This article explains how dbt drives automated data modeling and governance, how DataOps practices bring agility and control to data projects, and how StarRocks’ lakehouse architecture enables real‑time and batch analytics, illustrated with concrete workflows, version‑control conventions, and enterprise case studies.
The article walks through the evolution of data management—from idealistic visions and messy “shit mountains” to the realities of data lakes, metadata layers, governance challenges, trust breakdowns, and finally the promise of DataOps as a hopeful path forward.
This article examines the challenges of traditional banking data, explains how large language models can fuse structured and unstructured information, outlines a new data‑centric infrastructure and governance approach, and describes the DiFY platform’s AI‑agent and DataOps capabilities for agile, non‑intrusive integration with core banking systems.
The article details Xiaohongshu’s Dataverse platform evolution into a Data+AI system, highlighting inefficiencies in algorithm and data‑science workflows, the introduction of an interactive notebook, comprehensive data lineage, AI‑coding assistance, and future DataAgent plans to automate data engineering tasks.
Alibaba Cloud’s Serverless Elasticsearch service, combined with the SREWorks data‑driven operations platform, offers a cloud‑native, real‑time search and analytics engine that integrates metric and log collection, cost management, and health monitoring to enhance scalability, performance, and operational efficiency for enterprise applications.
The article details how Alibaba’s Tesla SRE platform supports the massive offline and real‑time big‑data ecosystems through a layered, data‑driven operations framework—DataOps—integrating unified portals, configuration, job, workflow, and analytics platforms, enabling automated monitoring, intelligent decision‑making, and self‑healing capabilities across 100,000+ nodes.
Alibaba's SREWorks platform, now open‑source, combines cloud‑native architecture, DataOps and AIOps to address the growing complexity of big‑data and AI operations, offering a layered SaaS/PaaS/IaaS solution that streamlines delivery, monitoring, management, control, operation, and service for modern enterprises.
This article explains how to transform traditional SRE troubleshooting into a data‑driven process by pre‑collecting operational metrics into a data warehouse, using ETL to create layered data models (ODS, DIM, DWD, DWS) that enable efficient, repeatable analysis while balancing data freshness and storage costs.
Tracing Alibaba’s journey from manual, script‑based operations through tool‑centric and platform‑driven DevOps to the data‑focused DataOps era and emerging AIOps, the article outlines the shifting responsibilities, architectural challenges, and the multidisciplinary skill set required for modern operations engineers.
The article explains how DataOps and SRE practices enable large‑scale, data‑driven operations in big‑data environments, aiming for fully automated, intelligent, and ultimately unmanned management of complex systems.
This article examines how Hainan Shuzhao Technology leverages ChatGPT‑4 and other large language models to enhance DataOps, address traditional data management challenges, improve Text2SQL accuracy, and outline future directions for agile, AI‑driven data pipelines.
This article presents Grab's Hugo platform, an automated fault‑detection and self‑healing system for over 4,000 data pipelines that combines multi‑source signal collection, intelligent diagnosis, layered auto‑repair, and a health API to dramatically improve data visibility, reduce manual intervention, and boost operational efficiency across the company.
This article explains the data resourceization stage, outlines seven core data management functions, introduces the DataOps paradigm to overcome traditional inefficiencies, and showcases real‑world case studies and a panoramic data architecture view for modern enterprises.
This report outlines a comprehensive framework for turning raw data into valuable assets, introducing DataOps and panoramic data architecture, and detailing practical methods for data value assessment, asset circulation, and operational mechanisms to help enterprises build a solid value baseline and expand data asset applications.
The article outlines how to obtain the DataFun Summit e‑book by following the public account instructions and provides concise English summaries of twelve technical sessions covering data lineage, integration, AI language models, multimodal content, game AI agents, lake‑warehouse governance, big‑data architecture, and cluster management.
This article presents a comprehensive overview of how DataOps principles combined with large language models such as GPT‑4 enable more agile and intelligent data engineering workflows, focusing on Text2SQL applications, schema‑linking techniques, practical product implementations, and future research challenges.
The article outlines how data has become a critical production factor, reviews national policies, industry and enterprise challenges, and presents six key trends—including DataOps, DataFabric, and AI‑focused governance—that shape the future of data management and security.
The article examines the evolution of data integration architectures—from traditional ETL through ELT to the emerging EtLT model—highlighting their advantages, disadvantages, industry trends, maturity stages, and practical guidance for enterprises and professionals navigating modern big‑data pipelines.
This article explains how metadata‑driven data governance addresses the challenges of the digital economy by detailing the era background, limitations of traditional methods, the roles of Data Fabric, Data Mesh and DataOps, and presenting real‑world case studies and future directions.
This article explains how agile methodologies can be applied to data management, covering the need for agile data practices, core principles, iterative modeling, governance, CI/CD pipelines, tooling, metrics, security, case studies, challenges, and future outlooks in a comprehensive, step‑by‑step guide.
