This article explores how enterprises can tackle the explosive growth of data by adopting modern big‑data architectures, including storage‑compute separation, data‑driven workflows, risk‑control frameworks, and real‑world Spark optimizations, offering practical guidance for scalable, high‑performance analytics.
This article reviews the challenges of handling Zettabyte‑scale data, outlines practical big‑data processing architectures, discusses storage‑compute separation, data‑driven workflows, risk‑control frameworks, and shares concrete Spark implementations at MobTech, offering actionable insights for modern data engineers.
The article summarizes Guo Yi’s 2019 Alibaba Cloud conference talk, outlining Xiaohongshu’s personalized recommendation architecture, detailing the data stack from ingestion to warehouse, and showcasing a Flink‑based real‑time multi‑dimensional user behavior aggregation use case, followed by a vision for the next year’s data architecture evolution.
This article examines the big‑data architectures of three leading Chinese internet companies—Taobao, Meituan, and Didi—detailing their data sources, synchronization mechanisms, batch and streaming processing layers, and the common scheduling components that unify their Hadoop‑based ecosystems.
This article outlines the challenges of various big‑data scenarios such as financial risk control, recommendation systems, and social feeds, explains why Spark is chosen over alternatives, describes a one‑stop data platform architecture with Spark‑HBase integration, and shares best‑practice tips and case studies.
The article explains the concept of a data middle platform, why it matters for rapid demand response and resource integration, and outlines the distinct responsibilities and required skill sets of data product managers and data platform product managers within such ecosystems.
This article details how Weibo’s advertising team designed and implemented a real‑time data platform capable of processing over a hundred billion daily logs, covering technology selection, Flink advantages, architecture evolution, data processing pipelines, component libraries, fault‑tolerance strategies, and the construction of a multi‑layer real‑time data warehouse.
The article explains Alibaba's data middle platform—its definition, methodology, organizational structure, key tools, and how it differs from traditional data warehouses and data lakes—while highlighting its role in supporting scalable, business‑centric data services and digital transformation.
By adapting Alibaba’s OneData methodology, the project establishes a unified data‑warehouse architecture, standards, and governance framework—including consolidated business intake, standardized design layers, naming conventions, and delivery metrics—that resolves data‑quality issues, enhances scalability and reusability, and delivers faster, reliable data support for evolving business needs.
The article explains that HBase and Hive occupy distinct roles in big‑data systems—HBase handles real‑time random queries on massive detail data, while Hive provides batch‑oriented SQL‑based processing on HDFS—and describes how they are typically combined in a data pipeline.
iQIYI upgraded its big‑data stack by adopting Druid as the core engine for free‑time queries and ElasticSearch for pre‑computed fixed‑time queries, overcoming early API, security and scaling challenges through monthly segment granularity, parallel sub‑queries, Redis caching and failover, cutting typical query latency from over two seconds to about 150 ms and reaching 99.9 % service success.
This article explores the core components of data systems, the evolution toward lightweight, intelligent big data architectures, the distinction between primary and secondary storage, challenges of data replication, and how Alibaba Cloud's Tablestore implements advanced features such as storage‑compute separation, CDC, and multi‑model indexing for scalable, cost‑effective structured big data storage.
To handle terabytes of daily data and diverse business needs, the company built a three‑layer basic data platform—collection/computation/storage, unified data management, and API‑driven services—augmented by a standardized collection system, a robust Domino scheduler, and a self‑service analysis tool, aiming to evolve into a full data‑middle‑office for end‑to‑end intelligence.
This article presents an in‑depth overview of Hulu’s big‑data platform, detailing its multi‑layer architecture, the design and functionality of the Sophon OLAP cache layer, and how Impala is employed for high‑performance query processing and integration with cloud‑native engines.
The article presents a comprehensive technical analysis of JD's Marketing 360 project, detailing the three industry pain points, the 4A consumer‑asset model, the 4E marketing methodology, and the underlying big‑data, AI‑driven architecture that enables real‑time analytics, multi‑modal model training, and performance optimizations.
This article explains how Meituan tackled growing real‑time data demands by redesigning its streaming platform, adopting a layered real‑time data warehouse architecture, selecting storage and compute technologies such as Cellar, Elasticsearch, Druid and Flink, and sharing practical tips on dimension expansion, joins, and aggregation to achieve higher throughput and lower latency.
