AI-Ready Enterprise Lakehouse Platform AI就绪的企业级湖仓一体平台

The Future of Manufacturing Data Intelligence 制造业数据智能的未来

LongDB is an AI-ready lakehouse platform that unifies data lakes and data warehouses, enabling enterprises to harness the full potential of their data for analytics, AI/ML, and real-time decision making—designed specifically for semiconductor and high-end manufacturing.

LongDB 是一个 AI 就绪的湖仓一体平台,融合数据湖与数据仓库的优势,助力企业充分释放数据价值,实现高效分析、AI/ML 应用与实时决策——专为半导体和高端制造业打造。

10x10倍
Faster Analytics分析性能提升
60%
Lower TCO总拥有成本降低
8+
Enterprise Clients企业级客户
1B+10亿+
Records/Day日处理记录
7×24
Production Stability稳定生产运行
PB+
Data Managed数据管理规模

The Data Silo Problem in Manufacturing制造业的数据孤岛问题

Modern manufacturing enterprises accumulate multiple analytics platforms over time. Each system may have been right for its specific use case, but the aggregate result is fragmented data that limits AI/ML adoption.

现代制造企业随着时间推移积累了多个分析平台。每个系统可能适合其特定用例,但整体结果是数据碎片化,阻碍了 AI/ML 的落地应用。

Fragmented Analytics分析系统碎片化

"Oracle is slow, add an MPP data warehouse for analytics" — each application maintains its own data copy. Same KPI shows different values in different systems.

"Oracle 太慢,加个 Doris/ClickHouse 做分析"——每个应用都维护自己的数据副本,同一 KPI 在不同系统中显示不同值。

High TCO & Complexity高成本与复杂性

Multiple licenses, multiple support contracts. 30-40% of IT resources consumed by data sync and integration pipelines in fragmented architectures.

多个许可证,多份支持合同。在碎片化架构中,30-40% 的 IT 资源消耗在数据同步和集成管道上。

Slow Time-to-Insight洞察时间过长

Dashboard data may be hours or days old. Data scientists spend 60-80% of time on data preparation instead of building models.

仪表盘数据可能是数小时或数天前的。数据科学家花费 60-80% 的时间在数据准备上,而不是构建模型。

AI/ML Adoption BarriersAI/ML 落地困难

AI initiatives blocked by scattered, inconsistent data. Feature engineering requires access to multiple disconnected systems.

AI 项目被分散、不一致的数据阻碍。特征工程需要访问多个断开连接的系统。

📋 The Core Thesis: The strategic value of a manufacturing data platform is not "query speed" — it's 3-5 years of evolution without rearchitecting. Point solutions may solve immediate issues, but often create Data Silos that compound over time.
📋 核心理念:制造业数据平台的战略价值不在于"查询速度"——而在于3-5年的平台演进无需重构。单点解决方案可能解决眼前问题,但往往造成日积月累的数据孤岛。

LongDB: Unified Lakehouse PlatformLongDB:统一的湖仓一体平台

A single platform that combines the best of data warehouses and data lakes, purpose-built for modern AI workloads in manufacturing environments.

一个融合数据仓库和数据湖优势的统一平台,专为制造业现代 AI 工作负载量身打造。

Lakehouse Architecture湖仓一体架构

Unified storage for structured, semi-structured, and unstructured data with full ACID transactions and schema evolution. Virtual Table mechanism supports flexible and extensible storage options.

统一存储结构化、半结构化和非结构化数据,具备完整的 ACID 事务和 Schema 演进能力。虚拟表机制支持灵活可扩展的存储选项。

AI-Native DesignAI 原生设计

Built-in ML runtime, feature store, model registry, and SupraBrain AI engine for seamless integration of AI/ML workloads with your data.

内置 ML 运行时、特征存储、模型注册表和 SupraBrain AI 引擎,实现 AI/ML 工作负载与数据的无缝集成。

Hybrid Cloud Ready混合云就绪

Deploy on-premises, private cloud, or public cloud. Full data sovereignty with seamless migration and consistent APIs across environments.

支持本地部署、私有云或公有云。完全数据主权,无缝迁移,跨环境一致的 API。

Real-time + Batch Unified实时+批处理统一

Single SQL interface for BI, reporting, streaming analytics, and ad-hoc queries. No data movement between systems required.

单一 SQL 接口支持 BI、报表、流式分析和即席查询。无需在系统间移动数据。

Two Powerful Products, One Vision两大产品,一个愿景

LongDB PlatformLongDB 平台

The core lakehouse platform providing unified data storage, processing, and analytics capabilities for enterprise-scale manufacturing workloads.

核心湖仓一体平台,为企业级制造业工作负载提供统一的数据存储、处理和分析能力。

  • Unified lakehouse with ACID transactions
  • Time travel & schema evolution capability
  • Real-time streaming ingestion (Kafka, CDC)
  • Virtual Table for extensible storage options
  • Data Science Studio for notebooks & ML
  • Enterprise security, governance & lineage
  • 统一湖仓,支持 ACID 事务
  • 时间旅行与 Schema 演进能力
  • 实时流式摄入(Kafka、CDC)
  • 虚拟表机制支持可扩展存储选项
  • 数据科学工作室(Notebook 与 ML)
  • 企业级安全、治理与血缘追踪

SupraBrain AI EngineSupraBrain AI 引擎

Next-generation agentic AI engine that brings intelligent automation and natural language interfaces to your data operations.

