Products & Services
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Core Product
Filling the Apache Ambari official Stack version gap — doing what the open-source community cannot.
HadoopForge is an enterprise-grade Hadoop cluster full lifecycle management platform independently developed by Shenzhen Qixuan Software Co., Ltd. Built on Apache Ambari 3.0.0 + Bigtop 3.5.0 ecosystem, it is the first commercial product in China to complete this adaptation, solving core pain points in enterprise big data infrastructure deployment, operations, and domestic substitution compliance.
Core Problems Solved
| Industry Pain Point | HadoopForge Solution |
|---|
| Ambari official Stack stuck on old versions, unable to manage Bigtop 3.5.0 components | Complete rewrite of BIGTOP/3.5.0 Stack with 15+ service metainfo.xml |
| Hive 4.0.1 fully incompatible with Ambari 3.x | Rewrote hive_check.py, Beeline replaces deprecated HiveCLI, fixed auth case sensitivity |
| Cloudera discontinued free edition, enterprises in dilemma | Open-source community edition free, enterprise edition 60%+ cost reduction, no vendor lock-in |
| 2027 domestic substitution 100% replacement deadline | Fully based on Apache open-source ecosystem, supports domestic chips and OS |
| Traditional deployment takes 2–3 weeks, 70% success rate | Visual drag-and-drop orchestration, 4–6 hours deployment, 98% success rate |
Core Product Features
- Complete BIGTOP/3.5.0 Stack Definition — 15+ service metainfo.xml rewritten, ${stack_version} dynamic mapping, Service Advisor logic rewrite, compatible with Hive 4.0.1 / Spark 3.5.0 / Kafka 3.7.0
- Visual Cluster Orchestration Engine — Drag-and-drop cluster topology design, auto-generated deployment plans, optimized resource allocation
- One-Click Automated Deployment — Dual-engine (Ambari + Bigtop) compatibility, minutes to go live, 98% success rate
- AI Intelligent O&M Brain — LSTM + Isolation Forest algorithm, fault prediction accuracy 92%, configuration drift detection and auto-repair
- Enterprise Security Integration — Kerberos + Ranger + OpenLDAP complete security stack
- Hive 4.0.1 Deep Adaptation — Industry’s first complete solution for Hive 4.0.1 compatibility, a problem the community left unsolved for 2 years
Supported Ecosystem Components (Bigtop 3.5.0)
- Storage Engines: HDFS, HBase, Kudu
- Computing Engines: Spark 3.5.0, Hive 4.0.1, Flink, Storm
- Message Queues: Kafka 3.7.0, Pulsar
- Data Warehouse / OLAP: Doris, ClickHouse, Hudi
- Security & Governance: Ambari, Ranger, Atlas, Kerberos
- Monitoring & Observability: Grafana, Prometheus
Customer Validation
- City Commercial Bank (80 nodes) — Deployment time reduced from 3 weeks to 5 hours, zero-downtime migration
- Telecom Operator (200 nodes) — 1.2PB daily processing, Hive 4.0.1 stable in production, AI alert accuracy 92%
- Manufacturing Enterprise (120 nodes) — Zero configuration drift incidents, O&M headcount reduced 40%, saving ¥1.2M per year
500+ nodes · 12 months · 0 production incidents · Based on Ambari 3.0.0 + Bigtop 3.5.0
Product Editions
- Complete BIGTOP/3.5.0 Stack definition, experience the latest features immediately
- Apache open-source ecosystem, rich upstream and downstream community projects
- Open source on GitHub, developer community support
- Completely free
Enterprise Edition
- ¥800–1,500 / node / year
- Professional commercial support team (phone, IM, email, on-site)
- Highest priority 7×24 fault response
- Expert support: planning / implementation / proactive inspection / fault investigation / knowledge transfer / critical period coverage
- AI O&M Brain advanced features (fault prediction + auto-repair)
- Domestic substitution compliance specialist support
Cloud Hosted Edition
- SaaS-style rapid deployment, easily manage cloud Hadoop clusters
- Supports major object storage (AWS S3, Alibaba Cloud OSS, MinIO, etc.)
