This article compares R, Python, Scala, and Java for big‑data projects, outlining each language’s strengths and weaknesses, and offers guidance on selecting the most suitable language based on project requirements, team expertise, and production needs.
This article explains Spark's executor memory architecture, covering on‑heap and off‑heap allocation, static versus unified memory managers, storage and execution memory handling, RDD persistence, eviction policies, and the role of Tungsten's page‑based management in optimizing performance.
Bloom filters are highly space-efficient probabilistic data structures that quickly test set membership using multiple hash functions, guaranteeing no false negatives while allowing a small false positive rate, making them ideal for large-scale applications such as email blacklists and massive URL deduplication.
This article recounts Wu Guanlin’s presentation on the evolution of Vipshop’s personalized recommendation system, detailing the technical challenges of real‑time predictions, the three generations of architecture, the four‑stage recommendation engine, and the VRE platform’s design for scalability and low latency.
The article examines how Baidu Waimai, positioning itself as a big‑data company, built an intelligent system for database resource optimization and risk management, outlines preventive measures for database reliability, and discusses the rapid growth and challenges of cloud databases.
This article explains various enterprise log types, recommends monitoring tools like Cacti, Zabbix, Splunk, and the ELK stack, and details architectures for handling server, application, and user‑click logs using technologies such as Logstash, Elasticsearch, Kibana, Kafka, Flume, and Spark.
The article outlines Meituan‑Dianping’s tourism recommendation system, detailing its evolution from simple hot‑sale recall to sophisticated decay‑based, GPS‑aware, collaborative filtering and XGBoost reranking pipelines, the four‑layer architecture supporting dozens of travel scenarios, and future plans to broaden recall, adopt deep models, and expand multimodal travel recommendations.
The article chronicles the development of Tongcheng's centralized log system from early file‑based logging through a MongoDB‑based solution to the current multi‑layer architecture using Flume, Elasticsearch, and Hadoop, highlighting design decisions, challenges, and future improvement plans.
This article introduces the architecture, key modules, and implementation details of the Little Boy platform, a Greenplum optimization and operations system that parses SQL, applies index and distribution‑key tuning, manages resources, and outlines future enhancements for large‑scale data warehouses.
This article walks through Spark’s origins, its core RDD concept, how it improves on Hadoop’s MapReduce, the role of in‑memory processing, functional programming support, and the emergence of Spark SQL with DataFrames and the Catalyst optimizer.
This article explains how real‑time data streaming turns massive, continuously growing datasets into actionable insights across finance, energy, and e‑commerce, showcasing early adopters like ConocoPhillips and DHL while urging businesses to rethink models for the next wave of data management.
The article presents Baidu's end‑to‑end third‑party sentiment feedback solution, detailing its evolution from waterfall to agile and lean development, and describing the three‑stage pipeline of data acquisition, cleansing, and warehousing that enables real‑time product quality loops.
The article outlines the key competencies, responsibilities, and career development advice for data professionals across the entire data stack—from building big‑data platforms and data warehouses to visualization, analysis, algorithm engineering, and deep‑learning applications—emphasizing the importance of creating business value with data.
Tencent Analytics (TA) is a free web analytics platform that processes terabytes of daily data in real time, using a custom architecture featuring JavaScript collection, event streaming, in‑memory computation, and NoSQL storage with Redis and LevelDB, offering site owners instant insights and high availability.
Tencent’s senior engineer explains how the TDW (Tencent Distributed Data Warehouse) grew from a few hundred to thousands of nodes, the challenges of cross‑city migration, and the modeling, relationship‑chain, dual‑write tables, and platform strategies they built to ensure seamless, low‑impact data and task migration.
The article explores the evolution of big‑data applications in China, from Oracle’s trend report and the concept of "omni‑domain data" to UMENG+’s technical architecture, unified tech stack, AI integration, and future directions for delivering real customer value.
This article details how Tencent's game operations team redefined operational services, introduced micro‑service architecture, applied big‑data driven recommendations, and built intelligent, automated pipelines for server opening, merging, version releases, and download services, achieving significant efficiency and cost gains.
Meituan Waimai’s Feature Archive platform processes billions of daily orders by managing ~200 user and 400 merchant tags through a three‑layer architecture—Hive, Elasticsearch, HBase, and MySQL—offering visual tag selection, instant self‑service queries, full data extraction, and a predicate‑logic query language, while supporting future extensibility.