新一代智能体 AI 引擎,为数据操作带来智能自动化和自然语言交互能力。

  • Natural language to SQL generation
  • Autonomous data agents with reasoning
  • Intelligent query optimization
  • Automated insight discovery & alerting
  • Multi-modal data analysis (images, docs)
  • Enterprise model integration (GPT, Claude, Gemini, Qwen, DeepSeek)
  • 自然语言转 SQL 生成
  • 具备推理能力的自主数据智能体
  • 智能查询优化
  • 自动洞察发现与告警
  • 多模态数据分析(图像、文档)
  • 企业级模型集成(GPT、Claude、Gemini、Qwen、DeepSeek)

🏭 Production Deployments生产环境部署案例

LongDB has production deployments in semiconductor and high-end manufacturing enterprises:

LongDB 已在半导体和高端制造企业中投入生产使用:

🔬 Semiconductor Fab (300mm) ⚙️ Precision Gear Manufacturer 🏠 IKEA Global Supplier 🚗 Global Automotive Glass Manufacturer
🔬 半导体晶圆厂(300mm) ⚙️ 精密齿轮制造商 🏠 宜家全球供应商 🚗 全球汽车玻璃制造商

Client names available under NDA. Reference calls can be arranged during evaluation.

客户名称可在签署保密协议后提供。评估期间可安排参考客户沟通。

Unified Data + AI Platform for Manufacturing面向制造业的统一数据+AI平台

LongDB Platform is an enterprise-grade lakehouse that unifies data management, analytics, and AI capabilities in a single, coherent platform designed for semiconductor and high-end manufacturing.

LongDB 平台是企业级湖仓一体系统,将数据管理、分析和 AI 能力统一在一个为半导体和高端制造业设计的一致性平台中。

Application Scenarios Data Integration Real-time Analytics Self-Service BI ML/AI Training Predictive Quality Process Optimization Data Sources 🏭 MES/MOM 📊 ERP/SAP 🔧 Equipment 📈 Test Data 🖼️ Vision/AOI 📁 Files/Logs ⏱️ Time Series Batch CDC Ingestion Kafka Connect CDC Spark Streaming Bulk Loaders File Loaders Batch + Stream Unified Pipeline LongDB Data + AI Platform Compute Layer SQL Engine Spark Runtime ML Runtime Query Optimizer Platform Services Data Catalog Governance Security Lineage Quality Scheduling SupraBrain AI Feature Store Model Registry Lakehouse Storage Native/Virtual Table Block/Object Storage Time Travel Agile BI Data Science Studio Data Pipe Monitoring APIs Consumers 📊 Tableau / Power BI 📈 Looker / Spotfire 🔬 Data Science Tools 🤖 ML Models 📱 Custom Applications 🔗 REST / GraphQL APIs JDBC / ODBC / REST Existing OLTP Systems (Retained) Oracle PostgreSQL SQL Server MES DB CDC replication to LongDB for analytics Infrastructure Layer — Deploy Anywhere Kubernetes On-Premises Public Cloud Private Cloud MinIO / S3 Hybrid Multi-Cloud LongDB Platform SupraBrain AI / Consumers Data Sources Existing OLTP (Retained) Ingestion Layer 应用场景 数据集成 实时分析 自助 BI ML/AI 训练 预测性质量 工艺优化 数据源 🏭 MES/MOM 📊 ERP/SAP 🔧 设备数据 📈 测试数据 🖼️ 视觉/AOI 📁 文件/日志 ⏱️ 时序数据 批量 CDC 数据摄入 Kafka Connect CDC Spark Streaming 批量加载器 文件加载器 批量 + 流式 统一管道 LongDB 数据 + AI 平台 计算层 SQL 引擎 Spark 运行时 ML 运行时 查询优化器 平台服务 数据目录 数据治理 安全 血缘 质量 调度 SupraBrain AI 特征存储 模型注册中心 湖仓存储 内表/虚拟表 块存储/对象存储 时间旅行 敏捷 BI 数据科学工作室 数据管道 监控 API 数据消费者 📊 Power BI / Spotfire 📈 帆软 / SmartBI 🔬 数据科学工具 🤖 ML 模型 📱 定制应用 🔗 REST / GraphQL API JDBC / ODBC / REST 现有 OLTP 系统(保留) Oracle PostgreSQL SQL Server MES 数据库 CDC 复制到 LongDB 进行分析 基础设施层 — 随处部署 Kubernetes 本地部署 公有云 私有云 MinIO / S3 混合多云 LongDB 平台 SupraBrain AI / 消费者 数据源 现有 OLTP(保留) 数据摄入层
Architecture Note: This is a "complement and consolidate" approach, not "rip and replace." Existing OLTP systems remain for transactional workloads while LongDB consolidates the analytics layer. CDC provides near-real-time replication to minimize ETL latency.
架构说明:这是一种"补充与整合"方法,而非"推倒重来"。现有 OLTP 系统继续处理事务性工作负载,而 LongDB 整合分析层。CDC 提供近实时复制,最大限度减少 ETL 延迟。

Enterprise-Grade Features企业级功能

Unified Lakehouse Storage统一湖仓存储

Native storage engine with ACID transactions, time travel, and schema evolution. Virtual Table mechanism supports flexible and extensible storage options.

原生存储引擎,支持 ACID 事务、时间旅行和 Schema 演进。虚拟表机制支持灵活可扩展的存储选项。

Real-time Streaming实时流处理

Kafka Connect, CDC, Spark Streaming, and Bulk Loaders for ingesting millions of events per second with exactly-once semantics.

