This article presents a visual case study of a company's data governance and data middle‑platform implementation, outlining the project background, solution architecture, and the resulting business value and effects through a series of illustrative images.
The article explores how generative AI transforms business intelligence by detailing BI's evolution, the ChatBI platform's architecture, NL2SQL challenges, performance and accuracy optimizations, and real‑world deployment outcomes that demonstrate reduced user barriers and enhanced analytical efficiency.
The article presents Turing Data Analysis (TDA), a self‑service BI platform that replaces fragile traditional pipelines with a unified DWD‑based data model, drag‑and‑drop analytics, multi‑engine query optimization and caching, delivering sub‑10‑second queries on billions of rows, fine‑grained permissions, and rapid dashboard creation, while reporting significant usage growth and outlining AI‑driven future enhancements.
Data middle platforms transform raw enterprise data into reusable assets by integrating collection, storage, processing, governance, and service layers, enabling faster deployment, consistent metrics, improved data quality, and business value across digital transformation, while addressing challenges like siloed data, low efficiency, and inconsistent standards.
This article outlines the core capabilities of data-driven development, the background and architecture of DataOps, its research challenges and focus areas, and explores future directions such as data virtualization, platform governance, and data value assessment, providing a comprehensive overview of DataOps practices.
This article examines ByteDance's DataOps journey, detailing the data‑engineering challenges faced, the concrete solutions and productization through the DataLeap platform, the metrics and best‑practice framework adopted, and the future directions involving AI‑assisted development and open‑source collaboration.
This article compares the advantages and disadvantages of cloud storage, on‑premises storage, and the hybrid cloud‑near storage model, explaining how each impacts scalability, cost, control, security, and integration for modern data platforms, and helps organizations select the most suitable solution.
This article examines the evolution of BI through generative AI, outlines the design and implementation of Baidu’s ChatBI platform, and discusses technical challenges such as NL2SQL integration, performance, accuracy, and user experience improvements that enable intelligent, low‑cost data analysis.
This article outlines the rapid growth of data in the smart automotive sector and details Changan's big data platform challenges—high cost, data accessibility, and operational complexity—and the practical migration from a Lambda to a unified Kappa architecture that delivers significant storage, compute, and maintenance efficiencies.
This article presents Shuxin Network's practical experience in building a cloud‑native, lakehouse‑integrated data platform for the financial sector, covering architecture evolution, challenges of domestic‑innovation (信创), the DataCyber solution, core components, deployment roadmap, and real‑world case studies.
This technical article details JD Retail’s 2023 data platform advancements, covering data‑asset certification and governance, a metric middle‑platform for unified indicator management, sophisticated visualization components, low‑code orchestration, and large‑model AI applications that together improve data retrieval efficiency, reduce storage‑compute costs, and support rapid business decision‑making.
The article describes NetEase Cloud Music’s public‑opinion analysis platform, which integrates external and internal data streams into a layered architecture—ingestion, processing, storage in Elasticsearch, visualization, and monitoring—and employs GPT‑based analyzers for clustering, sentiment, summarization, and intelligent alerts while optimizing costs and planning automated GPT‑driven reports.
This talk by Ant Group’s senior technical expert Wang Gaohang details the definition, design, mechanism, productization, and future outlook of the company’s metric system, covering concept consensus, semantic layers, workflow, AI assistance, performance optimization, and practical case studies.
iQIYI's Magic Mirror platform, evolving from 1.0 to 3.0, addresses the growing data analysis demands of the internet industry by empowering self‑service analytics, introducing multi‑stage architectures, advanced computation engines, customizable SQL, and visual dashboards, thereby improving efficiency, scalability, and data security for business users.
This article presents Didi's practical experience with StarRocks materialized views, covering the evolution of its OLAP architecture, the challenges of previous engines, the adoption of StarRocks, the design of materialized view acceleration for real‑time dashboards, and future optimization directions.
This article details Xiaomi's approach to isolating production environments for data development, covering platform evolution, security and quality challenges, physical versus logical isolation techniques, productization steps, implementation roadmap, business impact, and practical Q&A insights.
This article explains how YHP enables cloud‑native systems to achieve comprehensive observability by defining the three core signals—metrics, traces, and logs—addressing common enterprise pain points, and presenting an integrated platform that unifies data collection, storage, analysis, and visualization for efficient fault diagnosis and performance monitoring.
