Uber tackled the challenge of replicating over 350 PB of data across on‑premise and cloud lakes by redesigning Hadoop Distcp, moving intensive tasks to the Application Master, parallelising copy‑listing and commit phases, and leveraging Uber‑mapper jobs to dramatically cut latency and improve resource efficiency.
A recent HDFS outage caused the Standby and Observer NameNodes to crash after heavy client load triggered block recovery failures, exposing a bug in commitBlockSynchronization that leads to mismatched block IDs and edit‑log inconsistencies, which can be fixed by applying HDFS‑17861.
This article compiles three interview rounds for a data warehouse engineer role, covering fundamental concepts, practical skills, and leadership thinking with detailed Q&A on ETL, Hadoop components, schema design, data quality, data lake vs. warehouse, ACID properties, cloud solutions, SQL optimization, real‑time processing, security, and team management.
This guide walks you through every essential topic—from data warehouse architecture and layering, through ETL, OLAP, Hadoop, and Flink, to visualization tools, learning paths, recommended resources, and the management skills needed to become a proficient data warehouse architect.
This article details Beike's data‑fusion architecture evolution, covering industry trends, multi‑stage Hadoop upgrades, storage cost optimization with erasure coding, remote shuffle integration, GPU‑centric training stability, and future hybrid‑cloud strategies, while also sharing organizational and operational lessons learned.
This guide outlines a comprehensive Hadoop learning roadmap, covering essential prerequisites, core concepts such as HDFS, MapReduce, and YARN, hands‑on projects, advanced ecosystem tools like Hive, Pig, HBase and Spark, plus curated resources and community channels for aspiring big‑data engineers.
This article explains the origins of big‑data technologies, details the architecture and read/write mechanisms of Hadoop's HDFS, describes the MapReduce programming model, and provides complete Java code examples for a simple distributed file‑processing job using Maven dependencies.
This article explains Hadoop’s core concepts using a library analogy, details HDFS storage and MapReduce processing, provides complete Java implementations for a word‑count job with support for text, CSV, and JSON inputs, and discusses extensibility and performance optimizations such as combiners and custom partitioners.
The article examines the concepts of storage‑compute separation and the lake‑warehouse (lakehouse) model, tracing their evolution from physical Hadoop clusters to containerized compute and object storage, and argues that true separation requires MPP systems to adopt open standards, effectively merging lake and warehouse architectures.
This article presents Qichacha's comprehensive data‑cost‑reduction strategy, detailing its Hadoop‑based three‑pillar architecture, layered data warehouse, Hive upgrades, unified metadata across multi‑cloud clusters, middleware choices such as Alluxio and JuiceFS, version‑compatible hybrid clouds, and Kubernetes‑driven resource orchestration to achieve scalable, low‑cost data processing.
This article compiles a wide range of interview questions covering JVM garbage collection, Hadoop, Hive, Flink, HBase, data warehousing, real‑time processing, and HR topics, providing a thorough preparation guide for candidates targeting senior big‑data positions.
This article introduces the fundamentals of distributed computing, compares major frameworks such as Hadoop, Spark, and Flink, and outlines their key components, performance characteristics, and typical application scenarios including big‑data analytics, cloud services, real‑time streaming, and scientific computing.
This article details the background, planning, step‑by‑step procedures, encountered issues, and rollback strategies for upgrading a Hadoop HDFS cluster from version 2.6.0‑cdh to 3.1.2, including mixed‑deployment of DataNodes across different federations and necessary configuration changes.
This article introduces Tencent's Big Data Processing Suite (TBDS), discusses challenges of data silos, and presents Gravitino's open‑source unified metadata service and permission model, detailing how it integrates Hadoop, MPP, and various catalog plugins to provide consistent access control across heterogeneous data platforms.
This article details Didi's comprehensive big data cost governance framework, covering its data architecture, asset management scoring, Hadoop and Elasticsearch cost optimization methods, and practical insights on organizational processes and incentives for effective cost control.
The article details how repeatedly calling FileSystem.get(uri, conf, user) created distinct UserGroupInformation objects, inflating the static FileSystem cache and causing a heap‑memory leak that triggered an Out‑Of‑Memory error, and explains that using the two‑argument get method or explicitly closing instances resolves the issue.