Kafka Connect、CDC、Spark Streaming 和批量加载器,每秒摄入数百万事件,支持精确一次语义。

Data Governance数据治理

Built-in data catalog, lineage tracking, access control (RBAC), data quality monitoring, and audit logging.

内置数据目录、血缘追踪、访问控制(RBAC)、数据质量监控和审计日志。

Agile BI敏捷 BI

Interactive dashboards, drag-and-drop visualization builder, and self-service analytics for business users.

交互式仪表盘、拖拽式可视化构建器,为业务用户提供自助分析能力。

Data Science Studio数据科学工作室

Jupyter-compatible notebooks with support for Python, R, Scala, and SQL. Integrated with feature store and model registry.

兼容 Jupyter 的笔记本,支持 Python、R、Scala 和 SQL。与特征存储和模型注册表集成。

ML IntegrationML 集成

Native ML runtime, feature store, model registry, and MLflow integration. Train and deploy models directly on platform data.

原生 ML 运行时、特征存储、模型注册表和 MLflow 集成。直接在平台数据上训练和部署模型。

Agentic AI Engine 智能体 AI 引擎

SupraBrain AI

An enterprise-grade agentic AI engine built on four core pillars: conversational interface, RAG-powered knowledge, context-aware data agents, and extensible plugin architecture. SupraBrain transforms how organizations interact with and extract value from their data.

企业级智能体 AI 引擎,基于四大核心支柱:对话式界面、RAG 驱动的知识库、上下文感知的数据智能体和可扩展的插件架构。SupraBrain 重新定义企业与数据的交互方式,释放数据价值。

Four Pillars of Intelligent Data Interaction智能数据交互的四大支柱

SupraBrain combines conversational AI, retrieval-augmented generation, domain-aware data agents, and a flexible plugin ecosystem into a unified agentic engine.

SupraBrain 将对话式 AI、检索增强生成、领域感知数据智能体和灵活的插件生态系统融合为统一的智能体引擎。

1. Conversational Interface1. 对话式界面

ChatGPT-style InteractionChatGPT 式交互

Intuitive chat interface for natural language queries in English, Chinese, and other languages. Ask questions, get answers, visualizations, and actionable insights without writing code. Maintains conversation context for follow-up questions and iterative exploration.

直观的聊天界面,支持中英文等多语言自然语言查询。无需编写代码即可提问、获取答案、可视化和可操作洞察。保持对话上下文,支持后续问题和迭代探索。

Multi-turn Conversations多轮对话 Multi-lingual多语言 Voice Input语音输入

2. RAG Knowledge Base2. RAG 知识库

Retrieval-Augmented Generation检索增强生成

Enterprise knowledge base powered by RAG technology. Ingest documents, manuals, SOPs, and domain knowledge to provide accurate, context-aware responses. Vector embeddings enable semantic search across unstructured content with real-time retrieval.

由 RAG 技术驱动的企业知识库。摄入文档、手册、SOP 和领域知识,提供准确、上下文感知的响应。向量嵌入支持非结构化内容的语义搜索和实时检索。

Vector Store向量存储 Semantic Search语义搜索 Document Ingestion文档摄入

3. Data Agent3. 数据智能体

Context & Domain Model Awareness上下文与领域模型感知

Intelligent data agent that deeply understands your data context, schema relationships, and domain-specific data models. Automatically generates accurate SQL, performs multi-step reasoning, and executes complex analytical workflows with full data lineage awareness.

智能数据代理深度理解数据上下文、Schema 关系和领域特定数据模型。自动生成准确的 SQL,执行多步推理,在完整数据血缘感知下执行复杂分析工作流。

Schema UnderstandingSchema 理解 NL-to-SQL自然语言转 SQL Domain Models领域模型

4. Plugin/MCP Architecture4. 插件/MCP 架构

Extensibility & Integration可扩展性与集成

Open plugin architecture based on Model Context Protocol (MCP) for seamless extensibility. Connect to external tools, APIs, and enterprise systems. Build custom capabilities while maintaining security and governance controls across all integrations.

