Industry Solutions Overview
Singdata Lakehouse consolidates business scenarios that traditionally required multiple independent systems (stream processing clusters + AI inference services + data warehouses + BI + vector databases) into a single platform implemented with pure SQL, through core capabilities including Dynamic Table · AI Functions · Volume · PIPE · MERGE INTO · Window Functions · Full-text/Vector Search.
This page summarizes all currently published industry solutions to help you quickly locate the solution documentation that best matches your scenario.
Solution Landscape
| Solution | Industry | Core Requirement | Key Technologies |
|---|---|---|---|
| Manufacturing Part Defect AI Detection & Classification | Manufacturing | Dual-channel defect detection via production line images + text descriptions, with tiered AI inference triggering | AI_CLASSIFY · AI_EXTRACT · AI_COMPLETE · Dynamic Table · Volume |
| Equipment Predictive Maintenance | Discrete/Process Manufacturing | IoT sensor rolling-average anomaly detection + automated AI maintenance recommendation generation | AI_COMPLETE · Dynamic Table · BloomFilter Index · API Connection |
| Supply Chain Inventory Optimization | Manufacturing / Retail / E-commerce | Dynamic replenishment decisions + real-time supplier lead time integration, replacing static ERP models | Window Functions · Dynamic Table · MERGE INTO · AI_EXTRACT |
| Smart Mine Safety Alert | Mining / Heavy Industry | Cross-system correlation alerts across six subsystems + AI action recommendations + RAG historical knowledge retrieval | AI_CLASSIFY · AI_COMPLETE · AI_EMBEDDING · Dynamic Table · Full-text/Vector/Hybrid Search |
| Customer Complaint Intelligent Labeling | E-commerce / Retail / Local Services | Automatic customer service ticket classification, replacing manual labeling, end-to-end latency ≤5 minutes | AI_COMPLETE · Dynamic Table · API Connection |
| Email Customer Support Auto-Triage | E-commerce / 3C / Cross-border | Single AI call completes intent classification + entity extraction + reply draft, latency ≤10 minutes | AI_COMPLETE · Dynamic Table · REGEXP_EXTRACT · GET_JSON_OBJECT |
| Product Review Sentiment Analysis | E-commerce Platforms | Real-time sentiment classification of Kafka review streams + structured summary extraction | AI_SENTIMENT · AI_COMPLETE · CREATE PIPE · Dynamic Table |
| User Behavior Funnel Analysis | E-commerce / Local Services / Cross-border | Multi-channel funnel conversion rate auto-aggregation, latency ≤1 hour, pinpointing largest drop-off stages | Dynamic Table · MERGE INTO · COUNT DISTINCT · Window Functions |
| Autonomous Driving Full-Loop Data Platform | Autonomous Driving | Full loop from road test data collection → labeling → training set → model iteration | Dynamic Table · Volume · AI_COMPLETE · Table Stream |
Select by Technical Capability
Starting from "I want to use a specific Lakehouse feature" — find the corresponding reference solution:
| Technical Capability | Reference Solutions |
|---|---|
AI_CLASSIFY (classification function) | Defect AI Detection · Smart Mine (disaster type rapid classification) · Supply Chain Inventory Optimization (SKU ABC classification extension) |
AI_EXTRACT (structured extraction) | Defect AI Detection · Supply Chain Inventory Optimization (supplier notification parsing) |
AI_COMPLETE (LLM inference) | Predictive Maintenance · Smart Mine · Complaint Labeling · Email Customer Support · Product Reviews · Defect Detection · Autonomous Driving |
AI_SENTIMENT (sentiment classification) | Product Review Sentiment Analysis |
AI_EMBEDDING (text vectorization) | Smart Mine (historical incident semantic retrieval + RAG) |
| Dynamic Table (incremental computation) | All solutions use this |
MERGE INTO (idempotent archiving / UPSERT) | Supply Chain Inventory Optimization · User Behavior Funnel |
| Window Functions (multi-window / sliding averages) | Supply Chain Inventory Optimization · Smart Mine (gas trend prediction) · User Behavior Funnel |
CREATE PIPE (Kafka ingestion) | Product Review Sentiment Analysis |
Volume / GET_PRESIGNED_URL (image ingestion) | Defect AI Detection · Autonomous Driving |
REGEXP_EXTRACT + GET_JSON_OBJECT (LLM output parsing) | Email Customer Support Auto-Triage · Predictive Maintenance · Smart Mine |
| BloomFilter Index (high-cardinality column equality query acceleration) | Predictive Maintenance · Smart Mine (zone_id / sensor_type point queries) |
| Full-text Search + Inverted Index (Chinese keyword search) | Smart Mine (incident reports, action record retrieval) |
Vector Index + AI_EMBEDDING (semantic similarity / RAG) | Smart Mine (historical incident experience semantic retrieval injected into Prompt) |
| Full-text + Vector Hybrid Search (Hybrid Search) | Smart Mine (dual-optimized precision + recall) |
| API Connection (AI model credential management) | Predictive Maintenance · Smart Mine · Complaint Labeling · Email Customer Support |
| Table Stream (change capture) | Autonomous Driving Full-Loop |
Select by Business Need
I want to use AI to analyze unstructured data
Images / Video: See Defect AI Detection to learn how to connect images to AI functions via Volume + GET_PRESIGNED_URL.
Text classification (single label): See Complaint Intelligent Labeling for the most streamlined LLM classification pipeline — only three layers of Dynamic Tables from source to labeled result.
