This article introduces Apache Hadoop’s open‑source framework, explains its core components such as HDFS, MapReduce, ZooKeeper, HBase, Hive, Pig, Mahout, Sqoop, Flume, Chukwa, Oozie, Ambari and YARN, and outlines the steps to set up a beginner‑level Hadoop cluster on CentOS 7.
This article compiles extensive interview questions and topics covering Hadoop, Spark, Flink, Hive, Kafka, MySQL, Redis, Java fundamentals, and algorithms, organized by companies such as Xiaomi, ByteDance, Alibaba, Shopee, Tencent, Meituan, NetEase, and Baidu, to help candidates prepare effectively for big‑data engineering roles.
The article examines Salesforce’s five‑hour global outage caused by a shortcut DNS deployment and the subsequent recovery challenges, then explores a viral experiment where twenty smartphones generated artificial traffic congestion, illustrating how real‑time data feeds and operational safeguards can prevent large‑scale service disruptions.
This article details how the logistics HR data preprocessing platform processes around 20 million daily records by adopting HBase for high‑performance, scalable, column‑oriented storage, covering its architecture, read/write mechanisms, best practices, and performance considerations.
This article details TAL's transition from offline to real-time data warehousing, describing business drivers, pain points, architectural evolution through Hive, Flink+Kudu, and DorisDB, and outlining the system design, data flow, scheduling, monitoring, and the resulting business and cost benefits.
Airbnb’s 2019 Data Quality Improvement Plan reorganized its data‑engineering workforce, introduced a dedicated data‑engineer role, adopted a decentralized Minerva‑based architecture with Spark pipelines, instituted rigorous testing, governance, and certification processes, and established SLAs and monitoring to ensure timely, trustworthy, well‑documented data across the enterprise.
This article reviews the Pravega project and its Flink connector, covering Pravega's design for large‑scale streaming, the connector's evolution and exact‑once semantics, Flink 1.11 integration challenges, checkpoint mechanisms, and future plans such as schema‑registry and new Flink features.
This article describes how Shuhai Supply Chain upgraded its data warehouse from a complex, high‑cost 1.0 architecture to a streamlined, real‑time solution built around Apache Doris, detailing the motivations, design choices, zero‑code ingestion, metadata management, Flink connector, and the resulting performance gains.
This article introduces the most important and still mainstream components of the big data ecosystem—including Hadoop’s storage and compute framework, Hive data warehouse, HBase NoSQL database, Spark unified engine, Kafka messaging platform, and Elasticsearch search engine—explaining their core concepts, architectures, and typical use cases.
This article describes how SmartNews integrated Flink into its Airflow‑driven Hive batch pipeline to cut the actions table generation latency from three hours to about thirty‑four minutes, detailing the technical challenges, design decisions, and production results.
This article explains Apache Kudu’s architecture, schema design, update mechanism, and how NetEase leverages it for real‑time data ingestion, dimension table joins, data‑warehouse ETL, and AB‑testing, while also discussing encountered issues and upcoming feature requests.
The article details the background, architecture, core features, scheduling mechanisms, Lisp‑based query DSL, and Alluxio integration of Vipshop's self‑developed Hera data service, illustrating how it unifies multi‑engine data access, improves SLA, and accelerates large‑scale crowd computing tasks.
This article explores how a clear enterprise data strategy—distinguishing strategic goals from tactical steps, emphasizing clean and governed data, and integrating analytics with business missions—drives reliable outcomes and supports the experience economy through coordinated CXM platforms and data products.
At Youzan, data governance evolves from massive data assets to AI readiness through systematic data assetization, quantitative quality scoring, cost measurement, and targeted operational tactics, enabling precise quality monitoring, cost allocation, and continuous improvement that drive both data value and cost efficiency.
The article explains Bloom filters—a probabilistic, space‑efficient data structure using multiple hash functions on a bit array to answer set‑membership queries with controllable false‑positive rates, detailing their construction, query process, optimal parameters, and common uses such as URL deduplication, cache protection, and spam filtering.
The article explains the concept of a data middle platform, detailing its three-layer structure—data model, data service, and data development—illustrates how data modeling enables cross-domain integration, how services encapsulate data for flexible consumption, and how development tools support customized data applications, using a telecom operator example.