基于模型上下文协议(MCP)的开放插件架构,实现无缝扩展。连接外部工具、API 和企业系统。构建自定义能力,同时在所有集成中保持安全和治理控制。

MCP ProtocolMCP 协议 Custom Plugins自定义插件 API ConnectorsAPI 连接器
SupraBrain Agentic Engine Architecture USER INTERFACE 💬 Chat UI 🎤 Voice 🔌 API SUPRABRAIN CORE ENGINE 1. Conversational Interface Multi-turn • Context-aware • Multi-lingual 2. RAG Knowledge Base Vector Store • Semantic Search • Docs 3. Data Agent Schema • Domain Models • NL-to-SQL 4. Plugin/MCP Architecture Extensibility • Custom Tools • APIs MODEL PROVIDERS GPT Claude Gemini Qwen DeepSeek On-Prem LLM/LVM/VLM/ML 🔒 Row-level Security • Data Masking • Audit Logs • RBAC • Compliance DATA LAYER 📊 Lakehouse 📁 Data Catalog 🔗 Lineage 📈 Metrics 🏷️ Metadata 📋 Models 🔍 Search ⚙️ APIs 1. Conversational 2. RAG Knowledge 3. Data Agent 4. Plugin/MCP SupraBrain 智能体引擎架构 用户界面 💬 聊天界面 🎤 语音输入 🔌 API SUPRABRAIN 核心引擎 1. 对话式界面 多轮对话 • 上下文感知 • 多语言 2. RAG 知识库 向量存储 • 语义搜索 • 文档 3. 数据智能体 Schema 理解 • 领域模型 • NL-to-SQL 4. 插件/MCP 架构 可扩展性 • 自定义工具 • API 模型提供商 GPT Claude Gemini Qwen DeepSeek 本地 LLM/LVM/VLM/ML 🔒 行级安全 • 数据脱敏 • 审计日志 • RBAC • 合规 数据层 📊 湖仓 📁 数据目录 🔗 血缘 📈 指标 🏷️ 元数据 📋 模型 🔍 搜索 ⚙️ API 1. 对话式 2. RAG 知识库 3. 数据智能体 4. 插件/MCP
SupraBrain Agentic Engine Architecture USER INTERFACE 💬 Chat UI 🎤 Voice 🔌 API SUPRABRAIN CORE ENGINE Conversational Interface Multi-turn • Context-aware • Multi-lingual RAG Knowledge Base Vector Store • Semantic Search • Docs Data Agent Schema • Domain Models • NL-to-SQL Plugin/MCP Architecture Extensibility • Custom Tools • APIs MODEL PROVIDERS GPT Claude Gemini Qwen DeepSeek On-Prem LLM/LVM/VLM/ML 🔒 Row-level Security • Data Masking • Audit Logs • RBAC • Compliance DATA LAYER 📊 Lakehouse 📁 Data Catalog 🔗 Lineage 📈 Metrics 🏷️ Metadata 📋 Models 🔍 Search ⚙️ APIs Conversational RAG Knowledge Data Agent Plugin/MCP

Intelligence at Every Layer全层智能

Intelligent Query Optimization智能查询优化

AI-powered query analysis that suggests indexes, rewrites inefficient queries, and automatically tunes performance.

AI 驱动的查询分析,建议索引,重写低效查询,自动调优性能。

Automated Insight Discovery自动洞察发现

Proactively surfaces anomalies, trends, and patterns in your data. Get alerted to important changes before they become problems.

主动发现数据中的异常、趋势和模式。在问题发生前获得重要变化的警报。

Multi-modal Analysis多模态分析

Analyze images, documents, and sensor data alongside structured data. Perfect for manufacturing quality control.

结合结构化数据分析图像、文档和传感器数据。非常适合制造业质量控制。

SupraBrain in ActionSupraBrain 实战案例

🎯 Self-Service Analytics自助分析

"Show me yield by equipment for last month, compared to target, and highlight any equipment below 95%"

"显示上个月按设备划分的良率,与目标对比,并高亮显示低于 95% 的设备"

→ Generates SQL, creates visualization, adds commentary→ 生成 SQL,创建可视化,添加注释

🔍 Root Cause Analysis根因分析

"Why did our yield rate drop on production line 3 last Tuesday afternoon?"

"为什么上周二下午 3 号生产线的良率下降了?"

→ Correlates sensor data, identifies anomalies, suggests causes→ 关联传感器数据,识别异常,建议原因

📊 Report Automation报表自动化

"Generate a weekly executive summary of key manufacturing metrics and email it every Monday"

"生成关键制造指标的每周高管摘要,每周一通过邮件发送"

→ Creates automated pipeline, schedules delivery→ 创建自动化管道,调度交付

🤖 Predictive Maintenance预测性维护

"Alert me when any equipment shows signs of potential failure in the next 48 hours"

"当任何设备在未来 48 小时内显示潜在故障迹象时提醒我"

→ Deploys ML model, sets up real-time monitoring→ 部署 ML 模型,设置实时监控

🧠 LLM IntegrationLLM 集成

SupraBrain supports multiple model providers including GPT, Claude, Gemini, Qwen, and DeepSeek. Organizations can use cloud-hosted models or deploy on-premises LLM/LVM/VLM/ML models for full data privacy. Custom fine-tuning available for domain-specific terminology.

SupraBrain 支持多种模型提供商,包括 GPT、Claude、Gemini、Qwen 和 DeepSeek。企业可以使用云托管模型或本地部署 LLM/LVM/VLM/ML 模型以确保数据隐私。支持针对领域特定术语的自定义微调。

Lakehouse Architecture Explained湖仓架构详解

LongDB combines the best of data warehouses (reliability, governance, performance) with data lakes (flexibility, scale, cost) in a single unified architecture.