The article describes how GF Securities designed and implemented a unified big‑data empowerment layer based on Apache Kyuubi to address data‑centric challenges, improve efficiency, ensure controllable governance, and support agile data scenarios across ingestion, processing, storage, and security.
This article introduces Yunqi's self‑developed Lakehouse product, explaining its cloud‑native, one‑stop data platform architecture, incremental computing that balances freshness, performance and cost, and the autoMV feature that automatically creates materialized views to boost query speed up to nine times.
This presentation details YY Live’s data product team’s end‑to‑end business metric governance practice, covering problem background, analysis, governance objectives, multi‑team collaboration, implementation steps, achieved efficiencies, and future directions leveraging large language models.
Xiaohongshu’s OLAP team migrated from Presto to StarRocks, doubling cluster count to 30, boosting query speed by 6‑7 times, cutting latency to 200 ms, and achieving up to 35% cost reduction through gray‑scale migration and AWS Spot‑based elastic scaling.
Guming’s Front‑End Data Center integrates monitoring, performance, logging, and analytics for web, mini‑programs, Flutter clients, and Node.js services, offering real‑time alerts, high availability, sampling, multi‑channel data pipelines, custom charting, and detailed CPU/GC profiling to streamline issue diagnosis and business insights.
Facing growing task volumes and diverse workload types, we upgraded our data development platform's scheduling engine to Apache DolphinScheduler, detailing the migration process, architectural enhancements, stability and observability improvements, multi‑tenant support, and the resulting performance gains and future roadmap.
Senior Tencent product manager He Zhichao shares his experience and methodology for creating a high‑quality data platform, covering the transition from technical roles to product, understanding data users’ needs, the Euler asset‑factory implementation, product‑manager best practices, and solutions to common data‑engineering challenges.
Panasonic's digital transformation case study details the challenges of fragmented data across 64 subsidiaries, the strategic adoption of a serverless big‑data platform, governance milestones from 2021 to 2023, tool comparisons, standardization efforts, talent development, and future outlook driven by five core values.
This article details the architecture, core processes, and module designs of the JiShi BI platform, a self‑built data visualization and analysis system that integrates data ingestion, processing, enhanced AI‑driven analytics, and multi‑dimensional dashboard capabilities to support enterprise decision‑making.
This article details the background, challenges, design principles, and implementation of a unified data service platform—including data modeling, multi-source governance, real-time processing, and a Doris-based storage solution—to support large‑scale video data for a new media operation.
This article presents a comprehensive overview of Xiaomi’s data production platform, detailing the full data lifecycle, the technical‑driven product design methodology, the platform’s architecture and core capabilities, as well as real‑world case studies and a Q&A session that illustrate how the system improves data collection, storage, processing, and usage across the organization.
The Honghu data platform delivers a unified, UI‑driven environment that uses read‑time modeling to dynamically structure massive heterogeneous logs during queries, replacing pre‑defined schemas and ETL pipelines with rule‑based field extraction (regex, JSON, key‑value, IP) bound to data‑source types, trading CPU cycles for flexible, accurate analysis.
This article details Bilibili's data quality assurance framework, covering its evolution across four data platform stages, the architecture of its quality data warehouse, core capabilities such as a complete assurance system, digital‑driven continuous optimization, and efficient incident handling, plus case studies, future plans, and a Q&A session.
This article outlines Bilibili's data quality assurance framework, detailing its evolution across four development stages, the current data platform architecture, identified pain points, four key quality objectives, core capabilities such as a quality data warehouse, comprehensive monitoring, digital optimization, fault handling, and future directions.
This article details JD's three‑stage evolution of its data service platform, explains thematic (topic‑based) data services, introduces the One‑Service unified architecture, and outlines future plans for standardization, low‑code front‑end, and operational improvements.
This article details how JDV, JD.com’s internal visual dashboard platform, tackled the massive data‑intensive 618 promotion by implementing real‑time updates, cross‑midnight count stops, request‑state control, heartbeat monitoring, proxy data sources, and a suite of developer tools to ensure stability, performance, and rapid feature delivery.