This guide walks through planning a three‑node Hadoop 3.3.5 cluster, explains default and custom configuration files, details core‑site, hdfs‑site, yarn‑site, and mapred‑site settings, shows how to distribute configs, start HDFS and YARN, and perform basic file‑system tests.
This article details Didi's comprehensive big‑data asset governance platform, covering its architectural layers, Hadoop and Elasticsearch governance practices, health‑score models, lifecycle recommendations, and future plans for automated and intelligent data governance to reduce cost and manual effort.
This article presents a comprehensive overview of Ozone, a Hadoop‑based distributed object storage system, detailing its architecture, metadata management, scalability enhancements, small‑file handling, erasure coding, lifecycle policies, and future improvements aimed at boosting performance and reliability for large‑scale unstructured data workloads.
This article explains what a data lake is, its origins, key characteristics such as collecting all data, enabling diverse user access, and flexible processing, compares it with traditional data warehouses, discusses cost advantages, potential pitfalls like data swamps, and outlines best‑practice considerations for enterprise adoption.
This step‑by‑step guide shows how to install and configure a single‑node Hadoop 3.2.0 environment on CentOS 7, set up Python 3.7, write MapReduce mapper and reducer scripts in Python, and run a word‑count job using Hadoop streaming, illustrating core Hadoop concepts and their relevance today.
This tutorial walks you through Hadoop’s core components, sets up a single‑node Hadoop cluster on CentOS 7, installs Python 3, writes mapper and reducer scripts in Python, and runs a Hadoop‑Streaming word‑count job to demonstrate classic big‑data processing techniques.
Data lakes and data warehouses serve different purposes in big‑data architectures; this article explains their definitions, core attributes, five major distinctions—including data retention, type support, user coverage, adaptability, and insight speed—and offers guidance on selecting or combining the two approaches.
This article offers a concise MVP learning plan for recent graduates aiming to secure a big‑data internship, covering essential computer fundamentals, core big‑data frameworks, project ideas, and algorithm/SQL practice, along with practical study tips and resource recommendations.
This guide explains why HDFS enters safe mode after a DataNode failure, describes the safe‑mode state and its exit conditions, and provides step‑by‑step commands and troubleshooting procedures to analyze, fix, and recover from safe‑mode incidents in Hadoop clusters.
This article provides a comprehensive overview of big data, covering its origins, definitions, 5V characteristics, data formats, real‑world applications, Hadoop architecture, testing challenges, functional and performance testing strategies, and the skills required for effective big data testing.
This article compiles real interview experiences from a top tech firm and other leading companies, presenting a detailed list of common big‑data interview questions covering Hadoop, Hive, Spark, Flink, Kafka, data skew, HDFS architecture, and related concepts to help candidates prepare effectively.
This comprehensive tutorial details the process of deploying a complete Hadoop-based big data ecosystem on ARM architecture, covering the installation and configuration of essential components including Java, Zookeeper, Hadoop, MySQL, Hive, and Spark with practical code examples.
This article analyzes the rise of lake‑house architecture in the Hadoop ecosystem, compares the technical capabilities of Hudi, Iceberg and Delta Lake, details implementation enhancements such as MOR and multi‑writer support, showcases Flink integration, presents a real‑time marketing use case, and outlines future development directions.
This article presents a comprehensive case study of NetEase Cloud Music's data governance practice, covering data background, governance philosophy, detailed solutions across metadata, storage, compute, and model design, practical implementations, measurable cost savings, and future planning for sustainable data management.
This article examines the limitations of traditional physical‑server Hadoop clusters and explains how adopting cloud‑native technologies, distributed object storage, and compute‑storage separation can improve resource utilization, disaster recovery, performance, security, observability, and cost efficiency for large‑scale big data workloads.
This article shares a recent graduate's successful job offer story, emphasizes preparing a detailed personal project summary, and provides extensive big‑data interview questions covering Hadoop, Spark, Flink, Kafka, Hive, ClickHouse, and related technologies to help candidates excel in interviews.
This article compiles extensive big‑data interview questions from companies like Bilibili, ByteDance, Ant Group, and Tencent, offers practical advice on project depth, open‑source contributions, and provides strategic insights for recent graduates navigating a tightening job market.
This article analyses the security of Hadoop S3A write paths in data lakes, explains fast upload mechanisms, demonstrates disk‑IO and network‑error simulations, compares checksum algorithms, and presents Alibaba Cloud EMR JindoSDK best‑practice results with performance and reliability evaluations.
