Designing a Unified Enterprise Data Storage and Compute Platform

Unified Data Storage and Compute Overview

After a company launches its digital transformation strategy, it must first standardize and efficiently collect the data generated by various business processes, then develop it within a scientific framework. A unified data platform aggregates scattered data, provides exploration capabilities, and supports massive, continuously growing storage and analytical compute.

With rapid advances in big data technologies and accumulated enterprise experience, a complete methodology has emerged, consisting of platform system construction and technical capability development.

Functional Requirements for Storage and Compute

The foundational layer must support diverse data types—structured, semi‑structured, and unstructured—such as documents, contracts, time‑series, and geospatial data. It should scale to petabyte‑level storage, handle high‑concurrency writes, searches, and queries, and offer standard SQL development with ACID transactions. The compute side must enable both batch and real‑time processing.

Supporting tools are essential for data integration (offline and real‑time), operations, and security management, including graphical operation consoles and access‑control policies.

Architecture Requirements

Architecture choices have evolved from early Hadoop stacks to modern cloud‑native, multi‑model database designs. The platform must ensure high concurrency, high throughput, high availability, and secure data links. Business support layers provide SQL/API development, resource management, and security functions.

The storage‑compute layer, as the core, must be distributed, highly scalable (both horizontal and vertical), support multiple data models, and enable mixed real‑time and batch processing.

Resource Management Layer

This layer handles deployment, scheduling, lifecycle, multi‑tenant isolation, heterogeneous hardware management (including GPUs), and supports both short‑lived tasks (e.g., ETL, model training) and long‑running services (e.g., AI inference). It also accommodates domestic (国产) hardware and software ecosystems.

Conclusion

The article outlines the foundational layer of an enterprise data platform—storage and compute—detailing functional and architectural considerations. After establishing this layer and consolidating data resources, the next step is to transform data into business‑valuable assets, which will be covered in the following article.