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.
This article presents NetEase Data's end‑to‑end experience in building a metrics middle platform, covering product evolution, the motivations for a unified metrics layer, core technologies such as a logical semantic model, a custom metric query language, engine‑agnostic execution, and future roadmap plans.
This article examines the challenges of big‑data operations, explains how DataOps and AIOps complement each other, and details the ABM intelligent operations architecture, platform components, and real‑world use cases such as Flink hotspot detection, ChatOps assistants, and dynamic MaxCompute resource optimization.
In this presentation, Guo Wei, CEO of Baijiang Open Source and seasoned big‑data expert, explores how large‑model AI reshapes DataOps and DataFabric, detailing efficiency gains, intelligent deployment, and future enterprise architectures for big‑data and AI integration.
The article introduces Tencent's OLA data‑governance platform and its t‑Metric metric middle‑platform, explains the Headless BI concept, details the configuration‑driven metric production workflow, core capabilities, architecture, unified query service, ecosystem integration, and answers audience questions about real‑time analysis, dimension handling, and trust mechanisms.
This article reveals how Baidu implements end‑to‑end DataOps for its commercial data products, covering challenges of massive report generation, the design of a layered data architecture, platform‑wide automation, serverless deployment, risk control, monitoring, and optimization to achieve scalable, reliable data pipelines.
Bilibili's SQLScan is a static‑code analysis tool that parses Hive, Spark, Presto and Flink SQL via Antlr4, builds a unified AST, applies engine‑specific metadata plugins for rule enforcement, provides field‑lineage and cost‑analysis services, and has processed hundreds of thousands of daily queries, intercepting thousands of problematic statements to improve data quality and operational efficiency.
This article introduces DataOps, outlines its background and the problems it addresses, describes NetEase’s big‑data product ecosystem, and details a six‑stage data production pipeline—including coding, orchestration, testing, code review, release approval, and deployment – plus insights into two pipeline explorations.
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 explains the concept of Data Fabric (data weaving), its background, definition, relationship with data middle platforms, data governance and DataOps, compares it with related concepts, and outlines future development directions and challenges.
This article explores the challenges of data quality in mature big‑data environments and presents intelligent, automated approaches—including assertions, automatic detection, rule recommendation, link checking, and collaborative mechanisms—to embed quality checks throughout the data pipeline, improving efficiency and reliability.
At the 2023 Big Data Industry Development Conference in Beijing, the China Communications Standards Association unveiled the top ten big‑data keywords, highlighting trends such as lake‑warehouse integration, data assetization, DataOps, intelligent analytics, data ethics, security, public data licensing, and cross‑border data flows.
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.
In this interview, three data‑governance experts from Tencent, ByteDance, and Alibaba discuss the fundamental processes, core domains such as metadata, data lineage, metric systems, data quality and security, the main challenges they face, and emerging trends like DataOps, AI‑driven automation, and privacy‑preserving technologies.
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.
DataOps, rooted in agile and DevOps philosophies, uses automation and collaborative practices to streamline data processing, improve quality, and align analytics with business goals, offering continuous analytics, faster insights, and breaking data silos for better decision‑making across organizations.
This article traces the evolution of the modern data stack, explaining the shortcomings of traditional data warehouses, the rise of cloud‑native ELT, self‑service analytics, analytics‑as‑software, and enhanced decision intelligence, while highlighting emerging trends and practical implementations.
This article traces the origins and evolution of the modern data stack, explains the shortcomings of traditional data warehouses, describes how cloud‑native ELT, modular components and analytics‑as‑software enable self‑service BI and AI, and outlines emerging trends such as DataOps, enhanced analytics and decision intelligence.
Zhejiang Mobile leveraged DataOps principles to create a unified, micro‑service‑based anti‑fraud platform that integrates multi‑source telecom data, improves data governance and development efficiency, and achieved top‑level evaluation, showcasing a scalable solution for combating sophisticated telecom scams.
DataOps, the data‑centric counterpart of DevOps, combines agile principles, standardized tools, and cross‑team collaboration to manage the full data lifecycle—from integration and development to storage, governance, and service—enabling organizations to handle massive, diverse datasets efficiently, reduce silos, and turn data into actionable value.
This article examines the four‑stage evolution of data warehouses, highlights the cost‑effective, scalable advantages of cloud‑native warehouses, explores the rapid growth of data‑management infrastructure, and discusses the emerging practices of DataOps and AI integration that are redefining modern data stacks.
This article explores DataOps from its historical evolution through the digital 3.0 era, outlines its core technology stacks such as Data Fabric, Data Mesh, and Modern Data Stack, and demonstrates practical applications across finance, manufacturing, telecom, and public services, highlighting its role in agile, cloud‑native data management.
