Gartner’s latest analysis warns that the traditional data middle office is entering the Trough of Disillusionment and may disappear, while a new Data Infrastructure paradigm—cloud‑native, flexible, and AI‑enabled—emerges as the future engine for enterprise digital transformation.
This article presents JD Retail’s metric middle‑platform practice, describing the background problems of legacy metric systems, the four‑step solution framework, the overall architecture, semantic‑layer construction with the 4W1H method, configurable metric production, acceleration techniques, governance mechanisms, achieved results and future plans.
The article provides an in‑depth technical overview of data middle‑platform architecture, explaining its six decoupled subsystems—storage, collection, processing, governance, security, and operation—while illustrating how enterprises can use this layered approach to centralize data, improve agility, and unlock data‑as‑a‑service across various industry scenarios.
This article details Kuashou's metric middle platform, covering its role as a semantic layer, the unified architecture of OneMetric and OneService, metric standardization processes, automated modeling techniques, the Headless BI concept, and future directions for intelligent modeling and acceleration.
This article explores the evolution and architecture of cloud‑native data platforms, covering their historical roots, modern components such as storage layers, ingestion, processing, metadata, and consumption, and offers practical guidance on selecting tools, designing pipelines, and implementing best‑practice strategies for scalable, flexible data infrastructure.
This article describes the motivation, architecture, technology choices, and operational practices of building a lightweight Node.js middleware layer that acts as an API gateway for a data platform, covering its positioning, NestJS selection, Kubernetes deployment, health probes, and logging strategies.
This article details Minsheng Bank's data middle platform built since 2018, explaining its cloud‑native architecture, the underlying microservice and container design, the operational pain points it addresses, and the suite of DevOps tools, management solutions, and component strategies that enable scalable, secure, and efficient financial data services.
