AI-Ready Enterprise Lakehouse Platform AI就緒的企業級湖倉一體平台

The Future of Manufacturing Data Intelligence 製造業資料智慧的未來

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 是一個 AI 就緒的湖倉一體平台,融合資料湖與資料倉儲的優勢,助力企業充分釋放資料價值,實現高效分析、AI/ML 應用與即時決策——專為半導體和高階製造業打造。

10x10倍
Faster Analytics分析性能提升
60%
Lower TCO總擁有成本降低
8+
Enterprise Clients企業級客戶
1B+10億+
Records/Day日處理記錄
7×24
Production Stability穩定生產運行
PB+
Data Managed資料管理規模

The Data Silo Problem in Manufacturing製造業的資料孤島問題

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.

現代製造企業隨著時間推移累積了多個分析平台。每個系統可能適合其特定用例,但整體結果是資料碎片化,阻礙了 AI/ML 的落地應用。

Fragmented Analytics分析系統碎片化

"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,加個 GP/Vertica 做分析"——每個應用都維護自己的資料副本,同一 KPI 在不同系統中顯示不同值。

High TCO & Complexity高成本與複雜性

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

多個授權證,多份支援合同。在碎片化架構中,30-40% 的 IT 資源消耗在資料同步和整合管線上。

Slow Time-to-Insight洞察時間過長

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

儀表板資料可能是數小時或數天前的。資料科學家花费 60-80% 的時間在資料準備上,而不是建構模型。

AI/ML Adoption BarriersAI/ML 落地困難

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

AI 專案被分散、不一致的資料阻礙。特徵工程需要存取多個斷開連結的系統。

📋 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:製造業資料平台的策略價值不在于"查詢速度"——而在于3-5年的平台演進無需重構。單點解決方案可能解決眼前問題,但往往造成日積月累的資料孤島。

LongDB: Unified Lakehouse PlatformLongDB:統一的湖倉一體平台

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

一個融合資料倉儲和資料湖優勢的統一平台,專為製造業現代 AI 工作負載量身打造。

Lakehouse Architecture湖倉一體架構

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.

統一儲存結構化、半結構化和非結構化資料,具備完整的 ACID 事务和 Schema 演進能力。虛擬表機制支援靈活可擴展的儲存選項。

AI-Native DesignAI 原生設計

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

內建 ML 執行時、特徵儲存、模型註冊表和 SupraBrain AI 引擎,實現 AI/ML 工作負載與資料的無縫整合。

Hybrid Cloud Ready混合雲就緒

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

支援地端部署、私有雲或公有雲。完全資料主权,無縫遷移,跨環境一致的 API。

Real-time + Batch Unified即時+批處理統一

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

單一 SQL 接口支援 BI、報表、流式分析和即席查詢。無需在系統間移動資料。

Two Powerful Products, One Vision兩大產品,一個願景

LongDB PlatformLongDB 平台

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

核心湖倉一體平台,為企業級製造業工作負載提供統一的資料儲存、處理和分析能力。

  • 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
  • 統一湖倉,支援 ACID 事务
  • 時間旅行與 Schema 演進能力
  • 即時流式攝入(Kafka、CDC)
  • 虛擬表機制支援可擴展儲存選項
  • 資料科學工作室(Notebook 與 ML)
  • 企業級安全、治理與血緣追踪

SupraBrain AI EngineSupraBrain AI 引擎

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

新一代智慧代理 AI 引擎,為資料操作帶來智慧自動化和自然語言交互能力。

  • 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)
  • 自然語言轉 SQL 生成
  • 具備推理能力的自主資料智慧代理
  • 智慧查詢最佳化
  • 自動洞察發現與告警
  • 多模态資料分析(影像、文件)
  • 企業級模型整合(GPT、Claude、Gemini、Qwen、DeepSeek)

🏭 Production Deployments生產環境部署案例

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

LongDB 已在半導體和高階製造企業中投入生產使用:

🔬 Semiconductor Fab (300mm) ⚙️ Precision Gear Manufacturer 🏠 IKEA Global Supplier 🚗 Global Automotive Glass Manufacturer
🔬 半導體晶圓厂(300mm) ⚙️ 精密齒輪製造商 🏠 宜家全球供應商 🚗 全球汽車玻璃製造商

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

客戶名稱可在簽署保密協議后提供。評估期間可安排参考客戶溝通。

Unified Data + AI Platform for Manufacturing面向製造業的統一資料+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 平台是企業級湖倉一體系統,將資料管理、分析和 AI 能力統一在一個為半導體和高階製造業設計的一致性平台中。