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 presents an in‑depth overview of Tencent's Oula data governance platform, describing its construction goals, core capabilities, DataOps‑driven development workflow, unified metric store, data map services, and practical Q&A on asset health scoring and data lineage, illustrating a comprehensive end‑to‑end big‑data governance solution.
Tencent's Oula platform, launched in 2019, provides a DataOps‑driven, end‑to‑end data governance solution covering data discovery, asset factory, metric platform, and governance engine, and the talk details its construction goals, data development governance, unified metric system, data map, and Q&A on asset health and lineage.
This article explains DataOps as a methodology for improving data analysis quality and efficiency, outlines its origins, standards, and maturity model, and presents practical insights and case studies from Chinese enterprises on how DataOps addresses common data engineering challenges and drives digital transformation.
The DataFun Summit 2022, held on September 17, gathered leading experts from Baiji Whale Open Source, NetEase, Tapdata, and Alibaba Cloud to share deep technical insights on SeaTunnel V2 architecture, DataOps implementations, and open‑source big‑data studio tools, offering attendees practical guidance for modern data platforms.
This guide explains how to create a robust GitHub Actions CI/CD workflow that automatically tests Apache Airflow DAGs using pytest, flake8, and Black, then securely deploys them to Amazon Managed Workflows for Apache Airflow (MWAA) with optional Git hooks and fork‑and‑pull collaboration models.
ICBC’s software development center outlines its multi‑year journey adopting XOps practices—DevOps, DevSecOps, DataOps, MLOps, AIOps, ChatOps and BizDevOps—to boost development efficiency, enhance security, accelerate data‑driven AI, and cut costs, showcasing measurable improvements in release frequency, defect rates, and operational automation.
This article explains DataOps and its role in modern ETL pipelines, outlines the benefits of DataOps for efficiency and reliability, and provides a detailed roadmap and best‑practice guidelines for planning, implementing, and optimizing DataOps‑driven ETL in cloud‑native environments.
Xinwang Bank leverages Apache DolphinScheduler to handle over 9,000 daily task instances across real‑time, near‑real‑time, and offline batch scenarios, detailing background, application scenarios, optimizations, workflow improvements, import/export enhancements, alert system upgrades, and future plans to expand data‑ops capabilities.
This article details how NetEase's DataFunTalk presentation explores the growing data quality challenges in data development and demonstrates the application of DataOps principles—including pre‑ and post‑control mechanisms, sandbox environments, and automated governance tools—to systematically reduce defects, optimize resources, and ensure reliable data delivery across the company's diverse business lines.
Vivo’s self‑built database and storage platform evolved from a fragmented 2018 system into a two‑layer DaaS solution offering unified management, automated security, high availability, and cost‑effective scaling across MySQL, Redis, TiDB, object storage, and future autonomous services within a hybrid‑cloud architecture.
The article surveys rapidly growing IT positions—from quantum computing engineers and security‑compliance managers to big‑data, analytics, and DataOps engineers—explaining how these roles combine advanced technologies, regulatory expertise, and operational practices to drive business transformation and meet the evolving demands of digital enterprises.
The article examines how data teams can select the appropriate Ops framework—DataOps, MLOps, or AIOps—by comparing their origins, principles, responsibilities, and tooling, and stresses that cultural principles outweigh technology choices for efficient delivery of data and machine‑learning products.
The article explains the origins and differences of DevOps, DataOps, MLOps and AIOps, outlines their shared seven principles, and provides practical guidance on adopting the right X‑Ops culture to accelerate data‑driven and machine‑learning‑powered software delivery.
DataOps, an Agile‑derived methodology that extends DevOps principles to data analytics, emphasizes automation, collaboration, and continuous delivery to accelerate and improve data processing, quality, and business insight, while outlining its benefits, relationship to Agile/DevOps, and practical steps for adoption.
The 2020 GOPS Global Operations Conference in Shenzhen showcased a series of high‑impact DevOps talks, standards releases, enterprise assessments, and collaborative initiatives that highlighted the evolving landscape of cloud, data, and operational excellence across leading Chinese tech organizations.
This talk walks through the evolution from manual to automated operations, outlines Alibaba’s integrated operations platform architecture, showcases practical DataOps implementations, and explores AIOps applications such as self‑healing monitoring, hardware remediation, and resource optimization, offering concrete examples and insights for modern ops engineers.
The article reviews Alibaba's operational evolution, tracing stages from manual and script‑based Ops through tool‑centric and platform‑centric DevOps, to data‑driven DataOps and AI‑enhanced AIOps, while highlighting the skill shifts and challenges faced by modern SRE teams.
This article explains how Alibaba's MaxCompute platform tackles capacity bottlenecks by combining data‑driven insights, linear programming, and automated project migration strategies to predict resource needs, optimize cluster allocation, and quantify migration impacts for improved operational efficiency.