With data volumes soaring, traditional Hadoop and Spark clusters become costly and cumbersome for small to medium workloads, prompting many to seek lighter alternatives; this article examines the technical, operational, and financial burdens of Hadoop/Spark and introduces the open‑source SPL engine as a fast, low‑cost, easy‑to‑use big‑data solution.
This comprehensive tutorial walks you through setting up a three‑node Hadoop 3.3.4 and Spark 3.3.1 environment on CentOS 7 virtual machines, covering system preparation, JDK and Scala installation, Zookeeper configuration, Hadoop and Spark deployment, and verification with practical command‑line examples.
This article outlines the evolution of big data, defines the role of platform data product managers, details their core competencies—including general, professional thinking, and technical skills—covers the Hadoop ecosystem, and explains the end‑to‑end offline data‑warehouse construction process with practical examples and Q&A.
This article reviews the history of data platforms—from the first general‑purpose computers and early relational databases through traditional BI, agile BI, and big‑data technologies like Hadoop, Spark, and Flink, up to today’s cloud‑native modern data stack and its future outlook.
This document outlines a comprehensive big‑data platform construction plan for a financial enterprise, describing the current data challenges, objectives, three‑layer architecture, recommended commercial Hadoop solution (TDH), detailed model‑design steps, implementation schedule, hardware/software specifications, and key success factors.
This article presents the background, requirements, architecture, key modules, and practical impact of OPPO's non‑intrusive big‑data diagnostic platform—named Compass—designed to quickly locate issues, provide optimization suggestions, and achieve cost‑saving and efficiency gains for large‑scale Spark and Hadoop workloads.
This article details NetEase Cloud Music's year‑long data governance initiative, covering data background, governance strategy, project plan, practical actions, results, and future outlook, and shows how metadata‑driven management reduced storage by over 30% while improving reliability and efficiency.
This guide walks through deploying Hadoop 3.x on a Kubernetes cluster using Helm, covering repository addition, Docker image creation, Helm chart configuration, service adjustments, installation, verification commands, and clean uninstallation, complete with code snippets and screenshots.
This article details a systematic investigation of a Hadoop NameNode/DataNode that showed high memory usage at 3 AM, identifies zombie crond/sendmail/postdrop processes caused by a failed Postfix service, and provides cleanup commands and preventive measures for memory, disk, and inode issues.
This article explains how to overcome performance bottlenecks of a rapidly growing blog by progressively enhancing the traditional Nginx‑MySQL stack with load‑balanced app servers, Redis caching, read/write splitting, MySQL partitioning, MyRocks, and finally a hybrid NoSQL‑big‑data architecture using Hadoop and HBase.
The 2022 Open Source Big Data Heat Report, released at the Yunqi Conference, analyzes 102 active projects, discovers a 40‑month “Moore’s law” doubling of project heat, highlights three major trends—diversification, integration, and cloud‑native—and ranks the top 30 hottest open‑source big‑data projects.
This article explains how Ozone, the Hadoop community’s new distributed object‑storage system, overcomes HDFS’s small‑file limitations with a hierarchical Volume‑Bucket‑Object model, detailing its architecture, components, data flow for creating and reading objects, and the benefits of its scalable, fault‑tolerant design.
This article provides a comprehensive overview of Hadoop, covering its history, core features, the HDFS storage framework, MapReduce computation engine, YARN resource manager, real‑world application scenarios, and the surrounding ecosystem of tools such as Hive, Spark and Kafka.
This tutorial walks you through deploying Hadoop 3.x on a Kubernetes cluster using Helm, covering repository setup, Docker image creation, Helm chart customization, service configuration, installation, verification, and clean‑up, with all necessary commands and YAML snippets.
This article shares Xiaomi's internal practices of Hadoop YARN, covering scheduling and resource optimization, elastic scheduling, node overcommit handling, federation architecture, metadata warehouse construction, and future plans to improve cluster utilization and cost efficiency.
This article outlines the configuration settings for Apache Iceberg tables and catalogs on Hadoop, covering read and write properties, combine behavior for small HDFS files, reserved table properties, catalog lock options, and Hive Metastore connector Hadoop settings, supplemented with illustrative screenshots.