In this interview, Huawei Cloud MVP Wang Ming shares how a non‑computer‑science background led him to a successful IT career, discusses the advantages of interdisciplinary skills, offers entrepreneurship advice, predicts future tech trends, and explains the key concepts of his popular Go concurrency book.
The Xianyu “Play” tab flow‑control system combines task‑specific and rule‑based strategies with a dynamic strategy‑, control‑, and distribution‑chain architecture that integrates real‑time data processing into the recommendation engine, delivering guaranteed exposure, boosting daily posts by 14.4 % and paving the way for multi‑objective, zero‑code control.
This article examines common causes of slow MySQL queries, explains index mechanics and failures, then compares ElasticSearch’s fast tokenized search and HBase’s column‑oriented storage, offering practical guidance on when and how to use each technology.
This article provides a clear, visual guide to Kafka’s core concepts—including producers, consumers, topics, partitions, consumer groups, message ordering, and the underlying ZooKeeper‑managed cluster architecture—helping readers grasp how Kafka enables reliable, scalable stream processing.
The article explains edge computing as a decentralized computing model that addresses high‑reliability, low‑latency demands, data‑center energy consumption, big‑data processing pressure, low resource utilization, intelligent front‑ends, and security‑privacy concerns, and it outlines key research areas and related paradigms such as fog, mobile edge, sea, and intelligent edge computing.
To meet Xianyu’s billion‑event‑per‑day real‑time analysis needs, the team built a petabyte‑scale warehouse using Hologres for storage and Alibaba‑enhanced Flink (Blink) for streaming, organized into ODS, DWD, DWS, ADS and DIM layers, enabling minute‑level aggregations, rapid anomaly detection, and instant product‑team insights.
This article explains how Beike adopted the Druid OLAP engine to handle massive real‑time and offline query workloads, detailing its four‑component architecture, key technologies such as deep storage and metadata storage, practical optimizations for data ingestion, query caching, dynamic throttling, timeout control, and a roadmap for future enhancements.
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 walks through implementing a lightweight single‑machine MapReduce framework inspired by the original MapReduce paper, covering the abstract Map/Reduce model, task scheduling between master and workers, core Go code for map, reduce, worker, and coordinator, and a brief reflection on its limitations.
This article explains the growing analytical demands in the big‑data era, the limitations of traditional OLAP, and how Kyligence’s distributed OLAP engine addresses data‑island issues, multi‑dimensional and many‑to‑many analysis, unified security, and performance optimization with MDX on Spark, delivering a seamless Excel‑like experience.
This article explains Didi's end‑to‑end architecture for ingesting MySQL data into Hive using real‑time Binlog collection, a customized Canal component, message queues, HDFS storage, Dquality monitoring, and strategies for handling data drift and sharding in large‑scale big‑data environments.
This article details JD Logistics’ challenges in handling petabyte‑level data, outlines their existing data architecture, and explains how they adopted Apache Doris for faster, scalable analytics, covering table management, data import workflows, visualization tools, and future roadmap for data engineering.
This article walks through Flink's metric system by explaining the core interfaces such as MetricReporter and MetricRegistry, showing how metrics are added, registered, and queried during TaskManager startup, and detailing both REST and Prometheus approaches for retrieving metric values.
This article details the design and multi‑stage optimization of an A/B testing metric system, describing its product architecture, Spark‑based computation engine, ClickHouse OLAP layer, cumulative calculation improvements, and batch processing techniques that reduced processing time from hours to a few minutes for hundreds of experiments and metrics.
This article presents a comprehensive case study of Meituan's hotel‑travel data governance, covering the background, challenges, strategic goals, standardized processes, technical systems, cost and security optimizations, measurable outcomes, and future plans for automated governance.
This article explains what data governance is, why it is needed, its objectives, core components, implementation methodology, required tools, and real‑world practices from Meituan Delivery and Ant Financial, illustrating how organized data management drives business value and risk control.
Youzan Data Report Center monitors offline batch and real‑time data pipelines using accuracy and timeliness rules, cross‑table checks, upstream‑downstream comparisons, and scheduled alerts to detect anomalies early; since 2021 it has generated over 25 alerts, and plans a unified data‑quality dashboard.
This article explains how data has become a critical production resource, outlines the limitations of traditional data‑analysis workflows, defines data products and their components, describes their advantages and key characteristics, and shares practical case studies of data‑product implementations in a large e‑commerce environment.