- Elastic scaling, pay-as-you-go
- Unified permission security enhancement
Professional Services
Consulting Services
- Big data infrastructure planning and architecture design
- Ambari / Bigtop migration assessment and solution design
- Domestic substitution compliance consulting (2027 replacement target)
- Digital transformation strategy consulting
Implementation Services
- ¥1,500–2,500 / person-day
- HadoopForge cluster deployment
- Migration from legacy Ambari / Cloudera environments
- System integration and custom development
Operations Services
- 7×24 technical support hotline
- Cluster health monitoring and alerting
- AI-driven performance optimization
- Emergency fault response (Critical: within 1 hour)
Training Services
- Hadoop / Ambari principles and hands-on training
- HadoopForge administrator certification program
- On-site + online dual-mode delivery
- Enterprise customized training programs
Technical Support Response Levels
| Level | Response Time | Scenario |
|---|
| Critical P0 | ≤ 1 hour | Production cluster completely unavailable |
| High P1 | ≤ 4 hours | Core functionality impaired |
| Medium P2 | ≤ 8 hours | Partial feature anomaly |
| Low P3 | ≤ 24 hours | Inquiry and optimization advice |
Competitive Advantages
- Fills the Version Gap — First in China to complete Ambari 3.0.0 + Bigtop 3.5.0 full adaptation
- Hive 4.0.1 First-Mover — Industry’s only complete solution, fixing what the community left broken for 2 years
- 60%+ Cost Reduction — vs. Cloudera, with no vendor lock-in
- Domestic Substitution Native — Fully Apache open-source, supports domestic chips and operating systems
- AI-Driven O&M — LSTM + Isolation Forest, 92% fault prediction accuracy
Edge AI Inference Acceleration Product Line
Hongyuan Edge AI Inference Accelerator
The world’s first accelerator to deploy BitNet b1.58 quantization on FPGA hardware. BitNet W1.58 ternary quantization constrains weights to {-1, 0, +1}, replacing multiplication entirely with addition/subtraction — enabling every ¥200-class domestic FPGA to run AI Transformer inference.
2025 Shenzhen RISC-V & HarmonyOS Innovation Competition · Architecture Innovation Award 🏆
Key Metrics
| Metric | Value |
|---|
| Storage Compression | 16x |
| Sparse Weight Auto-skip | 33% |
| Matrix Computation Error | 0 (256/256 exact match) |
| Hardware BOM Cost | <¥200 |
| Domestic Component Rate | 100% (Anlogic FPGA + RISC-V + HarmonyOS LiteOS) |
| Full-Chain Validation | 6/6 All PASS, real hardware (no simulation) |
| Total Codebase | 16,487 lines (RTL + OS + inference engine) |
Technical Architecture (4-Layer Full-Stack)
- Application: Transformer inference engine (QKV / Multi-Head Attention / FFN full pipeline)
- OS: HarmonyOS LiteOS (task scheduling / IPC / memory management)
- Accelerator: BitNet 16×16 Tile array (ternary matrix multiply → add/subtract, 33% sparse skip)
- Hardware: Anlogic EG4X20 FPGA (20K LUT) · PicoRV32 RISC-V @50MHz · 128MB SDRAM
Product Roadmap & Pricing
| Phase | Product | Target Customer | Price |
|---|
| Phase 1 (Current) | AI Inference Dev Board | Universities / Makers / R&D | ¥499–799 |
| Phase 2 (6–12 months) | Vision Inference Module (OV2640 + FPGA + SDK) | Industrial inspection / Security / Agriculture | ¥1,299 |
| Phase 3 (12–18 months) | Wi-Fi AI Edge Node | Smart factory / Agricultural IoT | ¥1,999+SaaS |
| Phase 4 (18–24 months) | BitNet IP Core License (RTL IP) | FPGA / SoC vendors | ¥1M+ |
Target Applications
- University AI hardware labs · FPGA AI course experimental platforms
- Industrial vision inspection · <100ms latency, fully offline
- Smart agriculture IoT · Low power, works in field without internet
- Military / government research institutes · 100% domestic, no export control risk
Learn More →
Smart Terminal AI Middleware
Edge AI tiered inference platform for IoT / industrial embedded scenarios. Fills the gap between “can’t run models on-device” and “pure cloud is unreliable”, letting every MCU device answer device status queries in natural language.