The article describes how Qunar designed and evolved its Prism real‑time data platform—leveraging ELK, Kafka, Spark, Docker, and Mesos—to improve data collection, monitoring, and analysis, reduce deployment time, and support scalable DevOps operations across the company.
The talk outlines nine essential computing capabilities—transaction processing, system robustness, connectivity, decision-making, data insight, intelligent services, biometric authentication, blockchain trust, and immersive integration—that have transformed Ant Financial over the past decade and outlines the challenges and strategies for the next ten years.
This article explains the architecture, real‑time computation framework, and storage solutions of Tencent Analytics, detailing how massive TB‑level web‑traffic data are collected via JavaScript, processed in memory‑centric streaming components, and stored using Redis and LevelDB to achieve second‑level updates.
A diverse group of practitioners share recommendations for books, technologies, real‑world use cases, and practical challenges when learning and applying big‑data processing, covering Hadoop, Spark, data visualization, ETL, and the relationship between data, algorithms, and business value.
This article reviews major big‑data storage and query architectures—including HBase, Dremel/Parquet, pre‑aggregation systems, Lucene, and the custom Tindex solution—evaluating their strengths, weaknesses, and suitability for real‑time, high‑volume analytical workloads.
This article explores the challenges of operating Alibaba's large‑scale data platforms, describes the automation platform built to address them, and shares data‑driven, fine‑grained operational practices that enable stable, efficient, and cost‑effective service delivery.
This article explores the evolution of log processing in the big‑data era, detailing how ELK Stack, Kafka, and Spark Streaming work together to provide scalable, real‑time log collection, analysis, and visualization for modern cloud‑native operations.
Using big‑data visualizations, this article examines the 2017 Chinese Spring Festival migration, revealing early peak travel, the top twenty outbound cities accounting for over 40 % of movement, net flow patterns, and demographic profiles such as age, education and industry distribution across major urban centers.
This article explains Ctrip's flight ticket tracking framework (UBT), detailing client‑side and server‑side event collection methods, the purpose and trade‑offs of each tracking type, metric definitions, data association challenges, common pitfalls, and best practices for reliable data‑driven analysis.
This article explains how Alibaba leverages AI, big‑data analytics, and advanced recommendation algorithms to enable real‑time visitor clustering, personalized storefronts, and tailored content across its Customer Operation Platform, Double 11 promotion pages, QianNiu headlines, and service market, delivering significant conversion and engagement gains.
This article reviews four ELK deployment patterns, compares their strengths and weaknesses, and demonstrates how ELK can be applied to big‑data operations such as log aggregation, system monitoring, fault diagnosis, and performance analysis with practical examples.
The discussion explores how PHP engineers can advance their careers by embracing new technologies such as Go, Python, big data, AI, and cloud computing, while also emphasizing soft‑skill growth, project management, and strategic decision‑making based on business trends and personal goals.
Meituan Delivery’s rapid expansion across multiple categories relies on detailed user profiling and machine‑learning models—such as high‑potential customer prediction, churn risk regression and Cox survival analysis—to personalize acquisition, retention, and scenario‑based cross‑selling, while addressing sparse behavior, unstructured data, and geographic context challenges.
This article explains the fundamentals of search engine architecture, covering text collection, indexing, ranking and evaluation, and then traces Twitter's internal search evolution from MySQL full‑text search to the Earlybird index server, Blender aggregation, and smart memory‑SSD strategies.
The article describes how LeKe Sports built and continuously upgraded its Hadoop‑based big data platform—from a manual ETL‑to‑Elasticsearch reporting system to a 2.0 architecture featuring Spark Streaming, SQL‑based query layers, Elasticsearch indexing, and cloud‑native storage and backup solutions—to meet rapidly growing PB‑scale data demands.
The article explains Ctrip's comprehensive User Behavior Tracking (UBT) system for flight tickets, detailing client‑side (ctm, action, trace, pv) and server‑side tracking, their implementation, data flow, key metrics, common challenges, and best practices for reliable analytics.