Zhang Liang shares Didi's four-stage journey of constructing and governing large‑scale open‑source big‑data engine services—including engine selection, hardware sizing, PaaS platform building, proxy architecture, and governance—highlighting practical challenges, solutions, and ROI‑driven best practices for Kafka, Elasticsearch, Flink, and related technologies.
This article presents an extensive glossary of more than fifty big‑data concepts—including Apache projects, data‑analysis methods, storage formats, AI‑related terms, and emerging metrics—providing concise English explanations for each term.
Apache Kudu 1.15.0 adds experimental multi‑row transaction support (currently INSERT and INSERT_IGNORE), Raft‑based master configuration tools, table comment synchronization with Hive Metastore, per‑table size and row‑count limits configurable via flags or the kudu table set_limit tool, a customizable Kerberos principal flag, and TLS v1.3 with optional cipher‑suite selection, collectively enhancing low‑latency random access and analytical capabilities in the Hadoop ecosystem.
The article details NetEase Yanxuan's end‑to‑end data product ecosystem and data middle platform, describing four core data products, the architecture of the data middle platform, efficient high‑quality delivery, governance practices, and key performance metrics that support data‑driven decision making.
This article details how Yuedu Group designed and implemented an overseas big data platform, covering overall system architecture, offline data‑warehouse construction with dimensional modeling, real‑time streaming using Oceanus and ClickHouse, and future plans for cost reduction and data quality assurance.
This article introduces Kuaishou's data platform serviceification, outlining the background challenges for data engineers, the platform's architecture and key technologies such as configuration‑driven development, multi‑mode APIs, data acceleration, and high‑availability mechanisms, and concludes with a summary of achievements and future directions.
This article provides a side‑by‑side technical comparison of HBase, Kudu, and ClickHouse, covering their installation dependencies, architectural designs, read/write workflows, query capabilities, real‑world use cases at Didi, NetEase, and Ctrip, and practical operational tips.
DiDi’s real‑time MySQL‑to‑Hive pipeline captures row‑mode binlog with a custom Canal component, converts it to JSON, streams it via Kafka to HDFS, restores it into Hive tables, and uses Dquality for integrity, achieving millisecond latency for over 19,000 daily sync tasks handling roughly 50 TB of data.
The article outlines how digital‑champion enterprises achieve superior performance by integrating four core ecosystems—customer solutions, operations, technology, and talent—through strategic planning, partnership, and advanced technologies such as AI, big data, and industrial IoT, while highlighting maturity stages and practical implementation steps.
This article shares Qunar's experience using Flink together with Apache Iceberg 0.11 to address real‑time data warehouse challenges, covering background pain points, Iceberg architecture, solutions for Kafka data loss and Hive latency, and optimization practices such as small‑file handling, sorting, and checkpoint management.
This article explains how Tencent Cloud EMR integrated Hadoop YARN with Kubernetes Pods to create a hybrid online‑offline deployment, implement elastic autoscaling and multi‑label resource allocation, and achieve several‑hundred‑percent improvements in CPU utilization while preserving cluster stability.
This article introduces the HR performance data preprocessing platform’s requirements, explains why HBase was selected as the storage solution, details its core concepts, architecture, data write/read processes, best practices, limitations, and presents performance metrics demonstrating its suitability for large‑scale, high‑throughput workloads.
This article examines the challenges of using Kafka, Flink, and Hive for real‑time data warehousing, introduces Apache Iceberg 0.11 as a solution, details its architecture, query planning, Flink integration, code examples, optimization techniques, and summarizes the benefits for large‑scale data processing.
This article details Xiaohongshu’s multi‑stage evolution of its OLAP infrastructure—from Redshift to Presto, ClickHouse, and finally DorisDB—describing the data pipeline, tool comparisons, advertising use‑case implementation, and the resulting performance and operational benefits.
This article explains how HBase’s distributed column‑oriented architecture, high‑performance read/write capabilities, and flexible schema make it a cost‑effective solution for handling massive, unstructured HR performance data, covering its core concepts, cluster operation, best practices, and performance metrics.
The article outlines Ctrip's data governance framework, detailing background challenges, metadata construction, cost and quality optimization techniques, data flow improvements, platform modules, health metrics, and concludes with a summary of achievements and future directions.
This article explains the fundamental differences between databases, data warehouses, and data lakes, describes how they complement each other, and introduces the Lake House concept that integrates transactional and analytical workloads using cloud services such as Amazon S3, Redshift Spectrum, and Athena.