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 應用情境 資料整合 即時分析 自助 BI ML/AI 訓練 預測性品質 製程最佳化 資料來源 🏭 MES/MOM 📊 ERP/SAP 🔧 設備資料 📈 測試資料 🖼️ 視覺/AOI 📁 檔案/日誌 ⏱️ 時序資料 批量 CDC 資料攝取 Kafka Connect CDC Spark Streaming 批次載入器 檔案載入器 批次 + 串流 統一管線 LongDB 資料 + AI 平台 運算層 SQL 引擎 Spark 執行時 ML 執行時 查詢最佳化器 平台服務 資料目錄 資料治理 安全 血緣 品質 排程 SupraBrain AI 特徵儲存 模型註冊中心 湖倉儲存 内表/虛擬表 塊儲存/對象儲存 時間旅行 敏捷 BI 資料科學工作室 資料管線 監控 API 資料消費者 📊 Power BI / Spotfire 📈 帆软 / SmartBI 🔬 資料科學工具 🤖 ML 模型 📱 客製化應用 🔗 REST / GraphQL API JDBC / ODBC / REST 現有 OLTP 系統(保留) Oracle PostgreSQL SQL Server MES 資料庫 CDC 複製到 LongDB 進行分析 基礎設施層 — 任意部署 Kubernetes 地端部署 公有雲 私有雲 MinIO / S3 混合多雲 LongDB 平台 SupraBrain AI / 消費者 資料來源 現有 OLTP(保留) 資料攝取層
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.
架構說明:這是一种"補充與整合"方法,而非"推倒重來"。現有 OLTP 系統繼續處理事务性工作負載,而 LongDB 整合分析層。CDC 提供近即時複製,最大限度減少 ETL 延遲。

Enterprise-Grade Features企業級功能

Unified Lakehouse Storage統一湖倉儲存

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

原生儲存引擎,支援 ACID 事务、時間旅行和 Schema 演進。虛擬表機制支援靈活可擴展的儲存選項。

Real-time Streaming即時流處理

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

Kafka Connect、CDC、Spark Streaming 和批次載入器,每秒攝入數百萬事件,支援精确一次語意。

Data Governance資料治理

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

內建資料目錄、血緣追踪、存取控制(RBAC)、資料品質監控和稽核日誌。

Agile BI敏捷 BI

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

交互式儀表板、拖拽式視覺化建構器,為業務用户提供自助分析能力。

Data Science Studio資料科學工作室

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

兼容 Jupyter 的筆記本,支援 Python、R、Scala 和 SQL。與特徵儲存和模型註冊表整合。

ML IntegrationML 整合

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

原生 ML 執行時、特徵儲存、模型註冊表和 MLflow 整合。直接在平台資料上訓練和部署模型。

Agentic AI Engine 智慧代理 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.

企業級智慧代理 AI 引擎,基于四大核心支柱:對話式界面、RAG 驅動的知識庫、上下文感知的資料智慧代理和可擴展的外掛架構。SupraBrain 重新定義企業與資料的交互方式,釋放資料價值。

Four Pillars of Intelligent Data Interaction智慧資料交互的四大支柱

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

SupraBrain 將對話式 AI、檢索增强生成、領域感知資料智慧代理和靈活的外掛生態系統融合為統一的智慧代理引擎。

1. Conversational Interface1. 對話式界面

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,支援中英文等多語言自然語言查詢。無需編寫代码即可提問、獲取答案、視覺化和可操作洞察。保持對話上下文,支援後續問題和迭代探索。

Multi-turn Conversations多輪對話 Multi-lingual多語言 Voice Input語音輸入

2. RAG Knowledge Base2. RAG 知識庫

Retrieval-Augmented Generation檢索增强生成

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 技术驅動的企業知識庫。攝入文件、手册、SOP 和領域知識,提供準確、上下文感知的回應。向量嵌入支援非結構化内容的語意搜尋和即時檢索。

Vector Store向量儲存 Semantic Search語意搜尋 Document Ingestion文件攝入

3. Data Agent3. 資料智慧代理

Context & Domain Model Awareness上下文與領域模型感知

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.

智慧資料代理深度理解資料上下文、Schema 關係和領域特定資料模型。自動生成準確的 SQL,執行多步推理,在完整資料血緣感知下執行複雜分析工作流。

Schema UnderstandingSchema 理解 NL-to-SQL自然語言轉 SQL Domain Models領域模型

4. Plugin/MCP Architecture4. 外掛/MCP 架構

Extensibility & Integration可擴展性與整合

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.