LongDB 将数据仓库的优势(可靠性、治理、性能)与数据湖的优势(灵活性、规模、成本)融合在单一统一架构中。

Traditional Architecture vs LongDB Lakehouse TRADITIONAL (Lake + Warehouse) DATA LAKE Raw Data Storage Schema on Read No ACID ✗ Poor Governance ✗ Data Swamps ✓ Low Cost ETL DATA WAREHOUSE Curated Data Schema on Write Full ACID ✓ Fast Queries ✓ Governance ✗ High Cost ⚠ PROBLEMS • Data duplication & inconsistency • Complex ETL maintenance • High TCO • Slow time-to-insight • Limited ML/AI support LONGDB LAKEHOUSE UNIFIED LAKEHOUSE STORAGE ACID • Time Travel • Schema Evolution • All Data Types METADATA & GOVERNANCE SQL + SPARK ML RUNTIME SUPRABRAIN AI ENGINE ✓ BENEFITS • Single source of truth • 60% lower TCO • Real-time analytics on fresh data • Native AI/ML • Enterprise governance Multiple Systems, Multiple Silos Fragmented data • High complexity • Slow insights One Unified Platform Single source of truth • Simple • AI-ready 传统架构 vs LongDB 湖仓一体 传统架构(数据湖 + 数据仓库) 数据湖 原始数据存储 读时模式 无 ACID ✗ 治理不佳 ✗ 数据沼泽 ✓ 低成本 ETL 数据仓库 精选数据 写时模式 完整 ACID ✓ 快速查询 ✓ 治理 ✗ 高成本 ⚠ 问题 • 数据重复与不一致 • 复杂的 ETL 维护 • 高 TCO • 洞察缓慢 • 有限的 ML/AI 支持 LONGDB 湖仓一体 统一湖仓存储 ACID • 时间旅行 • Schema 演进 • 所有数据类型 元数据与治理 SQL + SPARK ML 运行时 SUPRABRAIN AI 引擎 ✓ 优势 • 单一数据源 • 降低 60% TCO • 实时分析新鲜数据 • 原生 AI/ML • 企业级治理 多系统,多孤岛 数据碎片化 • 高复杂性 • 洞察缓慢 一个统一平台 单一数据源 • 简单 • AI 就绪

Flexible Hybrid Cloud Architecture灵活的混合云架构

On-Premises本地部署

Full data sovereignty and compliance with air-gap requirements. Leverage existing infrastructure.

完全数据主权,符合物理隔离要求。充分利用现有基础设施。

  • Complete data control
  • Air-gap compatible
  • Custom hardware optimization
  • 完全数据控制
  • 物理隔离兼容
  • 自定义硬件优化

Private Cloud私有云

Run on VMware, OpenStack, or Kubernetes with cloud-like elasticity while maintaining isolation.

在 VMware、OpenStack 或 Kubernetes 上运行,享受类似云的弹性,同时保持隔离。

  • Elastic scaling
  • Network isolation
  • Existing infra integration
  • 弹性扩展
  • 网络隔离
  • 现有基础设施集成

Public Cloud公有云

Deploy on AWS, Azure, GCP, Alibaba Cloud, Huawei Cloud, or Tencent Cloud. Take advantage of managed services and global scale.

部署在 AWS、Azure、GCP、阿里云、华为云或腾讯云上。利用托管服务和全球规模。

  • Managed infrastructure
  • Global availability
  • Pay-as-you-go pricing
  • 托管基础设施
  • 全球可用性
  • 按需付费

Purpose-Built for High-End Manufacturing专为高端制造业打造

LongDB is designed with deep domain expertise for semiconductor, display, and advanced manufacturing industries where data complexity meets mission-critical requirements.

LongDB 凭借在半导体、显示器和先进制造业的深厚领域专业知识,专为数据复杂性与关键任务需求并存的行业设计。

🔬 Semiconductor Manufacturing半导体制造

The semiconductor industry generates massive volumes of data across fabrication, testing, and packaging. LongDB provides the performance and scale needed for advanced analytics and AI-driven quality control.

半导体行业在制造、测试和封装过程中产生海量数据。LongDB 提供先进分析和 AI 驱动质量控制所需的性能和规模。

Wafer Defect Classification晶圆缺陷分类

AI-powered classification of wafer defects (Center, Donut, Edge-Loc, Edge-Ring, Loc, Random) with real-time detection.

AI 驱动的晶圆缺陷分类(中心、甜甜圈、边缘局部、边缘环、局部、随机),支持实时检测。

Micro-LED Mass TransferMicro-LED 巨量转移

Handle millions of position and optical performance data points per substrate for defect analysis and yield optimization.

处理每个基板数百万个位置和光学性能数据点,用于缺陷分析和良率优化。

Panel Path Analysis面板路径分析

Track product flow through equipment with historical analysis. Identify equipment combinations that impact yield rates.

通过历史分析追踪产品在设备间的流转。识别影响良率的设备组合。

History Analysis履历分析

Calculate IV (Information Value) for equipment contribution to defect rates. Identify suspicious equipment for maintenance.

计算设备对缺陷率贡献的 IV(信息值)。识别需要维护的可疑设备。

Wafer Defect Types Center Donut Edge-Ring Edge-Loc Loc Random 晶圆缺陷类型 中心 甜甜圈 边缘环 边缘局部 局部 随机

🏭 Digital Manufacturing Transformation数字化制造转型

Consolidate data from ERP, MES, PLM, and shop floor systems into a single platform. Enable real-time visibility and data-driven decision making.

将 ERP、MES、PLM 和车间系统的数据整合到单一平台。实现实时可见性和数据驱动决策。

  • Unified data layer for all manufacturing systems
  • Real-time production monitoring dashboards
  • Predictive maintenance and quality analytics
  • Supply chain optimization
  • 所有制造系统的统一数据层
  • 实时生产监控仪表盘
  • 预测性维护和质量分析
  • 供应链优化

🔄 Legacy Data Warehouse Replacement传统数据仓库替换

Migrate from expensive legacy data warehouses to LongDB with lower TCO and better performance for modern workloads.

从昂贵的传统数据仓库迁移到 LongDB,以更低的 TCO 和更好的性能支持现代工作负载。

  • 60%+ cost reduction vs legacy systems
  • Automated schema migration tools
  • SQL compatibility for smooth transition
  • Parallel operation during migration
  • 相比传统系统成本降低 60% 以上
  • 自动化 Schema 迁移工具
  • SQL 兼容性确保平滑过渡
  • 迁移期间并行运行

📡 IoT & Sensor AnalyticsIoT 与传感器分析

Process millions of sensor readings per second from manufacturing equipment, environmental monitors, and connected devices.