This article presents a comprehensive guide to synchronizing heterogeneous data sources with ElasticSearch, migrating clusters across environments, and implementing robust disaster‑recovery solutions for both intra‑city and inter‑city high‑availability scenarios.
The article provides a comprehensive overview of digital transformation, covering its definition, essential strategic questions, key drivers such as customer expectations, cloud and AI, priority areas in the value chain, practical frameworks, roadmap steps, expected benefits and common reasons for failure.
This article explains the evolution of cloud computing from resource management to elastic virtualization, the emergence of IaaS, PaaS and SaaS service models, how big‑data processing relies on distributed cloud platforms, and why artificial intelligence now depends on massive data and cloud‑scale compute to deliver intelligent services.
This article compiles Baidu experts' presentations at QCon 2021, covering unified quality‑efficiency delivery for feed recommendation, software engineering capabilities, AIOps fault‑management practices, Apache Doris real‑time analytics, large‑scale Service Mesh deployment, massive service‑governance techniques, and deep‑learning platform innovations, with speaker details and audience benefits.
This article explains how to efficiently move large volumes of data from Hive to HBase by leveraging HBase's bulkload mechanism, detailing the original MapReduce workflow, its performance bottlenecks, and a rewritten Spark‑based solution that simplifies ETL, improves partitioning, and achieves several‑fold speedup.
This article provides a comprehensive overview of user profiling, covering its definition, the five‑dimensional framework (goal, method, organization, standards, validation), various tag classifications, tag‑system architecture, modeling techniques, practical applications such as precise marketing and product innovation, and a step‑by‑step guide for building a profiling system using big‑data and AI methods.
This article presents NetEase Game's streaming ETL solution built on Flink, covering business background, log characteristics, specialized and generic ETL services, architectural evolution, Python UDF integration, runtime optimizations, fault‑tolerance mechanisms, and future roadmap for unified real‑time and offline data warehouses.
This article explains how 58's commercial data team built a comprehensive data metric system—from identifying common metric definition issues to establishing a domain‑driven hierarchy, distinguishing atomic and derived metrics, implementing a unified metric management platform, and providing APIs and examples for querying and visualizing metrics.
The Longgong Data Analysis Platform enables Idle Fish to capture, store, and analyze billions of structured product attributes in real time across more than 8,000 categories, using TableStore, MySQL, ODPS, and a distributed scheduler to achieve over 50% query speedup, 80% category coverage, and rapid support for search and recommendation teams.
The article explores how low‑code platforms can complement traditional algorithm development, enhance collaboration between business users and engineers, and accelerate big‑data and AI initiatives by improving data cleaning, modular design, and feedback loops, while highlighting the trade‑offs of abstraction and flexibility.
This article provides a comprehensive overview of data warehouses, explaining their purpose, differences from databases, OLTP vs OLAP, traditional versus internet data warehouse models, layered architecture, modeling theories, metric dictionaries, date dimensions, naming conventions, data governance, and incremental synchronization techniques with practical SQL examples.
This article explains Apache Pulsar’s cloud‑native, storage‑compute separated architecture, its data model and scalability features, and how it integrates with Flink to provide a unified platform for both real‑time streaming and batch processing in big‑data applications.
The article details how NetEase Cloud Music co‑built a unified data service platform with NetEase YouShu, describing its architecture, phased development from internal use to online high‑concurrency services, feature enhancements such as API marketplace, multi‑source support, parameter conversion, and future roadmap for broader data products.
This article explains the concept of data lakes, outlines a four‑layer open‑source architecture, presents several classic Flink‑Iceberg use cases, details why Iceberg was chosen, and describes the design of Flink’s streaming sink and upcoming community roadmap.
This article introduces Kafka, LinkedIn’s high‑throughput distributed messaging system, explains its core concepts such as brokers, topics, partitions, offsets, producers, consumers, and consumer groups, outlines common use cases like asynchronous decoupling and data‑stream processing, and details its fast performance mechanisms, fault‑tolerance, installation, and configuration steps.
This article walks through a complete Flink‑based recommendation system, detailing its v2.0 architecture, recommendation algorithms, front‑end and back‑end components, and step‑by‑step Docker deployment of MySQL, Redis, HBase, and Kafka services.