This article explains why traditional centralized data lakes hinder modern software development, introduces the data‑mesh concept as a decentralized alternative, and walks through an e‑commerce microservice example with concrete steps, data‑API designs, and migration tactics to transition from a monolithic lake to a distributed data mesh.
iQIYI’s data‑middle‑platform team built a four‑zone data lake—raw, product, work, and sensitive—integrated with unified ODS/DWD/MID layers, a metadata catalog, and self‑service tools, leveraging HDFS, Hive/Iceberg, Spark/Trino, and Flink, migrated to Apache Iceberg for real‑time freshness, and now aims to further streamline modules and adopt new technologies.
This article explains what objects and tags are, distinguishes physical, network and electronic tags, outlines how to construct and manage a comprehensive tag taxonomy for user profiling, product labeling, and data platforms, and details quality assessment criteria for tags in DMP, CDP, and recommendation systems.
New Oriental’s recent DevOps transformation details how the company tackled siloed platforms, built a unified service‑tree‑driven infrastructure, created a real‑time data processing platform, and implemented comprehensive security measures—including red‑blue exercises, penetration testing, sensitive data monitoring, and CA/KMS—to boost efficiency and reduce costs.
The article summarizes ByteDance's DataOps journey, detailing its mid‑platform tool and Data BP model, core performance metrics, quality, hardware and human efficiency challenges, concrete DataOps implementation, productization through DataLeap, best‑practice promotion, and future outlook for data‑driven business value.
This article presents Kuaishou's comprehensive data service system, covering its domain modeling, evolution from custom to unified services, the Octo query engine and data preparation platform architecture, the dual data API and analysis services, and future plans for intelligence and serverless high‑performance capabilities.
This article outlines a comprehensive methodology for delivering a data governance project, covering planning, blueprint design, implementation, and acceptance phases, with detailed guidance on team formation, stakeholder roles, requirement analysis, platform architecture, management processes, and post‑deployment operations.
The article details Kuaishou’s KwaiBI business intelligence platform evolution, covering its 1.0 tool‑based implementation, the 2.0 standardized architecture built on an indicator middle‑platform, core processes, data integration, self‑service features, and future directions for self‑service and intelligent analytics.
The article shares Xiaomi’s practical experience in building and productizing intelligent analytics, explaining why it is needed, how it integrates with BI, the essential prerequisites, staged implementation, technical challenges, and future roadmap including smart alerts, automated insights, and data Q&A.
Didi’s data serviceization converts raw business data into consumable services through a four‑stage pipeline—integration, development, production, and back‑flow—while the Data Dream Factory and Shu‑Chain platform automate synchronization, provide a unified access gateway for thousands of APIs, and introduce a standard metric service that abstracts storage complexities and ensures high‑performance, secure data delivery.
This article presents a comprehensive case study of Baicaowei's data platform evolution, digital workflow, metric governance, business modeling, and BI insights, illustrating how big‑data technologies and rational architecture simplification empower the fast‑moving consumer goods sector to enhance operational efficiency and decision‑making.
This article presents the background, technical architecture, feature set, workflow, and practical results of OPPO's big data diagnostic platform, illustrating how intelligent, non‑intrusive task analysis improves efficiency, stability, and cost across massive offline and real‑time workloads.
The article chronicles Netflix’s evolution from a failing batch pipeline to a cloud‑native, self‑service streaming platform, detailing four development phases, the technical challenges faced, the stream‑processing patterns introduced, key learnings, and future opportunities for real‑time data and machine‑learning workloads.
The article argues that traditional centralized data warehouses and lakes are increasingly unsustainable for large enterprises and proposes a paradigm shift to a distributed data mesh architecture that emphasizes domain‑owned data products, discoverability, interoperability, and global governance to overcome inherent inefficiencies.
The team built an online traffic‑replay platform that captures real user requests, replays them in a synchronized pre‑release environment, automatically compares responses using AAdiff and field‑ignore rules, achieving 86% interface coverage, 30% fewer regression bugs, 98% replay success and halving manual testing effort, while providing a zero‑intrusion, high‑concurrency solution for ongoing smoke, regression, stress and cache validation.
This article details iQIYI's implementation of Apache Kyuubi for Spark Thrift Server, covering the evolution from native Spark Thrift to Kyuubi 0.7 and 1.x, multi‑tenant architecture, tag‑based configurations, SQL auditing, lineage collection, service monitoring, small‑file and Z‑order optimizations, and a brief Q&A.