This article summarizes a presentation by Ping An Insurance data engineers on Druid’s architecture, core concepts, node roles, tuning strategies, and real-world deployment for insurance analytics, illustrating how Druid enables sub‑second, high‑cardinality OLAP queries and supports both real‑time and batch processing.
The article examines the evolution of big‑data processing by comparing five prevalent architectures—traditional Hadoop‑based stacks, streaming‑only designs, Kappa, Lambda, and the unified Unifield model—highlighting their strengths, weaknesses, and suitable scenarios while discussing the limitations of classic BI systems and the role of distributed storage, computation, and machine‑learning integration.
This article explains how to design data architecture for microservice systems, covering microservice fundamentals, advantages, decoupling, lightweight APIs, continuous delivery, database per service versus shared databases, polyglot persistence, scaling dimensions, sharding strategies, and why MongoDB is a suitable choice.
The article describes the design, data integration, ID mapping, tag generation, and application scenarios of the 58 user profiling platform, which aggregates billions of user IDs across multiple business lines to provide online and offline persona data for personalization, analytics, and AI modeling.
This article explains how enterprises can build a scalable data analytics platform on Hadoop by outlining the multi‑layer architecture, storage options, data synchronization methods, and ETL/offline computation techniques, while highlighting practical component choices such as Hive, HBase, Spark, and Oozie.
In this interview, Baidu’s senior big‑data architect Ma Ruyue discusses his career transition from Hadoop to online databases, the design philosophy behind Baidu’s Palo ROLAP system, the future of HTAP, and his views on the evolving big‑data ecosystem including Spark, AI, and containerization.
This article explains the multi‑layer data architecture design for microservice systems, covering concepts such as data usability, primary and secondary data decoupling, sharding, multi‑source data adaptation and caching, and introduces data marts to improve scalability and maintainability.
Alibaba’s senior data expert Yao Bin Hui explains how the company constructed a standardized, end-to-end big-data ecosystem—from low-level data collection and AI algorithms to data services and product platforms—enabling petabyte-scale integration and second-level response times that power both internal operations and millions of external users.
The article describes the design, storage, update mechanisms, and optimization strategies of a headline recommendation platform that aggregates various data types using algorithms, MySQL, Redis, and a modular data‑fetching framework to achieve scalable and efficient content delivery.
The article details how the Transportation Division’s data warehouse grew from a simple SQL‑based solution to a multi‑layer, big‑data platform handling petabyte‑scale data with daily 10 TB increments, describing the technical and business architecture, ETL strategies, and future roadmap.
This article explores the cutting‑edge data pipelines of Netflix, Facebook, Airbnb and Pinterest, detailing the massive event volumes they handle, the core technologies such as Kafka, Spark, Presto and Hadoop, and how these giants design scalable, real‑time analytics infrastructures.
This article explains why data warehouses should be layered, describes the classic ODS‑DW‑APP model, details each layer’s purpose and implementation techniques, presents an improved layering scheme with dimension and temporary tables, and answers common questions about parallel DWS and DWD processing.
This article examines how Baidu Waimai designed and evolved its big data platform, comparing traditional BI to modern 4V‑driven architectures, detailing database choices, OLTP/OLAP trade‑offs, data‑analysis team structures, and the essential steps for data governance and platformization.
This article explains why data platforms are essential for modern SMEs, defines what a data platform is, outlines a four‑step methodology (source definition, analysis theme, ETL processing, and reporting), and shares architectural choices, team structures, common pitfalls, and practical advice for rapid, iterative implementation.
Game server development suffers from generic client‑communication tools and inadequate data stores, leading to duplicated, latency‑heavy code, so a purpose‑built, memory‑resident distributed cache that persists locally and eliminates serialization boiler‑plate is essential for real‑time, low‑latency gameplay.
The article shares a comprehensive recap of AWS re:Invent 2016, highlighting over a thousand new features across compute, storage, networking, security and tools, discussing serverless concepts, the Modern Data Architecture framework, and practical lessons learned by the English‑Fluent‑Talk engineering team.