This article explains why data warehouses are critical for decision‑making, outlines the challenges of immature warehouses, and provides a step‑by‑step framework—including goal setting, technology selection, problem identification, domain modeling, layer design, modeling principles, and governance standards—to help teams build a robust, maintainable data warehouse.
The presentation details NetEase's big data platform operations, covering current usage, the internally built EasyOps control system, a generic service‑operation framework based on Ansible, Prometheus‑Grafana monitoring, configuration management, network and storage optimizations, and lessons learned from cloud migration.
The article presents a detailed overview of Tencent Advertising's real‑time strategy data framework, explaining its role in the ad system, the challenges of massive log volumes, and the architectural, performance, and high‑availability solutions implemented to achieve fast, reliable, and scalable ad decision making.
Didi migrated over 10,000 Hive SQL tasks to Spark SQL, achieving 85% Spark task share, cutting execution time by 40%, and reducing CPU and memory usage by 21% and 49% respectively, through a systematic migration process that addressed syntax, UDF, performance, and functional differences between the two engines.
This article shares practical experience of building a high‑performance distributed prediction service by combining TensorFlow for Java with Spark‑Scala, covering framework selection, performance comparison, model training, loading, inference, deployment, and optimization techniques for large‑scale data processing.
This article explains common causes of slow MySQL queries, how proper indexing and lock handling can improve performance, introduces Elasticsearch’s inverted‑index advantages and suitable use cases, and outlines HBase’s column‑family storage model and row‑key design for large‑scale data.
This article presents NetEase Game's Flink‑based streaming ETL system, detailing business background, log classifications, specialized and generic ETL services, Python UDF integration, runtime optimizations, HDFS write tuning, SLA metrics, fault‑tolerance mechanisms, and future roadmap for unified data lakes and PyFlink support.
This article introduces JD's open‑source federated learning platform 9N‑FL, explaining the data‑island problem, the fundamentals and classifications of federated learning, its four key features, the system’s layered architecture, development timeline, real‑world advertising use case results, and future enhancements.
This article details the end‑to‑end upgrade of a 5000‑node Hadoop 2.6.0 cluster to Hadoop 3.2.1 at 58.com, covering HDFS migration, RBF and EC adoption, Yarn federation and rolling upgrades, MR3 integration, extensive compatibility testing, and operational lessons learned for large‑scale big‑data platforms.
This article presents a comprehensive guide to data model architecture, detailing layered data store definitions, classification structures, processing flow, naming conventions, and core design principles to help engineers build scalable, maintainable data warehouses.
This article details Vipshop's OLAP evolution, describing how Presto, Kylin, and ClickHouse are integrated, the deployment architecture with HAproxy and chproxy, containerization on Kubernetes, and the Flink‑ClickHouse pipeline that enables self‑service analysis of hundred‑billion‑row datasets while addressing performance challenges and future roadmap.
Kafka is a high‑throughput distributed publish‑subscribe system that uses brokers, topics, partitions, offsets, producers, consumers, and Zookeeper for metadata and leader election, offering fast sequential disk writes, page‑cache zero‑copy transfers, ISR‑based replication, and includes step‑by‑step installation of JDK, Zookeeper, and Kafka.
This article provides a comprehensive guide to Apache Kafka, covering its core concepts, architecture, key APIs, topics and partitions, deployment steps, multi‑broker clustering, fault tolerance, and data integration using Kafka Connect, with detailed command‑line examples.
This article provides a comprehensive technical overview of data middle platforms, covering data aggregation, offline and real‑time development, smart operations, data asset management, governance, service layers, platform implementations, warehouse layering, and key differences between offline and real‑time data warehouses.
The CSIG Visualization and Visual Analysis Committee’s visit to Alibaba’s Xixi Campus on May 21, 2021 brought together leading academics and industry experts to discuss graph data, big‑data research, spatio‑temporal data, low‑code design, and cutting‑edge visualization techniques, fostering deep industry‑academia collaboration.
This article compiles detailed interview experiences, question lists, and practical advice for candidates targeting backend, big‑data, and cloud positions at leading Chinese tech firms, offering timelines, personal background, preparation tips, and reflections to help job seekers navigate multi‑round technical interviews efficiently.
The article reviews Anthony J. Algmin’s reflections on past data‑architecture predictions, current hot topics such as cloud, AI/ML, data governance, and real‑time analytics, and forecasts future trends including metadata management, blockchain, and the evolving role of data architects within enterprises.
