The article details JD's borderless retail concept by presenting the experience architecture, AI‑driven interaction, edge‑perception layers, technical stack, and data‑driven metrics that together enable a low‑cost, scalable digital transformation of fashion brick‑and‑mortar stores.
Netflix’s data‑driven strategy, which uses massive subscriber analytics to shape original content and marketing, has sparked a clash with Hollywood’s traditional, relationship‑focused approach, leading to internal power struggles, leadership changes, and a broader debate over algorithmic versus human intuition in entertainment.
At Meitu’s Technology Salon, senior big‑data experts detailed the end‑to‑end architecture and stability measures of Meitu’s large‑scale data platform, introduced the high‑performance distributed bitmap solution Naix, showcased the evolution of Meizu’s user‑insight system, and highlighted Apache Kylin’s OLAP capabilities and Superset integration for scalable, real‑time analytics.
The article explains how Alibaba's concept of a "city brain" leverages internet, cloud computing, and massive data to apply machine intelligence for real‑time traffic management, public service optimization, and sustainable urban development, positioning data as a critical resource surpassing land in future cities.
This article examines how big data technologies are reshaping banking, insurance, and securities by enabling customer profiling, precision marketing, risk management, and operational optimization, while also outlining the key challenges such as data quality, integration complexity, standards, and governance that the sector must overcome.
This article details the design, technology selection, and implementation of WMDA, a no‑tracking (no‑annotation) user‑behavior analysis SDK for Android, covering bytecode‑instrumentation via a Gradle plugin, event collection for activities, fragments, and clicks, as well as processing, storage, reporting, and current limitations.
This article explains the definition of big data, its characteristic 3Vs, common data sources, supporting IT infrastructure, key technologies such as Hadoop and Spark, specialized databases, required skills, and several practical business use cases.
The article examines how the rapid growth of IoT devices in 2018 raises significant security concerns, predicts large‑scale attacks and data challenges, and highlights a shift toward edge computing and tighter IT‑OT collaboration to manage the expanding data and protect networks.
This article summarizes Wu Xiaoguang's talk at Gdevops 2017, detailing how Vipshop integrates data processing, analysis, and mining technologies—such as Flume, Kafka, Spark, and custom scheduling—to improve operational decision‑making, performance monitoring, root‑cause analysis, and predictive modeling across its e‑commerce platform.
This article analyzes Kaminario’s K2 and K2.N storage solutions, illustrating how SSD/NVMe core technology, hybrid‑cloud capabilities, and integrated AI/ML operations combine to deliver high‑performance, scalable, and intelligent enterprise storage.
This article discusses JD.com's Y Business Unit's AI-driven supply chain optimization, focusing on sales prediction algorithms, inventory control, and the challenges of predicting new product sales.
This article provides an extensive English overview of fifty additional big-data terminology and Apache open-source projects, explaining concepts such as Kafka, Hive, Spark, data cleaning, AI, graph databases, and many other tools and techniques essential for modern data engineering and analytics.
The article explains Gartner’s definitions of Business Intelligence and Artificial Intelligence, compares their core capabilities, discusses whether AI is over‑hyped, and illustrates how AI‑driven solutions differ from traditional BI across marketing, public safety, finance, and education sectors.
This article explains what big data is, outlines its 5V characteristics, and showcases numerous real‑world applications such as personal finance monitoring, tax fraud detection, healthcare prediction, public opinion tracking, precise marketing, product development, traffic planning, strategic decision‑making, and credit scoring.
This article discusses the importance of tracking user conversions and attributing them to specific marketing efforts, including methods like API integration and code embedding, along with various attribution models and machine learning approaches for accurate prediction.
The article explores how intelligent retail leverages AI-driven scenarios, facial recognition, smart pricing tags, virtual fitting rooms, and data analytics to enhance consumer experience, streamline operations, and boost conversion rates for retailers, suppliers, and shoppers alike.
The article explores the evolution of big‑data applications in China, from Oracle’s trend report and the concept of "omni‑domain data" to UMENG+’s technical architecture, unified tech stack, AI integration, and future directions for delivering real customer value.
Analyzing reading statistics of 23 original articles published before the 2017 Chinese New Year, this report reveals that SQL on Hadoop, Lambda architecture, and Docker+Jenkins were the three hottest big‑data topics, while also discussing the rise of Kappa, SQL optimization importance, and ongoing innovation in the field.
The article introduces QReport, a front‑end‑focused real‑time data reporting system developed to overcome the limitations of the Beacon log‑analysis platform, detailing its architecture, page‑identification method, flow analysis, real‑time capabilities, richer metrics, and overall advantages for web analytics.
On July 23, 2016, Hujiang hosted a technology salon focused on data applications, featuring five expert presentations covering data-driven operations in online education, O2O logistics, e‑commerce recommendation systems, pitfalls in personalization, and deep‑learning‑based image search, accompanied by case studies and visual materials.
At the 2016 China Big Data Industry Summit, Ctrip's CTO explained how the company's massive travel data and predictive analytics improve tourism planning, marketing, and privacy protection, illustrating the practical value of big data for users, platforms, and suppliers.
This article explains how the AirTrack platform combines A/B testing, dynamic experiment conditions, a real‑time SDK, and a data feedback system to let mobile teams quickly validate decisions, perform gray releases, and personalize features without waiting for full app version cycles.
The article explains why user‑behavior data is often ignored by UED teams, outlines a three‑stage collection‑analysis‑reporting pipeline, and shows how JavaScript running on V8/Node.js can empower front‑end developers to process massive logs, reducing reliance on separate BI resources.
In his 2015 China Hotel Marketing Summit keynote, Ctrip CTO Ye Yamin explained how machine‑learning models built on purchase behavior and order data improve hotel room availability predictions, shorten confirmation times, personalize recommendations, and evaluate advertising effectiveness, illustrating a data‑driven approach to user experience and operations.
This article explains how to combine backend analytics and user research techniques—such as questionnaires and deep‑interview simulations—to identify and refine target user groups at every stage of a product’s lifecycle, providing practical metrics and actionable insights.
The article examines the evolution of database technologies—from traditional relational databases and their ACID guarantees to NoSQL, NewSQL, and Hadoop—illustrating how a gaming company can combine these solutions to handle massive online traffic, ensure data integrity, and enable advanced analytics.
Using search query volumes, Baidu’s big‑data engine forecasts tourist numbers for Chinese attractions, achieving over 90% accuracy and partnering with CCTV to broadcast real‑time crowd predictions during the May Day holiday, while also outlining future forecasting plans for flu, sports, finance and real‑estate trends.
The article surveys the rapid growth of big data across sectors, highlights key open‑source technologies such as Hadoop, Spark, HBase and others, and profiles ten influential companies—including AWS, Cloudera, Hortonworks, IBM and Microsoft—offering insight into current trends, capabilities and competitive dynamics in the big‑data ecosystem.