The article details Umeng’s mobile big‑data platform architecture, describing its Lambda‑style hybrid design, data ingestion pipeline with dual Kafka clusters, offline and real‑time processing using Hadoop, Spark, Storm, and storage layers such as HDFS, HBase, MongoDB and Elasticsearch, while also discussing challenges in data collection, cleaning, computation, security, and value‑added services.
This article reviews a comprehensive big‑data lecture covering traditional databases, Hadoop ecosystems, commercial big‑data platforms, computing models, analysis techniques, visualization, and leading vendors, highlighting how these technologies shape today’s data‑driven enterprises.
This article details Meituan Dianping's data‑driven approach to precise marketing, describing the O2O marketing framework, a layered pyramid data system, profiling techniques, budget monitoring, and two real‑world case studies that together illustrate how big‑data technologies boost marketing efficiency on mobile platforms.
Meituan‑Dianping’s data‑driven precise‑marketing platform combines a layered pyramid architecture—data warehouse, service, and front‑end layers—with real‑time profile services powered by Redis and Elasticsearch, offering tools such as Hoek, Cord, and Cloud/Star to automate audience selection, coupon recommendation, and KPI monitoring, illustrated by food‑delivery user discovery and WeChat red‑packet coupon case studies, and guided by principles of reusable models and SOA decoupling.
This article presents Meituan Dianping's data‑driven precise marketing architecture, detailing a layered pyramid system, user profiling, budget monitoring, and two real‑world cases—potential user mining and a smart coupon engine—demonstrating how big‑data techniques improve marketing efficiency and ROI.
Airbnb’s engineering team outlines the evolution of its big‑data platform, detailing the philosophy behind its architecture, the dual “gold” and “silver” Hive clusters, migration to Mesos, use of Presto, Airpal, Airflow, and the performance and cost gains achieved through these design choices.
This article examines the critical issues of massive data handling, concurrency, file storage, relational design, indexing, distributed processing, AJAX usage, security, clustering, and OpenAPI trends that developers must address when architecting large, high‑interaction web sites.
This article explains how LinkedIn manages real‑time profile updates, news feeds, and social graph data through a three‑phase architecture that combines RDBMS, NoSQL stores, caching, and Lucene indexes to achieve high consistency, availability, and partition tolerance at massive scale.
The Meizu Tech Open Day showcased the company's rapid evolution to a data‑driven mobile internet firm, detailing its DW1.0 and DW2.0 data‑warehouse architectures, recommendation pipelines, Spark adoption, and ELK‑based log analytics, while sharing practical lessons and future challenges.
The article details Taobao's massive data product architecture, describing a five‑layer system that processes billions of daily records using Hadoop, real‑time streams, distributed MySQL and HBase clusters, and a middleware layer called Glider that unifies queries, caching, and front‑end integration.
This article introduces the fundamental MapReduce model and Hadoop framework, explains their roles in large‑scale data processing, and then examines Taobao’s massive‑data product architecture—including its data source, compute, storage, query, and product layers, as well as the MyFOX, Prom, and Glider components and caching strategies.
This article introduces the fundamental MapReduce model and Hadoop framework, explains their components such as HDFS, MapReduce, and HBase, and then examines Taobao’s large‑scale data product architecture—including storage, computation, query, and caching layers—to illustrate practical big‑data processing techniques.
The article outlines a practical, end‑to‑end data platform architecture for internet businesses, covering data collection, storage and analysis, sharing, real‑time processing, task scheduling, and the importance of simplicity and agility in building an agile data warehouse.
This article explores the 4V characteristics of big data, real‑world data growth examples, historical analogies, Google’s GFS‑MapReduce‑BigTable model, Hadoop’s architecture and HDFS processes, HBase components, NoSQL alternatives, and practical big‑data applications at Tencent and beyond.
This article chronicles Suning’s journey from early SAP‑based data warehouses to a modern, open‑source big data platform featuring real‑time collection, Hadoop‑Hive offline processing, Storm‑based streaming, and a visual development environment, highlighting how each layer addresses growing data volume, variety, and business demands.
This article explains why many companies over‑invest in Hadoop, outlines how to evaluate data size, growth, and relevance, and presents practical alternatives such as archiving, data sampling, database sharding, and hiring business‑savvy analysts before committing to a Hadoop deployment.