每秒处理来自制造设备、环境监测器和联网设备的数百万传感器读数。

  • Sub-second streaming ingestion
  • Time-series optimized storage
  • Real-time anomaly detection
  • Edge-to-cloud data pipeline
  • 亚秒级流式摄入
  • 时序优化存储
  • 实时异常检测
  • 边缘到云数据管道

🏥 Precision Healthcare精准医疗

Integrate clinical data, genomics, imaging, and real-world evidence for precision medicine research and clinical decision support.

整合临床数据、基因组学、影像和真实世界证据,用于精准医学研究和临床决策支持。

  • Multi-modal data integration
  • HIPAA-compliant security
  • ML-ready feature engineering
  • Federated learning support
  • 多模态数据集成
  • HIPAA 合规安全
  • ML 就绪特征工程
  • 联邦学习支持

Proven Results实证效果

SEMICONDUCTOR FAB半导体晶圆厂

8x

Faster Root Cause Analysis根因分析速度提升

Unified data access enables engineers to investigate defect patterns in minutes instead of hours, accelerating time-to-resolution.

统一数据访问使工程师能够在分钟内而非数小时内调查缺陷模式,加速问题解决。

DISPLAY MANUFACTURER显示器制造商

85%

Reduction in Data Pipeline Complexity数据管道复杂度降低

Eliminated ETL jobs between operational and analytical systems, reducing maintenance burden and data latency.

消除运营系统与分析系统之间的 ETL 作业,降低维护负担和数据延迟。

PRECISION MANUFACTURING精密制造

60%

Lower Infrastructure Costs基础设施成本降低

Consolidated multiple data systems into a single platform, reducing licensing, hardware, and operational overhead.

将多个数据系统整合到单一平台,减少许可、硬件和运维开销。

Platform Comparison by Category按类别平台对比

Understanding how LongDB fits in the data platform landscape. Rather than competing head-to-head with every platform, LongDB occupies a unique position as a unified Lakehouse designed for manufacturing.

了解 LongDB 在数据平台版图中的位置。LongDB 并非与每个平台正面竞争,而是占据独特定位——专为制造业设计的统一湖仓一体平台。

📊 MPP Analytical DatabasesMPP 分析型数据库

High-performance analytical query engines designed for data warehousing and business intelligence workloads.

为数据仓库和商业智能工作负载设计的高性能分析查询引擎。

Platform平台 Strengths优势 Limitations局限性 vs LongDB对比 LongDB
Teradata Mature, proven at scale, strong in retail/finance High cost, proprietary, limited cloud flexibility LongDB: 60% lower TCO, open standards, hybrid cloud
Greenplum Open source, PostgreSQL compatible, good ML support Complex operations, limited streaming, aging architecture LongDB: Modern lakehouse, better streaming, easier ops
Vertica Fast analytics, good compression, mature product Expensive licensing, proprietary format, limited AI LongDB: Open architecture, Virtual Table extensibility, native AI integration
StarRocks/Doris Fast OLAP, real-time analytics, good for dashboards Less mature, limited ML, separate storage layer LongDB: Unified storage+compute, stronger ML/AI
Teradata 成熟,大规模验证,零售/金融领域强势 成本高,专有,云灵活性有限 LongDB:TCO 降低 60%,开放标准,混合云
Greenplum 开源,PostgreSQL 兼容,ML 支持良好 运维复杂,流处理有限,架构老化 LongDB:现代湖仓,更好流处理,运维简单
Vertica 分析快速,压缩好,产品成熟 许可昂贵,专有格式,AI 有限 LongDB:开放架构,虚拟表可扩展性,原生 AI 集成
StarRocks/Doris OLAP 快速,实时分析,适合仪表盘 成熟度较低,ML 有限,存储层分离 LongDB:统一存储+计算,更强 ML/AI

☁️ Cloud Data Platforms云数据平台

Cloud-native data platforms offered by major cloud providers and independent vendors.

主要云提供商和独立供应商提供的云原生数据平台。

Platform平台 Strengths优势 Limitations局限性 vs LongDB对比 LongDB
Snowflake Excellent UX, auto-scaling, strong ecosystem Cloud-only, high costs at scale, limited on-prem LongDB: Hybrid cloud, on-prem support, lower TCO
Databricks Best-in-class ML, Delta Lake, strong Spark Complex pricing, requires cloud, steep learning curve LongDB: Simpler pricing, true hybrid, easier adoption
AWS Redshift Deep AWS integration, serverless option AWS lock-in, complex tuning, limited ML LongDB: Multi-cloud, simpler ops, native ML
BigQuery Serverless, excellent for ad-hoc, GCP integrated GCP lock-in, unpredictable costs, no on-prem LongDB: Predictable pricing, hybrid deployment
Azure Synapse Microsoft ecosystem, Power BI integration Azure lock-in, complex architecture, multiple engines LongDB: Unified engine, multi-cloud, simpler
Snowflake 优秀 UX,自动扩展,生态强大 仅限云端,大规模成本高,无本地部署 LongDB:混合云,本地部署支持,更低 TCO
Databricks 顶级 ML,Delta Lake,Spark 强势 定价复杂,需要云,学习曲线陡峭 LongDB:定价简单,真正混合云,易于采用
AWS Redshift 深度 AWS 集成,Serverless 选项 AWS 锁定,调优复杂,ML 有限 LongDB:多云,运维简单,原生 ML
BigQuery Serverless,适合即席查询,GCP 集成 GCP 锁定,成本不可预测,无本地部署 LongDB:可预测定价,混合部署
Azure Synapse 微软生态,Power BI 集成 Azure 锁定,架构复杂,多引擎 LongDB:统一引擎,多云,更简单

💾 Transactional Databases (OLTP)事务型数据库 (OLTP)

Traditional relational databases designed for transactional workloads. Note: LongDB complements these systems rather than replacing them.

