
* No need to write SQL, Python, or any other code
* Automatically handles data download, cleaning, storage, and analysis

### **Enterprise-Grade Data Infrastructure**

* Cloud-native Lakehouse architecture supporting petabyte-scale data processing
* Automatic backup and version management
* High availability and security guarantees

### **Intelligent Data Insights**

* AI-driven data analysis and pattern discovery
* Automatically generates business insights and recommendations
* Interactive visualization dashboard

***

## Implementation Steps

### Step 1: Environment Preparation

```
Prerequisites:
✅ Claude Desktop client
✅ Singdata Lakehouse account
✅ MCP Server configuration
```

### Step 2: Data Acquisition and Storage

**User Input**:

> "Create a volume named 'NYC Green Taxi Data', then fetch data from the NYC website for each month of 2025"

**AI Execution Process**:

1. **Create Storage Space**
   * Automatically create external Volume: `nyc_green_taxi_data`
   * Connect OSS storage: `oss://mcp-data-hangzhou/nyc-green-taxi/`
   * Configure auto-refresh and recursive scanning

2. **Batch Data Download**
   * Intelligently identify NYC TLC official data source
   * Download Jan-Jul 2025 Parquet files
   * Automatically handle file format and compression

3. **Data Ingestion**
   * Create unified data table: `nyc_green_taxi_2025`
   * Standardize data schema and field types
   * Add data month identifier field

**Results Summary**:

* Total records: 351,612 trips
* Time span: Jan-Jul 2025
* Data size: approx. 8.5 MB (Parquet compressed)
* Processing time: <5 minutes

### Step 3: Intelligent Data Analysis

**User Input**:

> "Analyze this data and generate a dashboard"

**AI Analysis Dimensions**:

1.  **Time Trend Analysis**
    * Monthly trip volume and revenue changes
    * 24-hour usage patterns
    * Weekday vs. weekend comparison

2. **Spatial Distribution Analysis**
   * Identify popular pickup locations
   * Discover high-value areas
   * Trip distance statistics

3. **Business Metrics Analysis**
   * Fare distribution and structure
   * Payment method preferences
   * Congestion fee impact assessment

4.  **User Behavior Analysis**
    * Passenger count patterns
    * Trip duration distribution
    * Tipping habits
      \:-: ![](.topwrite/assets/image_1756976097232.png =445)

***

## Key Findings

### **Revenue Trends**

* **Peak month**: May revenue of $1.33 million, up 31.6% month-over-month
* **Average fare**: $24.45, showing an upward trend
* **Total revenue**: $8.6 million accumulated over 7 months

### **Time Patterns**

* **Peak hours**: 4-6 PM, over 25,000 trips per hour
* **Nighttime premium**: Average fare of $30+ from 2-4 AM
* **Weekend effect**: Weekend fares are 3.9% higher than weekdays

### **Geographic Distribution**

* **Popular areas**: Locations 74 and 75 contribute 38.7% of total trip volume
* **High-value points**: Location 244 averages $35.47 per fare
* **Coverage**: Primarily serves outer boroughs and Upper Manhattan

### **Payment Trends**

* **Digital adoption rate**: 74% use credit card payments
* **Cash share**: 26%, showing a declining trend
* **Congestion fee**: 9.6% of trips include it, averaging $0.75

***

## Visualization Dashboard

### Interface Features
* **Responsive design**: Adapts to desktop, tablet, and mobile
* **Interactive charts**: Supports zoom, filter, and drill-down
* **Real-time data**: Configurable auto-refresh
* **Modern UI**: Gradient backgrounds, animated effects

### Chart Types

* 📈 **Trend charts**: Dual-axis display of monthly revenue and trip volume
* 📊 **Bar charts**: 24-hour usage patterns, popular location rankings
* 🍩 **Donut charts**: Fare distribution, payment method breakdown
* 📋 **Metric cards**: Key KPIs at a glance

***

## Business Value

### 🚀 **Efficiency Gains**
* **Development time**: Reduced from weeks to minutes
* **Technical barrier**: Zero code, business users can directly use it
* **Iteration speed**: Rapid response to changing requirements