This article outlines the background, objectives, and implementation framework of AIOps at a major bank, detailing data consolidation, analysis engines, scenario ecosystems, practical case studies, and future directions for intelligent, proactive IT operations.
This article presents NetEase's practical experience of constructing a big‑data security center using Apache Ranger, covering Ranger's core features, a comprehensive security solution, detailed technical analyses, and the outcomes of commercializing the platform across multiple enterprise environments.
Comcast’s hybrid‑cloud data platform, built on Trino and Amazon S3, faced challenges such as fragmented data access, costly data copies, and latency, leading the DX team to adopt Alluxio as a unified, cache‑enabled, secure middle‑layer that bridges storage and compute.
The article recounts NetEase’s presentation at the inaugural DataOps conference, detailing the evolution from DataOps 1.0 pipeline to a 2.0 integrated data development‑governance model, the challenges faced, practical solutions, and strategic advice for data managers.
The article summarizes Microsoft Build 2023 highlights, detailing Azure OpenAI Service's GA and new plugins, enterprise data integration via vector search, Microsoft Fabric's OneLake data lake, Windows Copilot features, and the Copilot Stack architecture that enables developers to build AI‑powered Copilot applications.
This article presents an in‑depth overview of the YouShu BI product, outlines the high‑concurrency performance challenges faced by enterprise BI, and details the multi‑layer performance architecture—including front‑end, back‑end, data engine, and data source layers—along with smart caching, MPP acceleration, materialized views, and the Data Doctor operations that together ensure low‑latency, reliable analytics for large‑scale users.
This article details how Wing Pay built a comprehensive data development and governance platform, covering company background, business scenarios, goals, challenges, task development workflow, task types, SparkSQL editor features, double‑environment deployment, Airflow scheduling, DataX data bus, resource isolation, compute optimization, data quality monitoring, cloud‑native practices, future outlook, and a Q&A on data permissions and governance.
Bilibili’s data platform upgraded its dependency model by shifting from project‑level to task‑level dependencies, adding root and end nodes, using virtual tasks for external data, introducing offset handling, implementing an abstract DependencySubject and asynchronous callbacks, achieving sub‑second latency for tens of thousands of daily tasks while planning automated lineage and richer rule support.
This article examines two decades of data warehouse evolution in the financial sector, identifies persistent pain points such as platform lag, data quality, and low service efficiency, and proposes a cloud‑native, data‑centric framework—including a unified blueprint, three‑layer architecture, and six core capabilities—to accelerate enterprise‑wide data capability building and drive high‑quality digital growth.
The article explains the concept, benefits, and detailed modular architecture of a data middle platform, covering data storage, acquisition, processing, governance, security, and operation frameworks, and illustrates how enterprises can build and evolve such platforms to turn data into valuable services.
The article details Zhongyuan Bank's end‑to‑end agile BI platform construction, covering business goals, a step‑by‑step development timeline, core architecture, eight key functionalities, low‑code data processing, real‑time streaming, visualization dashboards, intelligent Q&A, and future directions for platform intelligence and openness.
This article details Zhihu's Data Management Platform (DMP), covering the business problems it solves, the end‑to‑end workflow, feature taxonomy, system architecture, data pipelines for batch and streaming, audience targeting processes, performance challenges, and future technical directions.
This article outlines the motivations for building a comprehensive data platform, describes the measurement and tailoring approach, details WeDataSphere’s architecture—including DataSphere Studio and Apache Linkis middleware—and shares the open‑source roadmap and future vision for the platform.
This article traces 20 years of big‑data evolution, compares data lakes and data warehouses, defines both concepts, examines their technical trade‑offs, and presents Alibaba Cloud’s lake‑warehouse (lakehouse) solution that unifies flexible storage with enterprise‑grade performance and governance.
The article examines the opportunities and challenges of turning data platforms into SaaS solutions, compares sales‑driven and product‑driven models, analyzes cost factors and industry gaps, and shares practical approaches such as platform‑plus‑component architecture, real‑world case studies, and product‑management considerations for better meeting B2B customization demands.
The article explains how market trends, data‑driven enterprise needs, and internal platform experience led Didi to build Know Streaming—a zero‑intrusion, plugin‑based real‑time streaming solution built on Kafka—to address scalability, operability, and community adoption challenges.