为事务性工作负载设计的传统关系型数据库。注意:LongDB 是这些系统的补充,而非替代。

Platform平台 Best For最适用场景 Analytics Limitations分析局限性 LongDB Relationship与 LongDB 的关系
Oracle Enterprise OLTP, ERP backends Expensive for analytics scale, row-based storage CDC replication to LongDB for analytics
PostgreSQL General purpose, open source Limited horizontal scale, not for PB-scale CDC replication to LongDB for analytics
MySQL Web applications, simple OLTP Not designed for complex analytics CDC replication to LongDB for analytics
SQL Server Microsoft stack, enterprise apps Scaling costs high, Windows-centric CDC replication to LongDB for analytics
Oracle 企业 OLTP,ERP 后端 分析规模成本高,行式存储 CDC 复制到 LongDB 做分析
PostgreSQL 通用,开源 水平扩展有限,不适合 PB 级 CDC 复制到 LongDB 做分析
MySQL Web 应用,简单 OLTP 非为复杂分析设计 CDC 复制到 LongDB 做分析
SQL Server 微软技术栈,企业应用 扩展成本高,以 Windows 为中心 CDC 复制到 LongDB 做分析

🔗 Distributed NoSQL & NewSQL分布式 NoSQL 与 NewSQL

Distributed databases designed for high-scale transactional or specialized workloads.

为大规模事务或专业工作负载设计的分布式数据库。

Platform平台 Best For最适用场景 Analytics Limitations分析局限性 vs LongDB对比 LongDB
CockroachDB Distributed ACID, global transactions Not optimized for analytical queries Different use case; LongDB for analytics layer
TiDB HTAP (hybrid transactional/analytical) Trade-offs in both OLTP and OLAP performance LongDB: Focused analytics excellence
Cassandra High write throughput, time-series Limited SQL, no complex joins Can feed data to LongDB for analytics
MongoDB Document storage, flexible schema Not designed for complex analytics Semi-structured data → LongDB for analytics
CockroachDB 分布式 ACID,全球事务 未针对分析查询优化 不同用例;LongDB 用于分析层
TiDB HTAP(混合事务/分析) OLTP 和 OLAP 性能都有折衷 LongDB:专注分析卓越性
Cassandra 高写入吞吐量,时序 SQL 有限,无复杂连接 可将数据输入 LongDB 做分析
MongoDB 文档存储,灵活 Schema 非为复杂分析设计 半结构化数据 → LongDB 做分析

Why Choose LongDB为什么选择 LongDB

🎯 End-to-End Platform端到端平台

Unlike competitors that require cobbling together multiple products, LongDB provides a complete, integrated solution from ingestion to insight.

不同于需要拼凑多个产品的竞争对手,LongDB 提供从数据摄入到洞察的完整集成解决方案。

  • No need for separate ETL tools
  • Built-in BI and visualization
  • Integrated Data Science Studio & ML
  • Single vendor, single contract
  • 无需单独的 ETL 工具
  • 内置 BI 和可视化
  • 集成数据科学工作室与 ML
  • 单一供应商,单一合同

☁️ True Hybrid Cloud真正的混合云

Deploy anywhere—on-premises, private cloud, or public cloud—with the same platform, APIs, and user experience.

随处部署——本地、私有云或公有云——相同的平台、API 和用户体验。

  • Air-gap deployment for security
  • Data sovereignty compliance
  • Burst to cloud for peak loads
  • Consistent ops across environments
  • 物理隔离部署保障安全
  • 数据主权合规
  • 峰值负载弹性扩展到云
  • 跨环境一致运维

🏭 Manufacturing DNA制造业基因

Built by a team with deep expertise in semiconductor and high-tech manufacturing. Pre-built solutions for common industry challenges.

由在半导体和高科技制造业拥有深厚专业知识的团队打造。针对常见行业挑战的预置解决方案。

  • Semiconductor analytics templates
  • MES/ERP connectors (SAP, Oracle)
  • Quality and yield models
  • Industry-specific training & support
  • 半导体分析模板
  • MES/ERP 连接器(SAP、Oracle)
  • 质量和良率模型
  • 行业特定培训与支持

💰 Superior TCO卓越的 TCO

Achieve 60%+ cost savings compared to legacy data warehouses and competitive cloud platforms.