Key Metrics
| Metric | Value |
|---|
| MCU Clock | 240MHz RISC-V |
| Peak SRAM Usage | 128KB |
| Local Inference Latency | <100ms (Tier 1 keyword) |
| Codebase | 3,500+ lines (MVP all modules) |
3-Tier Progressive Inference Architecture
- Tier 1 Keyword Match: <100ms · Stored in Flash · Zero dependency · Offline
- Tier 2 Rule NLP Engine: <500ms · 19 intent types · Local execution
- Tier 3 Cloud LLM: <3s · Calls cloud LLM when WiFi available
The inference scheduler automatically selects the optimal path based on query complexity + network state, with automatic fallback from Tier 3 to Tier 2.
Four Core Components
- AI Inference Engine Library — Portable to any RISC-V/ARM MCU + RTOS, unified
ai_engine_query() API - Smart CLI Framework — Mixed natural language and traditional command input, TAB completion, zero dependency
- Device Monitoring SDK — Standardized CPU / memory / temperature / network / sensor collection
- FPGA Acceleration Module (optional) — Compresses inference latency to under 200ms, theoretical 50x speedup
Business Model
- SDK licensing: ¥5–20 / device (tiered by volume)
- Cloud LLM inference service: ¥0.01 / call
- Custom development: ¥50K–500K / project
Learn More →
AI Education Product Line
Otowyue Learn AI (悦学智伴)
China’s first AI education platform integrating FSRS × VRM digital human deeply. Fully offline deployment with data never leaving the device. Emotion sensing at the core, FSRS scientific memory as the engine, VRM digital humans as the interface — an AI teacher with soul.
Key Metrics
| Metric | Value |
|---|
| Emotion Recognition Accuracy | 92% |
| FSRS Memory Retention Rate | 93%+ |
| Learning Efficiency Improvement | 3x vs. traditional methods |
| End-to-End Voice Response | <800ms |
| Compatible LLM Models | 11+ |
| Production Code Volume | 60,000+ lines |
Five Core Innovations
- Emotion Sensing Engine — Text / voice / expression 3-channel multimodal fusion, 92% accuracy, 8 emotion types
- FSRS × Digital Human (China First) — MIT open-source FSRS algorithm + VRM digital human deep integration, 42% retention improvement
- Knowledge Graph Navigation — Covers all K-12 subjects, prerequisite/dependency mapping, automatic bottleneck detection
- Four-Role AI Collaboration — AI Teacher / AI Assistant / AI Advisor / AI Counselor working in concert
- VRM Digital Human Interaction — Humanized digital teacher, MFA phoneme-level pronunciation evaluation, 11+ LLM compatible
Fully Offline Architecture
Tech stack: Ollama + Sherpa-ONNX + MeloTTS + VRM + FSRS
- Video stays on device, only emotion labels transmitted — meets school intranet security requirements
- Works without internet — applicable to rural and remote schools
- Apache 2.0 open source license, freely customizable, no vendor lock-in
- 11+ LLM compatible, LLM-agnostic design
Product Editions & Pricing
| Edition | Target Customer | Pricing |
|---|
| ToB Institutional | K-12 schools / Training institutions / Corporate | ¥50K–200K / year (SaaS) |
| ToC Family | Students / Individual learners | ¥39–99 / month |
| ToG Government | Education bureaus / Rural revitalization | ¥500K–2M / project (fully private) |
Social Impact
- Covering 200+ rural schools, benefiting 100,000+ students — advancing educational equity
- 92% emotion issue detection rate, early mental health warning system
- Reduces 50% of repetitive teacher workload, 60% teaching efficiency improvement
Learn More →