Apache CarbonData 1.0.0, now an Apache incubating project, adds over 80 new features and bug fixes—including a new data loading solution, Spark 2.1 integration, update/delete SQL support, adaptive compression for numeric types, B‑Tree LRU cache, V2 format for faster first‑query performance, vectorized reader, bucket‑table joins, off‑heap memory, single‑pass loading, and pre‑generated dictionaries—aimed at delivering faster, more flexible, and efficient columnar storage for big‑data workloads.
This article explains how Alibaba’s Cainiao leverages big‑data prediction, supply‑chain scheduling, pre‑packing, intelligent routing, electronic waybills and smart sorting to handle billions of parcels during the Double 11 shopping festival, dramatically improving speed, cost and customer experience.
This article outlines ten essential Hadoop data‑security measures, describes the eight‑layer Hadoop ecosystem, presents real‑world Hadoop case studies, and discusses the platform's development roadmap and future trends, offering a comprehensive guide for big‑data professionals.
This article provides a comprehensive overview of Hadoop as the leading open‑source platform for big‑data processing, detailing its core components HDFS and MapReduce, the evolution to Hadoop 2.0/YARN, and the extensive ecosystem of tools and commercial solutions that enable scalable storage, analysis, and machine‑learning on massive data sets.
Learn how to construct a simple, high‑availability real‑time stream processing platform using lightweight components such as Kafka, Zookeeper, Thrift/Avro, and optional storage like MongoDB or Elasticsearch, offering a practical alternative to heavyweight frameworks like Storm and Spark Streaming for small‑to‑medium enterprises.
This article explores how software testers can leverage big data and data‑mining techniques, particularly a Naive Bayes classifier, to objectively prioritize defect fixes, improve testing processes, and address uncertainties inherent in traditional testing workflows.
Over three years, a small fintech company transformed its platform from a single‑server PHP/Java stack to a micro‑service‑based Spring Cloud architecture, undergoing four major upgrades that introduced distributed systems, SOA governance, big‑data pipelines, MongoDB replication, Redis caching, and open‑source tools, enabling transaction volumes exceeding one hundred billion.
This article details how Qihoo 360 built large‑scale traffic analysis, VxLAN deployment, and SDN practices to automate network operations, improve visibility, and support security, while sharing real‑world challenges, solutions, and a Q&A on network automation strategies.
The article explains how Taobao’s content‑centric strategy, embodied in the Beehive platform, builds an end‑to‑end content chain—from creator tools and health scoring to personalized distribution and commerce mechanisms—enabling massive, efficient content production and monetization during the Double 11 shopping festival.
This article details Alibaba's real‑time computing architecture for the 2016 Double 11 event, covering background, core components such as DRC, TT, Galaxy, OTS, XTool and OneService, and explains optimization techniques, fault‑tolerance strategies, stress‑testing practices, and future upgrade plans to handle massive streaming data workloads.
Transpedia, launched with TDH 4.6, consolidates all StarRing product manuals into a single online portal that offers real‑time synchronization, keyword search, and intuitive navigation, eliminating the need for PDF downloads and dramatically improving documentation accessibility and user experience.
The article reviews Meituan‑Dianping’s tech blog evolution since 2013, highlights the ten most‑read posts of 2016 across backend, big data, mobile, and AI, and outlines emerging focus areas such as testing, cloud storage, and algorithmic innovations for the coming year.
This article explains the origins, core features, logical architecture, and advantages of data lakes, contrasts them with traditional data warehouses, outlines a modern data architecture that combines lakes and warehouses, and introduces the DCE intelligent data lake platform with practical Q&A.
The article explains HBase’s write‑ahead‑log architecture, detailing how client put/delete requests travel through RPC to the RegionServer, are processed by MultiRowMutationService, written to the WAL via FSHLog.append and sync, and finally stored in MemStore, while describing durability options and the underlying source‑code call chain.
This article summarizes the latest releases and feature updates in the big data ecosystem—including Hadoop 3.0 Alpha, Druid 0.9.2, Apache Kudu 1.1.0, HAWQ 2.1.0 enterprise—as well as a brief overview of Docker’s 2015‑2016 version history and its adoption status in China.
This article describes Hulu’s real‑world OLAP query optimization, covering the fundamentals of OLAP, comparisons of row‑ and column‑based storage formats, detailed indexing mechanisms of Parquet, ORC and CarbonData, and the specific schema, shuffle, block size, speculation and GC tuning techniques that enabled CarbonData to dramatically accelerate wide‑table queries on SparkSQL.