This article provides a detailed overview of data middle platform concepts, describing a decoupled six‑subsystem architecture—including storage, collection, processing, governance, security, and operation frameworks—while illustrating typical enterprise implementations, industry‑specific solutions, and best‑practice considerations for building scalable, secure, and value‑driven data platforms.
This article presents an in‑depth overview of DataLeap's data lineage capabilities, covering the challenges, multi‑layer model design, implementation with Apache Atlas and JanusGraph, performance optimizations, diverse use cases across asset, development, governance and security domains, and future trends for lineage technology.
This article details Xinghuan Technology’s evolution from 2013 to the present, describing its self‑developed Big Data 3.0 stack—including a unified data platform, SQL‑centric development, cloud‑native resource scheduling, distributed storage managed by Raft, DAG‑based compute engines, and real‑time stream processing—while highlighting key milestones and design principles that differentiate it from traditional Hadoop‑based solutions.
Alibaba’s DataWorks team shares extensive experiences in building and operating a large‑scale data platform, covering data governance across stages—from data stability and quality to security, cost control, and organizational culture—illustrating how systematic practices and tools drive efficiency, reliability, and value for enterprises.
This article details Cloud Music's year‑long data assetization journey, covering the background, practical achievements, governance methods, and future roadmap for turning massive data warehouses into high‑value, well‑governed assets that drive cost reduction and business insight.
This article introduces OAX (Open Analysis eXpressions), Kuaishou’s unified open‑analysis language, detailing its design background, guiding principles, five‑layer language model, syntax—including data types, compute capabilities and five analysis elements—its access protocol, runtime architecture, optimization steps, and the benefits it brings to the company’s big‑data analytics ecosystem.
This article outlines Tencent's data governance challenges, its internal three‑stage practice, detailed case studies such as Tencent News and PCG cost governance, and introduces the WeData platform's architecture and tools for standardization, quality, security, and metadata management, concluding with a Q&A session.
This article details Kuaishou's one‑stop big data development platform, covering its massive scale, low‑code and real‑time capabilities, multi‑layer architecture, SLA guarantees, diagnostic tools, and future plans to further lower development barriers and democratize data engineering.
This article shares practical insights on constructing an O2O industry data platform, detailing user classification, business pain points, and a three‑step strategy—monitoring, analysis, and diagnosis—to extract core metrics, implement tailored reporting, conduct operational and pricing analyses, and drive data‑driven product improvements.
Lean Data Methodology combines lean thinking, design thinking, the Cynefin framework, and agile principles to create a data‑driven digital transformation system that defines value, eliminates waste, and equips enterprises with strategic, product, governance, collaboration, platform, and cultural capabilities for building lean digital enterprises.
This article explains how companies can transform data assets into operational power and market revenue by establishing data sharing platforms, leveraging analysis tools, building data products, and ensuring security and compliance throughout the data lifecycle.
This article explains how companies can transform collected data into valuable assets by addressing data assetization challenges, building comprehensive data platform functions, and developing team capabilities across governance, quality, security, and operational domains.
This article explores why the automotive industry's shift to new energy vehicles necessitates a redefinition of the Customer Data Platform (CDP), detailing the changing traffic structure, varied departmental demands, CDP typologies, implementation strategies, and the benefits of a unified, extensible CDP architecture for marketing, sales, and after‑sales.
This article explains how enterprises can build a unified data storage and compute foundation, covering strategic goals, functional and architectural requirements, and the layered design of business support, storage‑compute, and resource management to enable scalable, secure, and high‑performance data platforms.
This article presents the architecture and practical experience of ZhongAn's hundred‑billion‑scale data integration service, covering common integration technologies, business support scenarios for offline and real‑time data, technical challenges, evolution from single‑machine to service‑oriented designs, and future directions using Flink and DataX.
This comprehensive guide explains the evolution from traditional data warehouses to modern data lakes, detailing concepts, architectures, differences, implementation steps, and real‑world case studies, while also comparing major cloud providers' solutions and highlighting how data platforms support digital transformation and analytics.
The interview gathers insights from data‑platform experts on the maturity stages, technology trends, implementation methodologies, open‑source ecosystems, system architectures, governance, security, and assessment criteria of modern data middle platforms, offering a comprehensive guide for practitioners.