基于模型上下文協議(MCP)的開放外掛架構,實現無縫擴展。連結外部工具、API 和企業系統。建構自訂能力,同時在所有整合中保持安全和治理控制。

MCP ProtocolMCP 協議 Custom Plugins自訂外掛 API ConnectorsAPI 連結器
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 智慧代理引擎架構 使用者介面 💬 聊天界面 🎤 語音輸入 🔌 API SUPRABRAIN 核心引擎 1. 對話式界面 多輪對話 • 上下文感知 • 多語言 2. RAG 知識庫 向量儲存 • 語意搜尋 • 文件 3. 資料智慧代理 Schema 理解 • 領域模型 • NL-to-SQL 4. 外掛/MCP 架構 可擴展性 • 自訂工具 • API 模型供應商 GPT Claude Gemini Qwen DeepSeek 地端 LLM/LVM/VLM/ML 🔒 行級安全 • 資料去識別化 • 稽核日誌 • RBAC • 合規 資料層 📊 湖倉 📁 資料目錄 🔗 血緣 📈 指標 🏷️ 元資料 📋 模型 🔍 搜尋 ⚙️ API 1. 對話式 2. RAG 知識庫 3. 資料智慧代理 4. 外掛/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全層智慧

Intelligent Query Optimization智慧查詢最佳化

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

AI 驅動的查詢分析,建議索引,重寫低效查詢,自動調優性能。

Automated Insight Discovery自動洞察發現

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

主動發現資料中的異常、趨勢和模式。在問題發生前获得重要變化的警報。

Multi-modal Analysis多模态分析

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

結合結構化資料分析影像、文件和感測器資料。非常適合製造業品質控制。

SupraBrain in ActionSupraBrain 實戰案例

🎯 Self-Service Analytics自助分析

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

"顯示上個月按設備劃分的良率,與目標比較,並醒目標示顯示低于 95% 的設備"

→ Generates SQL, creates visualization, adds commentary→ 生成 SQL,建立視覺化,添加註解

🔍 Root Cause Analysis根因分析

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

"為什麼上周二下午 3 號生產線的良率下降了?"

→ Correlates sensor data, identifies anomalies, suggests causes→ 關聯感測器資料,辨識異常,建議原因

📊 Report Automation報表自動化

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

"生成關鍵製造指標的每周高管摘要,每周一通過郵件發送"

→ Creates automated pipeline, schedules delivery→ 建立自動化管線,排程交付

🤖 Predictive Maintenance預測性維護

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

"當任何設備在未來 48 小時内顯示潜在故障跡象時提醒我"

→ Deploys ML model, sets up real-time monitoring→ 部署 ML 模型,設定即時監控

🧠 LLM IntegrationLLM 整合

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 支援多种模型供應商,包括 GPT、Claude、Gemini、Qwen 和 DeepSeek。企業可以使用雲代管模型或地端部署 LLM/LVM/VLM/ML 模型以確保資料隱私。支援针對領域特定術語的自訂微調。

Lakehouse Architecture Explained湖倉架構詳解

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

LongDB 將資料倉儲的優勢(可靠性、治理、性能)與資料湖的優勢(靈活性、規模、成本)融合在單一統一架構中。

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 傳統架構 vs LongDB 湖倉一體 傳統架構(資料湖 + 資料倉儲) 資料湖 原始資料儲存 讀時模式 無 ACID ✗ 治理不佳 ✗ 資料沼泽 ✓ 低成本 ETL 資料倉儲 精選資料 寫時模式 完整 ACID ✓ 快速查詢 ✓ 治理 ✗ 高成本 ⚠ 問題 • 資料重複與不一致 • 複雜的 ETL 維護 • 高 TCO • 洞察緩慢 • 有限的 ML/AI 支援 LONGDB 湖倉一體 統一湖倉儲存 ACID • 時間旅行 • Schema 演進 • 所有資料類型 元資料與治理 SQL + SPARK ML 執行時 SUPRABRAIN AI 引擎 ✓ 優勢 • 單一資料來源 • 降低 60% TCO • 即時分析新鮮資料 • 原生 AI/ML • 企業級治理 多系統,多孤島 資料碎片化 • 高複雜性 • 洞察緩慢 一個統一平台 單一資料來源 • 簡單 • AI 就緒