The article examines the strengths and weaknesses of combining HBase and Elasticsearch for massive data storage and retrieval, outlines three integration patterns and their challenges, and presents Alibaba Cloud's Lindorm Searchindex as a SQL‑driven, low‑cost, strongly consistent solution that simplifies development and improves performance.
A unified semantic layer for data development solves metric‑change ripple effects, developer burden, and large‑scale query performance problems by offering consistent metric definitions, multi‑view access, concise auto‑generated SQL, instant propagation of updates, and engine‑driven optimal query selection, thereby bridging business and engineering and cutting maintenance effort.
This article provides a comprehensive introduction to Kafka, covering its role as a message system, core concepts such as topics, partitions, producers, consumers, messages, the cluster architecture with replicas and controllers, performance optimizations, log segmentation, and network design, all illustrated with diagrams and code examples.
This article explains the concept of a data lake—its origin in 2011, how it differs from traditional databases and data warehouses, its core characteristics such as raw data storage, on‑demand computing, and schema‑on‑read, as well as its advantages, challenges, architectural components, and future outlook within the big‑data ecosystem.
This article presents a comprehensive walkthrough of a Flink‑powered recommendation system, detailing its v2.0 architecture, module functions, recommendation algorithms (hotness, product similarity, collaborative filtering), front‑end and back‑end UI, and step‑by‑step Docker deployment of MySQL, Redis, HBase, and Kafka services.
This article systematically introduces NetEase Kyuubi, an open‑source high‑performance JDBC and SQL execution engine built on Apache Spark, covering its background, core architecture, service discovery, session and operation management, startup processes, and key source‑code implementations with detailed code examples.
Tencent Cloud Oceanus, a computing service powering internal apps like WeChat and external partners such as Bilibili, scales to over 30,000 cores handling 5 PB daily and 500,000 jobs, and tackles Flink SQL’s syntax, function and operational limits with table‑valued functions, incremental and enhanced tumble windows, and caching‑based retraction optimization that cuts downstream data volume up to 30× and improves join performance by about 20 %.
This article explains how UCloud's US3 object storage, combined with a custom Hadoop adapter, separates compute and storage, optimizes file system operations, and leverages caching and specialized APIs to dramatically reduce storage costs and improve read/write performance for large‑scale Hadoop workloads.
iQIYI built an in‑house user‑feedback analysis system that automatically ingests multi‑channel data, classifies and clusters issues, assesses feedback quality, localizes problems, and streamlines repair closure, boosting recall accuracy, alarm precision, closure rates and reducing cycle time across business lines to enhance user experience.
This article extends the concepts introduced in Streaming 101 by deeply exploring data processing patterns for unbounded data, covering windowing, watermarks, triggers, accumulation modes, and their practical implications for building robust low‑latency streaming pipelines.
This article provides an extensive overview of data governance in the big‑data era, covering common pitfalls, the role of metadata, data quality management, data standardization, and data asset management, and offers practical recommendations for organizations to implement effective governance practices.
The article analyzes how cloud‑native concepts such as DevOps, micro‑services, continuous delivery, and containerization can be applied to big‑data foundations, outlining four guiding principles—industrialized delivery, cost quantification, load‑adaptive scaling, and data‑centric design—and describing concrete Hadoop‑based architectures and Tencent Cloud solutions that lower cost while boosting performance.
This article provides a comprehensive tutorial on installing UCloud's free USDP version for private big‑data deployments, covering environment preparation, minimum node specifications, resource download, configuration files, one‑click initialization scripts, server startup, web UI access, license acquisition, and optional manual setup procedures.
Vineyard, an open‑source distributed memory data‑sharing engine, tackles the inefficiencies of traditional file‑system based big‑data pipelines by enabling zero‑copy, in‑memory object exchange, Kubernetes‑aware scheduling, and plug‑in operators, delivering up to 1.34× faster end‑to‑end execution.
This article provides a comprehensive overview of AIOps, covering its definition, evolution from manual to AI-driven operations, core capabilities, and real-world applications in capacity prediction, anomaly detection, and alarm merging, illustrated with case studies from a food‑retail giant and internal logistics.
The article provides a comprehensive technical overview of Toutiao's rapid growth, detailing its massive user base, data collection and processing pipelines, user modeling, cold‑start strategies, recommendation engines, storage solutions, push notification mechanisms, and the underlying microservice and PaaS architecture.