### **Insight Quality**

* **Multi-dimensional analysis**: Full coverage of time, space, and user behavior
* **Intelligent discovery**: AI identifies patterns easily overlooked by humans
* **Decision support**: Direct business recommendations and predictions

### **Cost Optimization**

* **Infrastructure**: Cloud-native architecture, pay-as-you-go
* **Human resources**: Reduced dependency on specialized data teams
* **Maintenance cost**: Automated operations, lower management overhead

***

## Applicable Scenarios

### 📊 **Business Analytics**
* Sales data analysis and forecasting
* Customer behavior insights
* Market trend research
* Competitive analysis

### **Operations Optimization**

* Supply chain efficiency analysis
* Equipment maintenance prediction
* Quality control monitoring
* Cost structure optimization

### **Product Analytics**

* User retention analysis
* Feature usage statistics
* A/B test evaluation
* Product roadmap planning

### **Marketing Analytics**

* Advertising effectiveness evaluation
* Channel ROI analysis
* User profiling
* Precision marketing strategies

***

## Core Tools Deep Dive

### 1. Claude Desktop

**Role**: Intelligent conversational interface and AI assistant

**Core Capabilities**:

* 🤖 **Natural language understanding**: Converts user requirements into specific operational commands
* 🔄 **Multi-modal interaction**: Supports text, charts, code, and other output formats
* 🧠 **Intelligent reasoning**: Performs logical reasoning and decision-making based on context
* 📋 **Task orchestration**: Automatically plans and executes complex data workflows

**Application in this case**:
* Understands natural language commands such as "create a volume" and "download data"
* Automatically plans the complete workflow from data acquisition to visualization
* Intelligently analyzes data patterns and generates business insights
* Creates interactive HTML dashboards

### 2. MCP Server (Model-Context Protocol)

**Role**: Bridge between AI and external systems

**Core Capabilities**:

* 🔌 **Protocol translation**: Converts AI commands into database operations
* 🛡️ **Security proxy**: Provides secure data access control
* ⚡ **Efficient communication**: Optimizes interaction performance between AI and data systems
* 🔧 **Tool integration**: Supports 70+ specialized data operation tools

**Key MCP Tools**:

#### 📊 Data Management Tools

```
✓ create_volume - Create data storage volumes
✓ import_data_src - Import data from URLs
✓ preview_volume_data - Data preview and validation
✓ list_files_on_volume - File management
```

#### 🏗️ Table Structure and Volume Tools

```
✓ create_table - Create data tables
✓ desc_object - View object details
✓ show_object_list - List database objects
✓ alter_table - Modify table structure
✓ create_volume - Create data lake storage volumes
```

#### 🔍 Query and Analysis Tools

```
✓ read_query - Execute SELECT queries
✓ write_query - Execute write operations
✓ vector_search - Vector similarity search
✓ match_all - Full-text search
```

#### 🌐 Data Acquisition Tools

```
✓ smart_crawl_to_volume - Intelligent web data crawling
✓ crawl_single_page - Single page content extraction
✓ import_data_from_db - Database data import
```

#### 🔗 Connection Management Tools

```
✓ create_storage_connection - Storage connection configuration
✓ create_api_connection - API service connection
✓ switch_context - Context environment switching
```

### 3. Singdata Lakehouse

**Role**: Cloud-native data lakehouse integrated platform

**Core Architecture**:

* 🏗️ **Storage-compute separation**: Independent scaling, cost optimization
* 📊 **Multi-mode support**: Batch processing, stream processing, interactive queries
* ⚡ **Query acceleration**: Intelligent caching and pre-computation

**Data Storage Layer**:

* **Volume management**: Unified data storage abstraction
* **Multi-cloud support**: OSS, S3, COS, and other object storage
* **Format compatibility**: Parquet, ORC, JSON, CSV, and more
* **Version control**: Data lineage and time travel

**Compute Engine**:

* **SQL engine**: Standard SQL, compatible with multiple dialects
* **Vector computing**: Supports AI/ML workloads
* **Stream processing**: Real-time data pipelines
* **Federated queries**: Unified queries across data sources

