Practice of Lakehouse‑Integrated Data Platform Architecture in the Financial Innovation Sector
This article presents the evolution of data platform architectures, the specific challenges of financial‑sector information‑technology innovation, and the design, core components, deployment path, and real‑world case studies of the cloud‑native lakehouse solution DataCyber developed by Shuxin Network.
The article outlines four stages of data platform evolution—from traditional data warehouses to cloud‑native lakehouse architectures—highlighting trends such as cloud‑native integration, lake‑warehouse convergence, and compute‑storage separation.
It then examines the unique challenges faced by the financial innovation ("Xinchuang") market, including the need for domestic component adaptation, performance and security assurance, and heterogeneous cluster migration.
DataCyber, Shuxin Network’s cloud‑native data‑intelligent platform, is introduced with its design goals of autonomy, openness, multi‑tenant support, and AI integration, followed by a detailed architecture comprising CyberEngine, CyberData, CyberAI, and CyberMarket.
The core technical components—CyberLakehouse, unified metadata service, scheduler, integration engine, and market—are described, emphasizing support for Kubernetes, heterogeneous hardware, and security frameworks such as Kerberos and Ranger.
A practical deployment roadmap for financial Xinchuang data platforms is provided, covering unified management, pilot selection, resource planning, data migration, stress testing, and gradual cut‑over.
Finally, two case studies demonstrate successful migration from CDH to the Xinchuang‑compatible lakehouse stack for a major bank and the construction of a provincial financial service platform that leverages DataCyber’s data, AI, and sandbox capabilities.
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