Datus and Singdata Lakehouse Integration Overview

What is Datus

Datus is an open-source data engineering agent designed to build evolvable contextual environments for data systems. Datus represents a paradigm shift in data engineering: from the traditional approach of "building tables and data pipelines" to "providing domain-aware intelligent agents for analysts and business users."

CLI Quick Overview:

Web Quick Overview:

Core Components

Datus-CLI: An AI-driven command-line interface for data engineers, which can be understood as "Claude Code for data engineers." Key features include:

  • Interactive SQL Writing: Generate and optimize SQL queries through natural language
  • Subagent Building: Create domain-specific intelligent agents (subagents)
  • Context Building: Interactively build and evolve contextual knowledge for data systems

Datus-Chat: A web chatbot that provides for data analysts:

  • Multi-turn Conversations: Continuous data exploration and analysis dialogue
  • Feedback Mechanisms: Built-in feedback systems including likes, issue reporting, success cases, etc.
  • User-friendly: Optimized interface experience for non-technical users

Datus-API: A stable, accurate data service API for other agents or applications.

Technical Features

  • Multi-AI Model Support: Integrates Qwen, DeepSeek, OpenAI, Claude, and other AI models
  • Extensible Architecture: Supports MCP (Model Context Protocol) tool integration.
  • Multi-data Source Connectivity: Supports various database and data warehouse platforms.
  • Chinese Language Optimization: Specially optimized for Chinese language contexts and usage habits.

Integration Architecture

┌─────────────────────────────────────────────────────────────────┐ │ User Interface Layer │ ├──────────────────────────────┬──────────────────────────────────┤ │ Datus-CLI │ Datus-Chat │ │ (Command Line) │ (Web Interface) │ │ ┌─────────────────────────┐ │ ┌─────────────────────────────┐ │ │ │ • Natural Lang Query │ │ │ • Multi-turn Conversations │ │ │ │ • SQL Generation │ │ │ • Subagent Selection │ │ │ │ • MCP Tool Invocation │ │ │ • Feedback Mechanisms │ │ │ └─────────────────────────┘ │ └─────────────────────────────┘ │ └──────────────────────────────┴──────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Datus Agent Core │ ├─────────────────────────────────────────────────────────────────┤ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────────┐ │ │ │ AI Models │ │ Subagents │ │ Context Management │ │ │ │ │ │ │ │ │ │ │ │ • Qwen │ │ • lakehouse │ │ • Database Schema │ │ │ │ • DeepSeek │ │ • mcp_agent │ │ • Query History │ │ │ │ • OpenAI │ │ │ │ • Embedding Vectors │ │ │ │ • Claude │ │ │ │ • Knowledge Base │ │ │ └─────────────┘ └─────────────┘ └─────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘ │ ┌────────────────┴────────────────┐ ▼ ▼ (Datus-Singdata) (MCP Protocol) ┌─────────────────────────┐ ┌─────────────────────────┐ │ Data Layer │ │ Tool Extension │ ├─────────────────────────┤ ├─────────────────────────┤ │ Singdata Lakehouse │◄─────┤ Singdata MCP Server │ │ │ │ │ │ ┌─────────────────────┐ │ │ ┌─────────────────────┐ │ │ │ • Data Storage │ │ │ │ • Instance Mgmt │ │ │ │ • Compute Engine │ │ │ │ • Job Monitoring │ │ │ │ • SQL Execution │ │ │ │ • System Ops │ │ │ │ • Metadata Mgmt │ │ │ │ • Analytics │ │ │ └─────────────────────┘ │ │ └─────────────────────┘ │ │ │ │ │ │ Connection: │ │ Connection: │ │ • Service Endpoint │ │ • HTTP Transport │ │ • Username/Password │ │ • SSE Transport │ │ • Instance/Workspace │ │ • Tool Filtering │ └─────────────────────────┘ └─────────────────────────┘

Architecture Description

User Interface Layer:

  • Datus-CLI: Provides a command-line interface for data engineers
  • Datus-Chat: Provides a web interface for data analysts and business users

Datus Agent Core:

  • AI Model Layer: Supports multiple large language models, allowing selection of the most suitable model based on task type
  • Subagent Management: Different intelligent agents handle different business scenarios.
  • Context Management: Maintains the knowledge graph and query context of the data system.

Data Layer:

  • Singdata Lakehouse: Provides data storage, computing, and SQL execution capabilities

Tool Extension Layer:

  • Singdata Lakehouse MCP Server: The official MCP server provided by Singdata Lakehouse, extending system capabilities through standardized protocols and offering advanced management and analysis tools

Connection Relationship Description

  1. Datus <-> Singdata Lakehouse: Connected via the Datus-Singdata connector for database connectivity, supporting SQL query execution and metadata retrieval.
  2. Datus <-> Singdata Lakehouse MCP Server: Connected via the MCP protocol, invoking advanced management and analysis tools.
  3. Singdata Lakehouse MCP Server <-> Singdata Lakehouse: The MCP Server serves as an extension service for Singdata Lakehouse, able to access and manage the underlying data platform.

Integration Value

Datus + Singdata Lakehouse

Singdata Lakehouse, as a modern data lakehouse platform, has powerful data processing and storage capabilities. After integration with Datus:

  1. Lower the Barrier to Entry: Business users can directly query and analyze massive datasets without learning SQL
  2. Improve Analysis Efficiency: Natural language queries significantly reduce the time cost of data exploration
  3. Intelligent Insights: AI-driven query optimization and result interpretation help users better understand data
  4. Chinese-friendly: Optimized for Chinese language contexts, better suited for local users' habits.

Datus + Singdata Lakehouse MCP Server

Through integration with the official Singdata Lakehouse MCP Server, system capabilities are further extended:

  1. Instance Management: Intelligently switch between different Singdata Lakehouse instances and environments
  2. Job Monitoring: Query and analyze SQL job execution history and performance metrics.
  3. System Operations: Perform system status queries and configuration management through natural language.
  4. Advanced Analytics: Utilize specialized analysis tools for deep data insights.
  5. Workflow Automation: Encapsulate complex data processing workflows as simple natural language instructions.

Use Cases

  • Data Analysts: Quickly explore and analyze business data, generate reports and insights
  • Business Users: Users without technical backgrounds can easily query the data they need
  • Data Engineers: Perform system management and job monitoring through MCP tools
  • Decision Makers: Quickly access key business metrics and trend analysis

Last updated: November 2025

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