Flexible Hybrid Cloud Architecture靈活的混合雲架構

On-Premises地端部署

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

完全資料主权,符合物理隔離要求。充分利用現有基礎設施。

  • Complete data control
  • Air-gap compatible
  • Custom hardware optimization
  • 完全資料控制
  • 物理隔離兼容
  • 自訂硬件最佳化

Private Cloud私有雲

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

在 VMware、OpenStack 或 Kubernetes 上執行,享受類似雲的彈性,同時保持隔離。

  • Elastic scaling
  • Network isolation
  • Existing infra integration
  • 彈性擴展
  • 網路隔離
  • 現有基礎設施整合

Public Cloud公有雲

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、阿里雲、華為雲或騰訊雲上。利用代管服務和全球規模。

  • Managed infrastructure
  • Global availability
  • Pay-as-you-go pricing
  • 代管基礎設施
  • 全球可用性
  • 按需付费

Purpose-Built for High-End Manufacturing專為高階製造業打造

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

LongDB 凭借在半導體、顯示器和先進製造業的深厚領域專業知識,專為資料複雜性與關鍵任务需求並存的業界設計。

🔬 Semiconductor Manufacturing半導體製造

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.

半導體業界在製造、測試和封装過程中產生海量資料。LongDB 提供先進分析和 AI 驅動品質控制所需的性能和規模。

Wafer Defect Classification晶圓缺陷分類

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

AI 驅動的晶圓缺陷分類(中心、甜甜圈、邊緣局部、邊緣環、局部、隨機),支援即時偵測。

Micro-LED Mass TransferMicro-LED 巨量轉移

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

處理每個基板數百萬個位置和光學性能資料點,用于缺陷分析和良率最佳化。

Panel Path Analysis面板路径分析

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

通過歷史分析追踪產品在設備間的流轉。辨識影響良率的設備組合。

History Analysis履歷分析

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

計算設備對缺陷率貢獻的 IV(信息值)。辨識需要維護的可疑設備。

Wafer Defect Types Center Donut Edge-Ring Edge-Loc Loc Random 晶圓缺陷類型 中心 甜甜圈 邊緣環 邊緣局部 局部 隨機

🏭 Digital Manufacturing Transformation數位化製造轉型

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

將 ERP、MES、PLM 和车間系統的資料整合到單一平台。實現即時可見性和資料驅動決策。

  • Unified data layer for all manufacturing systems
  • Real-time production monitoring dashboards
  • Predictive maintenance and quality analytics
  • Supply chain optimization
  • 所有製造系統的統一資料層
  • 即時生產監控儀表板
  • 預測性維護和品質分析
  • 供應鏈最佳化

🔄 Legacy Data Warehouse Replacement傳統資料倉儲替換

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

从昂貴的傳統資料倉儲遷移到 LongDB,以更低的 TCO 和更好的性能支援現代工作負載。

  • 60%+ cost reduction vs legacy systems
  • Automated schema migration tools
  • SQL compatibility for smooth transition
  • Parallel operation during migration
  • 相比傳統系統成本降低 60% 以上
  • 自動化 Schema 遷移工具
  • SQL 兼容性確保平滑過渡
  • 遷移期間並行執行

📡 IoT & Sensor AnalyticsIoT 與感測器分析

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

每秒處理來自製造設備、環境監測器和聯網設備的數百萬感測器讀數。

  • Sub-second streaming ingestion
  • Time-series optimized storage
  • Real-time anomaly detection
  • Edge-to-cloud data pipeline
  • 亚秒級流式攝入
  • 時序最佳化儲存
  • 即時異常偵測
  • 邊緣到雲資料管線

🏥 Precision Healthcare精准醫療

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

整合臨床資料、基因组學、影像和真實世界證據,用于精准医學研究和臨床決策支援。

  • Multi-modal data integration
  • HIPAA-compliant security
  • ML-ready feature engineering
  • Federated learning support
  • 多模态資料整合
  • HIPAA 合規安全
  • ML 就緒特徵工程
  • 聯邦學習支援

Proven Results實證效果

SEMICONDUCTOR FAB半導體晶圓厂

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.