Text multi-task (classification + extraction + draft generation): See Email Customer Support Auto-Triage, which demonstrates a single AI_COMPLETE call completing five tasks simultaneously through a structured JSON prompt, plus the pattern of stable LLM output parsing using REGEXP_EXTRACT + GET_JSON_OBJECT.
Text sentiment + structured summary extraction: See Product Review Sentiment Analysis, which shows dual-function division of labor between AI_SENTIMENT and AI_COMPLETE, plus tiered triggering cost control that skips AI for neutral reviews.
Extracting fields from unstructured notifications: See Supply Chain Inventory Optimization for supplier lead time parsing — AI_EXTRACT converts email/notification text into structured fields that directly drive business logic.
Historical document knowledge base + RAG enhancement: See Smart Mine, which shows how to build a searchable knowledge base from historical incident reports using AI_EMBEDDING + vector index, then inject retrieval results into AI_COMPLETE prompts — no standalone vector database (Milvus/Pinecone) required.
I want to build near-real-time data pipelines
All solutions use Dynamic Table for incremental refresh without external schedulers. Complexity from low to high:
- Simplest (row-by-row AI inference): Complaint Labeling · Email Customer Support — three layers of Dynamic Tables, new data completes AI classification within ≤10 minutes of write
- Medium (aggregation + threshold filtering): Predictive Maintenance · User Behavior Funnel — aggregation for noise reduction/UV statistics, then filtering/MERGE into summary tables
- Multi-level multi-frequency (industrial safety): Smart Mine — real-time pipeline (1-minute Studio Cron) + aggregation layer DT (5 minutes) + cross-system correlation DT (5 minutes) + trend prediction DT (5 minutes), four complementary refresh intervals
- Complex (stream ingestion + multi-layer AI): Product Review Sentiment Analysis — Kafka PIPE ingestion + four-layer Dynamic Tables + dual AI functions + aggregated views
I want to do e-commerce operations data analysis
- Funnel conversion rate: User Behavior Funnel Analysis — multi-channel UV statistics, three-segment drop-off breakdown,
MERGE INTOidempotent write to summary tables, latency ≤1 hour - Negative review alerts: Product Review Sentiment Analysis — real-time tagging of negative reviews, aggregated positive review rate by SKU, driving quality control and proactive follow-up
- Customer service ticket efficiency: Complaint Intelligent Labeling + Email Customer Support Auto-Triage — automatic ticket classification routing, high-priority alerts, AI reply drafts — all three in one
I want to replace static decision models in MES/ERP
See Supply Chain Inventory Optimization. The solution demonstrates dynamic replenishment calculation with Dynamic Table, automatic switching between supplier real-time lead times and ERP static values using COALESCE, and idempotent archiving with MERGE INTO — all without modifying existing systems.
I want to deploy AI in industrial scenarios
- Quality inspection: Defect AI Detection — dual image + text channels,
AI_CLASSIFYfull-volume classification followed by tieredAI_COMPLETEtriggering, cost-controlled - Equipment O&M: Predictive Maintenance — after sensor data lands in the Lakehouse, Dynamic Table automatically completes rolling-average aggregation, multi-dimensional threshold filtering, and AI recommendation generation in three pipeline layers, no additional AI service needed
- Safety production: Smart Mine — cross-system JOIN correlation alerts across six subsystems,
AI_CLASSIFY+AI_COMPLETECTE chaining, RAG injection of historical incident experience, PoC launch in 6 weeks
I have large-scale multimodal data to manage
See Autonomous Driving Full-Loop. This solution covers unified management of structured time-series data, semi-structured JSON events, and large files (Parquet annotation packages), as well as the complete closed-loop architecture from data collection to model iteration — a reference blueprint for other data-intensive industries (precision agriculture, medical imaging, satellite remote sensing).
Key Metrics by Solution
| Solution | Typical Data Scale | End-to-End Latency | AI Cost Control Strategy |
|---|---|---|---|
| Defect AI Detection | Tens of thousands of images per production line per day | Dynamic Table refresh interval | AI_CLASSIFY first, then decide whether to invoke AI_COMPLETE based on result, saving ~40% of calls |
| Predictive Maintenance | Multi-dimensional sensor data per device per second | ≤10 minutes | Two-level threshold filtering, AI triggered only for medium/high risk, reducing call volume by 90%+ |
| Supply Chain Inventory Optimization | Multiple warehouses × hundreds of SKUs × daily snapshots | Hourly refresh | AI_EXTRACT only processes supplier notification text; AI_COMPLETE only triggered for anomalous-demand SKUs |
| Smart Mine Safety Alert | Single mine: 500–2,000 sensors; group-level: millions of points | ≤90 seconds (real-time pipeline) / 5 minutes (DT aggregation) | Two-level threshold pre-filtering, only HIGH/CRITICAL triggers AI; BoolFilter narrows candidate set before vector search |
| Customer Complaint Labeling | Daily average 5,000–50,000 tickets | ≤5 minutes | Incremental refresh, each ticket calls AI only once |
| Email Customer Support Auto-Triage | Continuous incoming customer service emails | ≤10 minutes | Single call completes five tasks, ~60% fewer token consumption vs. three separate calls |
| Product Review Sentiment Analysis | Continuous Kafka stream | ≤10 minutes | Neutral reviews skip AI_COMPLETE, saving ~35% tokens |
| User Behavior Funnel | Daily tens of millions of events, multi-channel | ≤1 hour | Pure SQL aggregation, no AI call cost; scalable to BITMAP approach for very large scale |
| Autonomous Driving Full-Loop | Fleet of millions of vehicles, peak 1M msg/s | Minutes to hours (by pipeline layer) | Long-tail scenarios trigger labeling; non-critical data bypasses AI pipeline |