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:

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

Time Trend Analysis
- Monthly trip volume and revenue changes
- 24-hour usage patterns
- Weekday vs. weekend comparison
Spatial Distribution Analysis
- Identify popular pickup locations
- Discover high-value areas
- Trip distance statistics
Business Metrics Analysis
- Fare distribution and structure
- Payment method preferences
- Congestion fee impact assessment
User Behavior Analysis
- Passenger count patterns
- Trip duration distribution
- Tipping habits !

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

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!