相比传统数据仓库和竞争云平台,实现 60% 以上的成本节省。

  • Competitive licensing
  • Efficient resource utilization
  • Reduced integration costs
  • Lower operational overhead
  • 有竞争力的许可定价
  • 高效资源利用
  • 降低集成成本
  • 更低运维开销
🔍 Honest Assessment: LongDB is well-suited for analytical workloads and AI/ML in manufacturing contexts. However, it should be understood as a lakehouse platform, not a universal database. For high-frequency transactional workloads (e.g., MES real-time writes at thousands of TPS with sub-10ms latency), dedicated OLTP databases remain the appropriate choice. LongDB complements these systems rather than replacing them.
🔍 诚实评估:LongDB 非常适合制造业场景中的分析工作负载和 AI/ML。但应将其理解为湖仓一体平台,而非通用数据库。对于高频事务性工作负载(如 MES 实时写入数千 TPS 且延迟低于 10ms),专用 OLTP 数据库仍是合适选择。LongDB 是这些系统的补充,而非替代。
Company Vision 公司愿景

Becoming the Global Leader in Manufacturing Data Intelligence成为全球制造业数据智能领导者

LongDB Technology envisions a future where every manufacturing enterprise can harness the full potential of their data through intelligent, unified, and accessible platforms. We're building the foundation for the AI-powered factory of the future.

龙迪数智科技致力于构建一个未来,让每家制造企业都能通过智能、统一、易用的平台充分释放数据潜力。我们正在为 AI 驱动的未来工厂奠定基础。

Our Vision & Mission我们的愿景与使命

🎯 Vision愿景

To become the world's leading data intelligence platform for manufacturing, empowering enterprises across Asia and globally to transform data into competitive advantage through unified lakehouse architecture and AI-native capabilities.

成为全球领先的制造业数据智能平台,通过统一湖仓架构和 AI 原生能力,赋能亚洲及全球企业将数据转化为竞争优势。

🚀 Mission使命

To deliver an end-to-end data platform that eliminates complexity, reduces costs, and accelerates time-to-insight for manufacturing and high-tech enterprises—making enterprise-grade data intelligence accessible to all.

提供端到端数据平台,消除复杂性、降低成本、加速制造业和高科技企业的洞察时间——让企业级数据智能触手可及。

What's Coming Next即将推出

Q1 2026

SupraBrain 2.0SupraBrain 2.0

Enhanced agentic capabilities with multi-agent orchestration, agent workspace for collaborative workflows, multi-step reasoning, and improved NL-to-SQL accuracy. Integration with GPT, Claude, Gemini, Qwen, and DeepSeek.

增强的智能体能力,支持多智能体协同、智能体工作空间、多步推理和改进的自然语言转 SQL 准确性。与 GPT、Claude、Gemini、Qwen 和 DeepSeek 集成。

Q2 2026

Fusion Vector Search & Enhanced RAG融合向量搜索与增强 RAG

Fusion vector database integration combining dense and sparse retrieval for superior semantic search. Enhanced RAG with hybrid search, re-ranking, and multi-modal document understanding for enterprise AI applications.

融合向量数据库集成,结合稠密和稀疏检索实现卓越的语义搜索。增强的 RAG 支持混合搜索、重排序和多模态文档理解,赋能企业级 AI 应用。

Q3 2026

Multi-Cloud Federation多云联邦

Query data across multiple cloud providers and on-premises deployments with a single SQL interface. True hybrid cloud with unified governance.

通过单一 SQL 接口查询多个云提供商和本地部署的数据。真正的混合云统一治理。

Q4 2026

Industry Solutions行业解决方案

Pre-packaged solutions for semiconductor, automotive, and high-end manufacturing with domain-specific data models, dashboards, and ML models.

为半导体、汽车和高端制造业预置的解决方案,包含领域特定数据模型、仪表盘和 ML 模型。

Go-to-Market Approach市场推广策略

🌏 Asia-First Strategy亚洲优先战略

Deep focus on Greater China, Taiwan, Korea, and Southeast Asia where manufacturing drives economic growth. Local teams, local language support, and partnerships with regional system integrators.

深耕大中华区、台湾、韩国和东南亚等制造业驱动经济增长的地区。本地团队、本地语言支持以及与区域系统集成商的合作。

🏭 Vertical Focus垂直领域专注

Concentrated expertise in semiconductor, display, and high-tech manufacturing. Build reference architectures and proof points that resonate with industry-specific challenges.

专注于半导体、显示器和高科技制造业。构建与行业特定挑战产生共鸣的参考架构和验证案例。

🤝 Partner Ecosystem合作伙伴生态

Strategic partnerships with cloud providers (Alibaba, Tencent, AWS), system integrators, and technology vendors to extend reach and deliver comprehensive solutions.

与云提供商(阿里巴巴、腾讯、AWS)、系统集成商和技术供应商的战略合作,扩大覆盖范围并提供全面解决方案。

LongDB Technology龙迪数智科技

LongDB Technology (龙迪数智科技) is a data+AI platform company founded by industry veterans with deep experience in enterprise data management, AI/ML, and semiconductor manufacturing.

Our team combines expertise from leading global technology companies with intimate knowledge of Asian market requirements, regulatory environments, and business practices.

We believe that the future of enterprise data is unified, intelligent, and accessible. LongDB is building that future—one customer at a time.

龙迪数智科技(LongDB Technology)是一家数据+AI平台公司,由在企业数据管理、AI/ML 和半导体制造领域拥有丰富经验的行业资深人士创立。

我们的团队融合了来自全球领先科技公司的专业知识,同时深谙亚洲市场需求、法规环境和商业惯例。

我们坚信企业数据的未来是统一的、智能的、易于访问的。LongDB 正在构建这个未来——每次服务一位客户。

Sales Inquiries销售咨询

[email protected]

Locations办公地点

Beijing · Hefei · San Francisco北京 · 合肥 · 旧金山