Deploying a petabyte-scale data lake facilitates improved query-response times, fine-grained access controls, audit logging, AI readiness, compared to its legacy platform.
Battery maker China Aviation Lithium Battery (CALB) is a partially state-owned developer and manufacturer of lithium-ion batteries for electric vehicle manufacturers in China and abroad.
Rapid business expansion has exposed limits in its legacy data systems, which have struggled to support smart manufacturing, large-scale research and development, and tighter information security around proprietary battery formulas.
In early 2025, the firm had begun modernizing its data infrastructure to support future AI and data analytics platforms while addressing regulatory and cybersecurity requirements. Previously, several key concerns were:
- Fragmented systems could not provide full lifecycle traceability across production, quality, and supply chain data at the scale the company required.
- Legacy access controls were coarse-grained, making it difficult to enforce differentiated permissions on sensitive formula data while still giving engineers timely access to operational information.
- The operational environment suffered from bottlenecks when many users queried data simultaneously, constraining real-time analytics for research and development teams.
To address these issues, CALB implemented an enterprise-wide data platform built as a petabyte-scale data lake to integrate core system data sources and support end-to-end battery lifecycle analysis. The production rollout started in the first quarter of 2025 and was completed by the fourth quarter of the same year. Key technical elements of the transformation included:
- Enterprise-wide data lake architecture supporting petabyte-scale storage and analytics
- Integrated data from research, production, and supply chain systems for lifecycle analysis of batteries
- Fine-grained, column-level access controls and dynamic data masking for sensitive formula data
- Centralized authentication and authorization mechanisms to enforce enterprise security policies
- High-concurrency query handling to remove data bottlenecks and enable near real-time analytics
- Comprehensive audit logging across data access and operations to support compliance
The upgraded environment has strengthened protection of CALB’s proprietary data, improved support for large-scale analytics, and created a more robust foundation for future AI workloads. The new data platform allows the firm to “operate at scale in production while maintaining strict security and auditability for sensitive information.”
CALB’s data platform transformation, provided by Cloudera, ensures that data from different systems can be analyzed in place, without having to be repeatedly copied and transformed, which should speed up sharing between sites and lower the cost of cross-team collaboration.