This guide explains how to use Inceptor's management UI—especially the Jobs and Cluster tabs—to monitor execution metrics, interpret stage details, read DAG visualizations, and diagnose performance issues in large‑scale SQL workloads.
Alibaba’s HiStore column‑store database handles over 6 trillion records and 5 PB of data daily, delivering ultra‑high compression, low hardware cost, and millisecond‑level multidimensional queries for massive e‑commerce and cloud workloads.
The article quantitatively breaks down HDFS NameNode memory consumption, showing that the Namespace tree and BlocksMap together dominate heap usage (≈53 GB in large clusters), provides detailed per‑object size estimates for NetworkTopology, INode and block structures, and proposes a simple formula to predict total heap requirements and tuning recommendations.
In this Alibaba Tech Forum talk, senior expert Wei Hu explains how machine learning and big‑data technologies are used to empower merchants with personalized storefronts, intelligent posters, and AI‑driven headlines, boosting their efficiency and sales performance.
This article surveys twelve leading open‑source big‑data and analytics platforms—including Hadoop, Spark, Talend, and MongoDB—detailing their capabilities, market adoption, and how they fit into modern enterprise data‑driven workflows.
This article presents a comprehensive overview of Ctrip's Aegis risk control system, detailing its modular architecture, rule engine, data service layer, Chloro analytics platform, and future directions, while highlighting the use of streaming, big‑data processing, and machine‑learning models for real‑time fraud detection.
Tencent's Glacier multi‑dimensional analysis platform combines massive user‑portrait tags with routine analytical reports, delivering fast, accurate real‑time queries across countless dimensional combinations, enabling analysts and operators to perform targeted operations and insights as product data continuously evolves.
At Meitu’s third Internet Technology Salon in Xiamen on November 26 2016, over 150 senior engineers heard Meitu’s Lu Rongbin detail the company’s progression from simple rsync scripts to a scalable mobile data and open statistical platform, while Tencent’s Zhao Shiyuan showcased the Glacier multi‑dimensional analysis system for fast, tag‑driven queries, underscoring collaborative technical exchange in South China.
MIT Technology Review reports that Alibaba’s 2022 Double 11 shopping festival set new e‑commerce records while showcasing the company’s advanced big‑data, AI, and cloud‑computing technologies, highlighting massive transaction volumes, high‑quality data processing, robust security measures, and the strategic push toward global digital infrastructure.
This article explains the concept of user profiling, outlines its four-stage construction process, discusses the significance of tagging users, and details practical data modeling techniques—including static and dynamic data sources, weight calculations, and real‑world examples—aimed at improving precision marketing and recommendation systems.
This article introduces Apache Beam, its Beam Model, and how the Beam SDK enables developers to write unified, flexible pipelines for both bounded batch jobs and unbounded streaming workloads, illustrating concepts with mobile‑gaming examples and detailed code snippets.
This article examines Tencent Analytics' architecture, detailing its real-time data collection, in‑memory computation, and NoSQL storage strategies that enable second‑level updates for terabyte‑scale web traffic.
The article describes how Alibaba leverages big data, cloud computing and AI through its “City Brain” project and security platforms to improve urban traffic management, public safety, anti‑fraud measures and e‑commerce risk control, illustrating the transformative impact of data‑driven technologies on modern social governance.
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 describes how Zhihu combats large‑scale spam by applying text and behavior clustering techniques—using Jaccard similarity, SimHash fingerprinting, and Spark‑based graph partitioning—to efficiently identify and group similar spammy content and actions.
The conference featured a series of high‑availability architecture talks covering performance‑driven design, RPC framework resilience, big‑data platform evolution, MySQL cluster consistency, and cloud infrastructure best practices, presented by experts from 58.com, Alibaba, Tencent, Baidu, and others.
The article explores the evolution of data processing from Hadoop and Spark to modern SQL, NoSQL, and NewSQL solutions, comparing their architectures, performance trade‑offs, and use‑cases, while illustrating concepts with examples like MapReduce, Hive, Impala, and streaming platforms such as Storm.
Amid rising data‑storage complexity, twelve innovative startups emerged in 2015‑2016, leveraging flash, disk, and cloud technologies to improve data mobility and management across hierarchical storage tiers, offering solutions ranging from cloud‑native storage networks to SAN arrays and virtualization platforms.