統一資料存取使工程师能够在分钟内而非數小時内調查缺陷模式,加速問題解決。

DISPLAY MANUFACTURER顯示器製造商

85%

Reduction in Data Pipeline Complexity資料管線複雜度降低

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

消除營運系統與分析系統之間的 ETL 作業,降低維護負擔和資料延遲。

PRECISION MANUFACTURING精密製造

60%

Lower Infrastructure Costs基礎設施成本降低

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

將多個資料系統整合到單一平台,減少授權、硬件和運維開銷。

Platform Comparison by Category按類別平台比較

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 在資料平台版圖中的位置。LongDB 並非與每個平台正面競爭,而是佔據獨特定位——專為製造業設計的統一湖倉一體平台。

📊 MPP Analytical DatabasesMPP 分析型資料庫

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

為資料倉儲和商業智慧工作負載設計的高性能分析查詢引擎。

Platform平台 Strengths優勢 Limitations局限性 vs LongDB比較 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,大規模驗證,零售/金融領域强势 成本高,專有,雲靈活性有限 LongDB:TCO 降低 60%,開放標準,混合雲
Greenplum 開源,PostgreSQL 兼容,ML 支援良好 運維複雜,流處理有限,架構老化 LongDB:現代湖倉,更好流處理,運維簡單
Vertica 分析快速,压缩好,產品成熟 授權昂貴,專有格式,AI 有限 LongDB:開放架構,虛擬表可擴展性,原生 AI 整合
StarRocks/Doris OLAP 快速,即時分析,適合儀表板 成熟度较低,ML 有限,儲存層分離 LongDB:統一儲存+計算,更强 ML/AI

☁️ Cloud Data Platforms雲資料平台

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

主要雲供應商和獨立供應商提供的雲原生資料平台。

Platform平台 Strengths優勢 Limitations局限性 vs LongDB比較 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 優秀 UX,自動擴展,生態强大 仅限雲端,大規模成本高,無地端部署 LongDB:混合雲,地端部署支援,更低 TCO
Databricks 顶級 ML,Delta Lake,Spark 强势 定价複雜,需要雲,學習曲線陡峭 LongDB:定价簡單,真正混合雲,易于採用
AWS Redshift 深度 AWS 整合,Serverless 選項 AWS 鎖定,調優複雜,ML 有限 LongDB:多雲,運維簡單,原生 ML
BigQuery Serverless,適合即席查詢,GCP 整合 GCP 鎖定,成本不可預測,無地端部署 LongDB:可預測定价,混合部署
Azure Synapse 微软生態,Power BI 整合 Azure 鎖定,架構複雜,多引擎 LongDB:統一引擎,多雲,更簡單

💾 Transactional Databases (OLTP)事务型資料庫 (OLTP)

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

為事务性工作負載設計的傳統關係型資料庫。注意:LongDB 是這些系統的補充,而非替代。

Platform平台 Best For最適用情境 Analytics Limitations分析局限性 LongDB Relationship與 LongDB 的關係
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 企業 OLTP,ERP 后端 分析規模成本高,行式儲存 CDC 複製到 LongDB 做分析
PostgreSQL 通用,開源 水平擴展有限,不適合 PB 級 CDC 複製到 LongDB 做分析
MySQL Web 應用,簡單 OLTP 非為複雜分析設計 CDC 複製到 LongDB 做分析
SQL Server 微软技术栈,企業應用 擴展成本高,以 Windows 為中心 CDC 複製到 LongDB 做分析

🔗 Distributed NoSQL & NewSQL分布式 NoSQL 與 NewSQL

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

為大規模事务或專業工作負載設計的分布式資料庫。

Platform平台 Best For最適用情境 Analytics Limitations分析局限性 vs LongDB比較 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 未针對分析查詢最佳化 不同用例;LongDB 用于分析層
TiDB HTAP(混合事务/分析) OLTP 和 OLAP 性能都有折衷 LongDB:專注分析卓越性
Cassandra 高寫入吞吐量,時序 SQL 有限,無複雜連結 可將資料輸入 LongDB 做分析
MongoDB 文件儲存,靈活 Schema 非為複雜分析設計 半結構化資料 → LongDB 做分析

Why Choose LongDB為什麼選擇 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.