**Application in this case**:

* Created `nyc_green_taxi_data` volume for storing raw data
* Established `nyc_green_taxi_2025` table for structured storage
* Executed complex multi-dimensional analysis queries
* Supported sub-second query response for 350,000+ records

### 4. External Data Sources

**NYC TLC (Taxi & Limousine Commission)**

**Data Characteristics**:
* 📅 **Regular updates**: Monthly releases, 2-month latency
* 📊 **Data quality**: Officially authoritative, standardized format
* 💾 **Storage format**: Parquet compressed format
* 🌐 **Access method**: CDN accelerated download

**Data Fields** (21 core fields):

```
• vendorid - Service provider ID
• lpep_pickup_datetime - Pickup time
• lpep_dropoff_datetime - Dropoff time
• pulocationid/dolocationid - Pickup/Dropoff location
• trip_distance - Trip distance
• fare_amount - Base fare
• total_amount - Total fare
• payment_type - Payment method
• cbd_congestion_fee - Congestion fee (new in 2025)
```

### 5. Visualization Component (Claude Desktop Artifacts)

**Chart.js + Custom HTML**

**Chart Types**:

* 📈 **Dual-axis line charts**: Monthly trends (trip volume + revenue)
* 📊 **Bar charts**: 24-hour patterns, popular locations
* 🍩 **Donut charts**: Fare distribution, payment methods
* 📋 **Metric cards**: Key KPI display

**Interactive Features**:

* ✨ **Hover effects**: Display detailed values
* 🔄 **Responsive**: Adapts to different screen sizes
* 🎨 **Animations**: Smooth transition effects
* 📱 **Mobile-friendly**: Touch optimized

## Technical Architecture

### Data Flow Architecture

:-: ![](.topwrite/assets/image_1756975969606.png =372)

### Tool Call Examples

Actual tool call sequence in this case:

```
1. get_current_context() - Retrieve current environment information
2. create_volume() - Create nyc_green_taxi_data volume
3. import_data_src() × 7 - Batch download 7 months of data files
4. create_table() - Create nyc_green_taxi_2025 unified table
5. write_query() × 7 - Insert each month's data into the main table
6. read_query() × 8 - Execute multi-dimensional analysis queries
7. add_data_insight() - Save analysis insights
8. artifacts.create() - Generate visualization dashboard
```

### Performance Optimization Features

* **Query cache**: Millisecond-level response for repeated queries
* **Intelligent indexing**: Automatic index creation for frequently queried fields
* **Partitioning strategy**: Monthly partitioning to improve query efficiency
* **Compressed storage**: Parquet format saves 90% storage space
* **Parallel processing**: Concurrent multi-file download and processing

***

## Quick Start

### 1. Apply for Trial
* Visit the Singdata official website to apply for an account
* Download and install the Claude Desktop client
* Configure the MCP Server connection

### 2. Prepare Data

* Define analysis goals and data sources
* Prepare data access permissions
* Plan storage structure

### 3. Start the Conversation

```
Example dialogue:
User: "I want to analyze our company's sales data"
AI: "Sure, let me help you with that analysis..."
```

### 4. Obtain Insights

* Review automatically generated analysis reports
* Engage in conversational deep dives with AI
* Export results and visualizations

***

## Summary

Singdata Lakehouse MCP Server, combined with Claude Desktop, provides enterprises with an unprecedented data analysis experience:

### **Core Advantages**
* **Natural interaction**: Complete complex data analysis through conversation
* **Zero technical barrier**: Business users directly obtain insights
* **Enterprise-grade capability**: Supports large-scale, high-concurrency scenarios
* **Rapid iteration**: From idea to insight in minutes

### **Innovative Breakthroughs**

* Redefining human-machine collaboration models
* Dramatically lowering the barrier to data analysis
* Accelerating enterprise digital transformation
* Unlocking the true value of data

### **Future Outlook**

As AI technology continues to advance, this "conversational data analysis" approach will become an enterprise standard, enabling every business user to become a data analyst and letting data truly drive every business decision.

***

Start your intelligent data analysis journey today!
