Challenge 01Mapping · SQL · Lineage
Slow response to analysis needs
Business and manufacturing analysis still depended on manual table mapping, SQL writing, and lineage checks.
How a Global Solar Manufacturer Improved Data Engineering Efficiency and Built a Trusted, AI-Ready Data Foundation
An AI data engineer supports modeling, engineering, tuning, quality, and lineage analysis to build a trusted, AI-ready data foundation for solar manufacturing.
Global shipment leaderServing 160+ countries and regions3,000+ customers
About the customer
A global solar manufacturing leader covering ingots, wafers, cells, and high-efficiency modules, serving more than 160 countries and regions and over 3,000 customers, and ranking among global module shipment leaders for years.
- Countries and regions
- 160+
- Global customers
- 3,000+
- Long-term global shipment leader
- Leader
Key challenges
Before its intelligent transformation, the customer faced critical bottlenecks across solar and new energy manufacturing.
Challenge 02Sites · Systems · Definitions
Data trust was difficult to maintain
Inconsistent definitions across sites and systems, schema changes, and task failures affected downstream reporting.
DataAGI solution
DataAGI acts as an AI data engineer, automating natural-language modeling, DDL/SQL generation, SQL tuning, task configuration, lineage analysis, and impact assessment.
1Data assets
Source metadata, development assets, standards, and quality data across the data lifecycle.
2AI foundation
DataAGI development Skills plus metadata, publishing, and model-switching capabilities.
3Development agents
Agents for SQL and Python generation, quality and lineage analysis, schema evolution, and model architecture.
Natural-Language Request
Modeling and code
Quality and lineage
Publish and audit
Production-Ready Data Asset
Business impact
A measurable shift in efficiency and quality that makes AI value visible.
Higher data engineering efficiency
DataAGI automates modeling, DDL/SQL, tuning, task configuration, lineage, and impact assessment to turn business logic into production-ready data assets.
Stronger data quality and governance
DataSimba unifies metadata, metric definitions, tasks, and lineage, while DataAGI generates quality rules, assesses schema-change impacts, and evaluates release risks.
- Improvement in data engineering efficiency
- 70%
- Modeling / SQL / lineage automation
- Reduction in data quality issues
- 50%
- Automated rules and release checks
- Increase in trusted data coverage
- 90%
- Unified definitions, lineage, and audit