In this interview, Lianjia’s big‑data platform lead Lv Yi discusses his career, the motivations for joining Lianjia, the evolution of the company’s data infrastructure, architectural decisions, and the challenges of scaling a big‑data platform for real‑estate operations.
This article explains how HDFS, inspired by Google’s GFS, provides a low‑cost, highly reliable, fault‑tolerant, and high‑performance distributed file system for big‑data workloads by using replication, standby NameNodes, block storage, rack awareness, and compute‑close‑to‑data strategies.
The article chronicles Taobao’s 13‑year evolution of its big data platform, detailing three phases—from a single‑node Oracle setup and the Tianwang scheduler, through a Hadoop‑based “Cloud Ladder 1” architecture with real‑time analytics, to the current MaxCompute/ODPS era with cross‑region projects and advanced data services.
The article outlines how mobile Internet merges the data‑driven society with the physical world to create a new big‑data architecture and describes the seven distinct camps—Infrastructure, Analytics, Applications, Cross‑Domain Architecture, Open‑Source, Data Sources & APIs, and Incubator & Training—that together form a comprehensive end‑to‑end big‑data solution ecosystem.
The paper presents a production‑grade, low‑cost mobile‑app exception monitoring platform built on Spark Streaming, Kafka, and Elasticsearch that achieves high availability through exactly‑once processing and checkpointing, minute‑level latency by decoupling raw and symbolized logs, high throughput via reservoir sampling, and dynamic scalability without code changes.
The article explores how AI, cloud computing, big data, and advanced logistics power China’s Double 11 shopping festival, turning it into a showcase for cutting‑edge technologies that drive personalization, real‑time analysis, and industry‑wide digital transformation.
In this interview, Sogou architect Liu Jian shares how his team built a highly available, scalable commercial advertising platform, discusses the evolution of its infrastructure, offers practical advice for engineers aspiring to become architects, and reflects on emerging technologies and time‑management strategies.
The article analyzes four major big‑data evolution trends—SQL on Hadoop overtaking hybrid architectures, SSDs becoming cache in Hadoop clusters, the rise of real‑time analytics, and the convergence of cloud computing with big data—while presenting supporting data, predictions, and architectural diagrams.
This article examines the evolution of Taobao's technical architecture—from a LAMP stack through Oracle‑based mainframes to a cloud‑native platform—highlighting the performance, scalability, and cost challenges of traditional IT and offering best‑practice strategies for migrating enterprise systems to the cloud.
Apache Storm is a distributed real‑time stream processing system that ingests data from sources, routes it through spouts and bolts forming a topology, and enables scalable, fault‑tolerant analytics such as filtering logs to HDFS or handling VIP user data, all with a simple programming model.
The article explains Taobao’s five‑layer data product architecture—covering data sources, compute, storage, query, and product layers—and describes how massive volumes of data are ingested, processed in batch and streaming, stored in MySQL and HBase clusters, and served efficiently through a unified middle‑layer and sophisticated caching mechanisms.
This article examines Toutiao’s evolution from a simple news aggregator to a 600‑billion‑RMB valued AI‑driven recommendation platform, detailing its market growth, data‑driven personalization, product features, business model, talent philosophy, and future outlook.
The article explains how modern business operation management integrates cloud computing, big data analytics, and proactive IT monitoring to shift from traditional infrastructure‑centric maintenance to a user‑experience‑driven, data‑powered approach that boosts performance, accelerates growth, and supports digital transformation.
This article shares hands‑on experience from Spark Summit attendees, covering why Spark is powerful, common performance problems such as slow jobs, OOM, data skew, excessive partitions, and provides concrete tuning advice on executors, cores, memory, and debugging techniques.
In his 2016 Yunqi Conference keynote, Wang Jian highlighted how Alibaba’s cloud and AI technologies transform city traffic by linking surveillance cameras to traffic lights, discussed the evolution from Deep Blue to AlphaGo, and reflected on the broader impact of data-driven innovation on society.
This article describes Ctrip's challenges with web security, evaluates hardware and commercial cloud WAF shortcomings, and presents a low‑cost, low‑risk cloud‑based WAF solution that leverages DNS redirection, closed‑loop rule management, Lua/Tengine deployment, supervised machine‑learning log analysis, and big‑data streaming for real‑time threat detection and mitigation.