不同于需要拼凑多個產品的競爭對手,LongDB 提供从資料攝取到洞察的完整整合解決方案。

  • No need for separate ETL tools
  • Built-in BI and visualization
  • Integrated Data Science Studio & ML
  • Single vendor, single contract
  • 無需單獨的 ETL 工具
  • 內建 BI 和視覺化
  • 整合資料科學工作室與 ML
  • 單一供應商,單一合同

☁️ True Hybrid Cloud真正的混合雲

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

任意部署——地端、私有雲或公有雲——相同的平台、API 和用户體驗。

  • Air-gap deployment for security
  • Data sovereignty compliance
  • Burst to cloud for peak loads
  • Consistent ops across environments
  • 物理隔離部署保障安全
  • 資料主权合規
  • 峰值負載彈性擴展到雲
  • 跨環境一致運維

🏭 Manufacturing DNA製造業基因

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

由在半導體和高科技製造業擁有深厚專業知識的團隊打造。针對常見業界挑戰的預置解決方案。

  • Semiconductor analytics templates
  • MES/ERP connectors (SAP, Oracle)
  • Quality and yield models
  • Industry-specific training & support
  • 半導體分析模板
  • MES/ERP 連結器(SAP、Oracle)
  • 品質和良率模型
  • 業界特定培训與支援

💰 Superior TCO卓越的 TCO

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

相比傳統資料倉儲和競爭雲平台,實現 60% 以上的成本節省。

  • Competitive licensing
  • Efficient resource utilization
  • Reduced integration costs
  • Lower operational overhead
  • 有競爭力的授權定价
  • 高效資源利用
  • 降低整合成本
  • 更低運維開銷
🔍 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.
🔍 誠實評估:LongDB 非常適合製造業情境中的分析工作負載和 AI/ML。但應將其理解為湖倉一體平台,而非通用資料庫。對于高頻事务性工作負載(如 MES 即時寫入數千 TPS 且延遲低于 10ms),專用 OLTP 資料庫仍是合適選擇。LongDB 是這些系統的補充,而非替代。
Company Vision 公司願景

Becoming the Global Leader in Manufacturing Data Intelligence成為全球製造業資料智慧領導者

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.

龍迪數智科技致力於建構一個未來,讓每家製造企業都能通過智慧、統一、易用的平台充分釋放資料潛力。我們正在為 AI 驅動的未來工廠奠定基礎。

Our Vision & Mission我們的願景與使命

🎯 Vision願景

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.

成為全球領先的製造業資料智慧平台,通過統一湖倉架構和 AI 原生能力,賦能亞洲及全球企業將資料轉化為競爭優勢。

🚀 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.

提供端到端資料平台,消除複雜性、降低成本、加速製造業和高科技企業的洞察時間——让企業級資料智慧觸手可及。

What's Coming Next即將推出

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.

增强的智慧代理能力,支援多智慧代理協同、智慧代理工作空間、多步推理和改進的自然語言轉 SQL 準確性。與 GPT、Claude、Gemini、Qwen 和 DeepSeek 整合。

Q2 2026

Fusion Vector Search & Enhanced RAG融合向量搜尋與增强 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.

融合向量資料庫整合,結合稠密和稀疏檢索實現卓越的語意搜尋。增强的 RAG 支援混合搜尋、重排序和多模态文件理解,賦能企業級 AI 應用。

Q3 2026

Multi-Cloud Federation多雲聯邦

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

通過單一 SQL 接口查詢多個雲供應商和地端部署的資料。真正的混合雲統一治理。

Q4 2026

Industry Solutions業界解決方案

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

為半導體、汽車和高階製造業預置的解決方案,包含領域特定資料模型、儀表板和 ML 模型。

Go-to-Market Approach市场推广策略

🌏 Asia-First Strategy亞洲優先策略

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.

深耕大中华區、台湾、韩国和东南亚等製造業驅動經濟增長的地區。地端團隊、地端語言支援以及與區域系統整合商的合作。

🏭 Vertical Focus垂直領域專注

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

專注于半導體、顯示器和高科技製造業。建構與業界特定挑戰產生共鳴的参考架構和驗證案例。

🤝 Partner Ecosystem合作伙伴生態

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

與雲端供應商(AWS、Azure、GCP、阿里巴巴、騰訊、華為)、系統整合商和技術供應商的策略合作,擴大覆蓋範圍並提供全面解決方案。

LongDB Technology龍迪數智科技

LongDB Technology (龍迪數智科技) is a Beijing-based data 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.

龍迪數智科技(LongDB Technology)是一家資料+AI平台公司,由在企業資料管理、AI/ML 和半導體製造領域擁有豐富經驗的業界資深人士創立。

我們的團隊融合了來自全球領先科技公司的專業知識,同時深谙亞洲市场需求、法規環境和商業惯例。

我們堅信企業資料的未來是統一的、智慧的、易于存取的。LongDB 正在建構這個未來——每次服務一位客戶。

Website官網

LongDB 官網

Sales Inquiries業務諮詢

[email protected]

辦公據點

臺北 · 北京 · 合肥 · 舊金山