This article explains how a comprehensive indicator system within a data middle platform can address the automotive industry's data challenges, outlines the evolution of data platforms, details a step‑by‑step methodology for indicator design, development, and management, and presents real‑world case studies.
The article examines how China's data middle platform concept is reshaping enterprise data strategy, highlighting a shift toward value‑driven adoption, the intertwined relationship with data governance, and emerging trends such as fine‑grained business governance, full‑link monitoring, integrated platforms, and DataOps.
This article analyzes why telecom operators need a real‑time data middle platform, outlines its layered architecture and model design, examines the shift from Lambda to Kappa and lakehouse approaches, and highlights how these innovations enable faster, scenario‑driven insights and competitive advantage.
This article explains the concept, necessity, principles, and practical implementation of a data middle platform, outlining its methodology, organizational structure, technical architecture, and the challenges it solves such as inconsistent metrics, data duplication, low query efficiency, poor quality, and high construction costs.
This article explains what a data middle platform is, why organizations need it, its core principles, technical architecture, and practical implementation guidelines, highlighting how it solves issues like inconsistent metrics, duplicate data construction, low query efficiency, poor data quality, and high development costs.
This article traces the evolution from traditional data warehouses to modern data lakes and data middle platforms, explains why siloed data development hampers efficiency, and details the architecture and indicator‑library design used by Zhengcaiyun to achieve unified, reusable data services.
The article outlines Alibaba's twelve‑year journey building a data middle‑platform, detailing four development stages, the four major technical challenges faced, and emerging trends such as lake‑warehouse integration, autonomous data‑warehouse operation, natural‑language query, and AI‑driven data engineering.
The article outlines Alibaba's twelve‑year journey of building a data middle platform, describing four development stages, the technical challenges faced, and emerging trends such as lake‑warehouse integration, autonomous data‑warehouse operation, natural‑language query, and AI engineering.
This article explains what tags are, how they function as data assets, defines the concept and architecture of a data middle platform, and demonstrates why tags are the pivotal element that enables enterprises to turn raw data into valuable, reusable business services.
This article details Kuashou's construction of a unified metric middle‑platform called Gaia, the OneMetric and OneService systems for standardized metric management and service, their architectural design, key technologies, achieved outcomes, and plans for expanding data services across the company.
This article explains how new‑retail companies can design and implement a data middle platform using Alibaba Cloud's DataWorks, covering business model analysis, technical architecture, layer‑by‑layer data modeling, governance, security, and the concrete benefits of turning raw data into actionable business insights.
This article provides an extensive summary of data middle platform concepts, covering data aggregation, collection tools, offline and real‑time development, data governance, service layers, warehouse construction, and operational practices, illustrating how enterprises build and manage a unified data ecosystem.
This article compares data warehouse, data lake, data platform, and data middle platform, explaining their definitions, architectures, strengths, limitations, and use‑case differences, and provides tables that highlight how each solution handles structured and unstructured data, governance, flexibility, and business value.
The article explains what a data middle platform is, why it differs from a traditional big‑data platform, the efficiency, collaboration and talent challenges it addresses, its definition as a data‑driven innovation layer built on big data, cloud and AI, and outlines its logical architecture centered on data APIs.
The article outlines a practical five-step methodology for constructing a data middle platform, covering data resource inventory, application planning, asset construction, detailed design and implementation, and organizational planning to guide enterprises toward effective data-driven transformation.
This talk reviews China's big‑data evolution from early enterprise data warehouses to modern data middle platforms, outlines core architectural components, technology selections, data development practices, lifecycle and quality management, and shares practical Q&A insights for building scalable, cost‑effective data infrastructures.
YouZan’s Metric Library is a unified indicator management system that resolves inconsistent metric definitions, duplicate development, and unclear lineage by standardizing dimensions, atomic metrics, modifiers, and automatically generating derived metrics, then supporting scheduling, batch operations, and online APIs through integration with its data warehouse and service platforms.
During the pandemic, Tencent Cloud University and its Most Valuable Experts launched a free online course, led by ThoughtWorks' Shi Kai, to teach enterprises how to build data middle platforms with a lean data innovation system, offering live sessions, a weekly schedule, and incentives such as USB drives and Bluetooth speakers.
The Data Middle Platform, described by Shi Kai, is a service‑oriented architecture that transforms raw enterprise data into reusable, real‑time APIs for business applications, bridging the gap between traditional warehouses and front‑end systems, accelerating digital transformation through unified governance, rapid development, and direct business value.
The article explains why companies are building data middle platforms, defines what a data middle platform is, and details NetEase Yanxuan’s architecture, including its data warehouse, data services, and BI platform, illustrating how these components enable data‑driven transformation and fine‑grained operations.
The article explains the data middle platform concept, its distinction from traditional big‑data platforms, the architectural principles behind Alibaba's implementation, and how real‑time ingestion, processing, and service layers enable efficient, collaborative, and scalable data-driven applications.
This article explains what a data middle platform is, outlines its construction process, discusses how to choose suitable products, and presents enterprise case studies, offering a comprehensive guide to building and leveraging a data middle platform for big‑data initiatives.
Drawing on NetEase Yanxuan’s experience, this article explains what a data middle platform is, why companies are building one for digital transformation and fine‑grained operations, and details its core components—including the data warehouse, data services, and BI platform—illustrated with real‑world diagrams.
The article explains the concept of a data middle platform, why it matters for rapid demand response and resource integration, and outlines the distinct responsibilities and required skill sets of data product managers and data platform product managers within such ecosystems.
This article details Mafengwo’s journey in constructing a data warehouse and data middle platform, covering the core three‑layer architecture, hybrid modeling approaches, the supporting toolchain for data synchronization, scheduling, and metadata management, and the design of an indicator platform for business analytics.
The article explores the rapid rise of data middle platforms in China, tracing their historical roots, explaining their core purpose of unifying data across legacy and new systems, showcasing Shulan Technology’s real‑world implementations, and analyzing market dynamics and future opportunities for enterprises and startups alike.
The article explains what a data middle platform is, why it differs from data warehouses and data platforms, outlines its core capabilities, discusses the strategic and tactical considerations for building one, and examines the organizational, technical, and privacy challenges involved in its adoption.
The article explains the emergence of data middle platforms in the era of digital and intelligent transformation, defines their architecture and functions, outlines four key reasons for building them—including data reuse, AI enablement, innovation acceleration, and talent development—and provides practical principles for successful implementation.
In this detailed account, Alibaba senior technologist Zhang Lei explains the concept of a full‑domain data platform, its “four‑horizontal and three‑vertical” architecture, the OneData ecosystem, cost‑saving strategies, data quality tools, and practical challenges of building and operating massive big‑data infrastructure across the Alibaba ecosystem.