This article explains how to design and implement heterogeneous data synchronization, leverage batch and stream processing frameworks like Hadoop and Storm for large‑scale analysis, and choose appropriate storage solutions—from in‑memory databases to distributed column‑family stores—while addressing performance, reliability, and monitoring in complex distributed environments.
In his keynote at the Alibaba Cloud Conference, CTO Zhang Jianfeng explains how advances in computing power, AI, big data, and IoT are driving the digital transformation of retail, manufacturing, and services, enabling smarter products, personalized experiences, and a fully connected intelligent world.
In his Yunqi Conference keynote, Alibaba CTO Zhang Jianfeng explains how soaring computing power, digitalization, AI, IoT and immersive technologies will transform retail, manufacturing and services into intelligent, personalized ecosystems, illustrating the vision with examples like a smart golf club and a city‑wide data brain.
This article introduces a programmer‑focused guide to data mining, explaining how personalized recommendation systems work, illustrating historical and modern examples, and outlining the book’s practical Python‑based approach to mastering big‑scale data analysis.
Data warehouses centralize and transform enterprise data for multidimensional analysis, and modern demands have spawned four types—traditional, real‑time, associative discovery, and data marts—each with distinct technical requirements, while Hadoop‑based solutions like Transwarp Data Hub address challenges of scale, variety, latency, and security.
Learn how to quickly set up a multi‑node Hadoop cluster on a single machine using Docker containers, covering image preparation, SSH configuration, fixed IP assignment with pipework, and building custom Hadoop images, enabling a lightweight, cost‑effective big‑data environment for development and testing.
The article introduces Koalas, an automated distributed diagnostic tool for TDH clusters that identifies and resolves computing environment issues—such as network, platform, and system problems—through one‑click checks, detailed reporting, and both preventive and diagnostic use cases.
This tutorial walks you through preparing three Linux servers, configuring password‑less SSH, installing Hadoop 2.7.3, editing core XML files, distributing the installation, starting the services, and verifying HDFS and MapReduce functionality with practical commands and screenshots.
This article presents a detailed case study of Vipshop's log platform during the 419 sales event, analyzing the 2013 architecture, bottlenecks in RabbitMQ and Storm, and the subsequent redesign using Kafka, Impala, and HBase to achieve scalable, reliable big‑data processing.
This article introduces Apache Spark as a fast, general‑purpose big‑data engine, explains its ecosystem, compares HDFS with MongoDB, and demonstrates how Spark can be combined with MongoDB through the Mongo‑Spark connector, including real‑world case studies and sample code.
This article introduces Apache Flume, explains its design goals of reliability, scalability, manageability and extensibility, outlines core concepts and architecture, provides step‑by‑step configuration using the first mode, demonstrates integration with Zookeeper, Kafka and a shell script, and shows how to launch and verify the agent.
This article provides a light‑hearted yet thorough overview of deep learning, covering neural network fundamentals, layer construction, back‑propagation, ResNet shortcuts, encoder‑decoder structures, PU‑learning for unlabeled data, GPU acceleration, and practical advice on data size, frameworks, and deployment in financial scenarios.
Qihoo 360 shares how it grew from a 2011 MongoDB adopter to running over 1500 instances handling 200 billion queries per day, detailing its architecture, key applications, migration decisions, backup strategies, scaling best practices, and lessons for future MongoDB projects.
The article dissects Hadoop 2.4.1’s HDFS NameNode memory architecture, detailing how the Namespace, BlockManager, NetworkTopology, and LeaseManager consume the heap, exposing scaling problems when metadata reaches hundreds of millions of inodes and blocks, and recommending file merging, block‑size tuning, federation, or external KV stores to mitigate heap pressure.
The Ctrip Information Security Salon held on August 20 in Shanghai featured expert talks on cloud‑based WAF, big‑data security analytics, ELK‑driven monitoring, product security practices, and concluded with a recruitment drive for security engineers, showcasing practical implementations and industry challenges.
This article provides a comprehensive, hands‑on tutorial for setting up a Hadoop 2.6.4 cluster on a CentOS 6.5 development server, covering SSH password‑less login, user/group creation, DNS configuration, JDK installation, environment variables, Hadoop installation, HDFS and YARN configuration, and troubleshooting native library warnings.