This article outlines the design and implementation of a unified data service platform that standardizes data access, accelerates model processing, provides flexible API construction, and ensures high availability through gateway, caching, and monitoring, ultimately reducing cost and improving efficiency for both C‑end and B‑end applications.
This article reviews the history of data platforms—from the first general‑purpose computers and early relational databases through traditional BI, agile BI, and big‑data technologies like Hadoop, Spark, and Flink, up to today’s cloud‑native modern data stack and its future outlook.
This article outlines the essential requirements, architectural design, and five key capabilities of an enterprise‑level unified data platform, explaining how data integration, cloud‑native features, and layered design enable digital transformation and business innovation.
The Vivo Real‑Time Computing Platform, built on Apache Flink, delivers a one‑stop data construction and governance solution that processes up to 5 PB daily, offering high‑availability submission and control services, robust stability, rich SQL usability, efficient Kubernetes deployment, strong security, and supports real‑time warehouses and short‑video recommendation, while targeting future elastic scaling and lake‑house unification.
This article presents a comprehensive overview of a data service middle platform, detailing its background, architectural design, data construction, model definition and acceleration, API creation, query processing, service gateway, common solutions for standardization and cost reduction, as well as achieved results and future plans.
The article details ByteDance’s DevMind platform, describing its origins, the challenges of measuring software development efficiency, the collaborative and value‑driving “flywheel” concepts, the architectural design across data lifecycle and query engine layers, and the principles and future roadmap for scaling R&D performance.
This article presents Douyu Live's experience in building a data middle platform, outlining the challenges of data application, the four‑stage evolution of their data tools, current achievements, and future goals to empower every employee as a data analyst.
This article presents an in‑depth overview of 58.com’s self‑built Star River big data platform, covering its evolution across three eras, resource management hierarchy, core technical capabilities such as metadata services, data maps and lineage, governance practices, and the roadmap for further enhancements.
This presentation details how Guanyuan Data leverages Apache Spark within its self‑service analytics platform, covering product features, flexible deployment, resource isolation, performance challenges, architectural solutions, and future cloud‑native enhancements to support thousands of users and massive query workloads.
This article explains how Aspect‑Oriented Programming (AOP) was introduced into a data construction platform to address high development effort, strong business coupling, and maintenance difficulty by isolating cross‑cutting concerns such as logging, thereby improving modularity, development speed, and code maintainability.
This article details NetEase DataFan's journey in building a full‑stack big‑data platform, explains the design‑first data‑mid‑platform approach, analyzes cost, quality, and security problems encountered, and presents the modern data‑governance framework that integrates development, governance, and consumption into a closed loop.
The article details ZTO Express's data governance journey, covering company background, drivers and goals, challenges such as data asset inventory, standardization, quality, and modeling, and outlines their multi‑layered governance framework, practical implementations in data quality, model and metadata, and future plans.
This article presents an overview of a smart city unified perception platform, detailing its modular architecture, solutions for multi-source heterogeneity, incremental synchronization strategies, and real-time API data collection, while discussing extensibility and practical implementation considerations.
This article details the architecture, evolution, and technical solutions of the Berserker big‑data platform—including component design, state‑management problems, release strategies, two‑phase commit, RPC handling, routing, message queuing, containerized execution, dependency model redesign, and future roadmap—demonstrating how the system achieved high availability, low latency, and scalable operations.
This article details Huolala's journey from identifying the need for a fast, secure, and scalable BI solution to designing and implementing a self‑service data analysis platform that integrates diverse data sources, offers intuitive visualisation, and addresses real‑world operational challenges.
This article details Xiaomi's multi‑year journey in building a group‑wide Business Intelligence platform, covering its historical evolution, technical challenges in performance, modeling, visualization and permissions, the current four‑layer architecture, and future plans to make the platform more business‑centric and simpler.
The article describes how TCL leveraged StarRocks to create a unified data analytics platform, detailing the company’s background, OLAP evolution, typical StarRocks use cases such as real‑time dashboards, HR analytics, and email alerts, and outlines future plans for further integration and performance improvements.
This article outlines NetEase DataFun's journey in building a full‑stack big data platform, describing the four‑stage development of data governance—from designing a unified data middle‑platform to addressing cost, quality, and security challenges—and presents the principles of modern data governance that integrate development, consumption, and continuous improvement.