This guide explains how to download JDK, Maven, Scala and Spark 3.3.0, modify the Spark pom and configuration files for CDH 6.3.2, compile Spark with Maven, deploy the binaries to a client node, set up spark‑sql and spark‑submit scripts, and address common runtime issues.
This article provides a comprehensive tutorial on setting up Flink 1.11 with Iceberg 0.11.1, creating Hive catalogs, building databases and tables, inserting data, and exploring Iceberg components, file structures, partitioned tables, execution plans, and programmatic access via Scala.
This article details Hulu's five‑year journey from Hadoop 2.6 to 3.3.2, covering major feature evolutions, the original cluster architecture, a comprehensive upgrade plan, compatibility challenges across HDFS, YARN, Hive, Spark and Flink, and the testing and rollout strategies that ensured a smooth migration.
Shilong Fei from Xiaomi Data Platform presents an in‑depth exploration of elastic scheduling for Hadoop YARN, covering background, design of resource pools, auto‑scaling architecture, challenges such as job stability and user transparency, achieved cost reductions, and future plans for further optimization.
This article explains the main layers of a typical big data platform—data collection, storage and analysis, sharing, and application—detailing common tools such as Flume, DataX, Hive, Spark, SparkSQL, Impala, and Spark Streaming, and discusses task scheduling and monitoring in the ecosystem.
This talk presents Xiaomi's design and deployment of an elastic scheduling system for Hadoop YARN, covering background analysis, resource‑pool strategy, auto‑scaling architecture, stability challenges, label‑based resource isolation, Spark shuffle handling, cost‑saving results and future plans.
This article details Bilibili's end‑to‑end Presto on Hadoop architecture, covering the multi‑engine SQL stack, dispatcher routing, cluster scale, stability enhancements like coordinator HA and real‑time punish, query limits, Hive UDF compatibility, insert‑overwrite support, Alluxio caching, multi‑datacenter routing, query result caching, Raptorx local cache, JDK upgrades, dynamic filtering, and future roadmap, illustrating how these innovations boosted query throughput and reduced latency.
This article details the end‑to‑end process of migrating a 10,000‑node offline data‑warehouse from CDH 5.14.4 (HDFS 2.6.0) to HDP 3.1.4 (HDFS 3.1.1), covering version selection, rolling‑upgrade strategy, incompatibility fixes, client handling, tool coexistence, testing, automation, and lessons learned.
This comprehensive article explains big data concepts, definitions from Gartner and IBM, real‑world use cases, the Hadoop ecosystem architecture, and detailed introductions to HDFS, MapReduce, YARN, Hive, and HBase, including practical examples and shell commands.
This guide explains how to set up the Flink SQL client to work with Apache Iceberg, covering Scala version requirements, downloading and deploying Iceberg jars, configuring Hive and HDFS catalogs, creating databases and tables, performing insert and overwrite operations, and querying data in both batch and streaming modes.
This article details Bilibili's end‑to‑end Presto‑based SQL‑on‑Hadoop architecture, covering overall system components, query routing, Presto feature set, extensive stability and availability enhancements, performance boosts through caching and multi‑datacenter deployment, and outlines future development plans.
Bilibili’s Presto on Hadoop combines a multi‑engine offline platform with Kubernetes‑managed YARN scheduling, Ranger security, and a custom dispatcher, scaling to over 400 nodes handling 160 k daily queries on 10 PB, while adding coordinator HA, resource‑group punishment, query limits, Alluxio caching, dynamic filtering, and numerous SQL‑level enhancements, with future auto‑scaling and materialized‑view automation.
Bilibili transformed its YARN CapacityScheduler from a heartbeat‑driven design to a multi‑threaded global scheduler by separating lock handling, adopting Weighted Round‑Robin with DRF, adding batch node selection, fixing proposal inconsistencies, tuning GC and logging, and thereby reduced application allocation time by about 38 % on clusters of up to 8,000 nodes.
This article examines how LinkedIn tackled severe scheduling slowdowns when its Hadoop YARN cluster grew to nearly 10,000 nodes, analyzes the root causes of resource‑manager bottlenecks, and describes the fairness‑redefinition and scheduling‑logic patches that restored throughput and scalability.
The Apache Ambari project, once a leading web‑based management and monitoring tool for Hadoop clusters, has been officially retired and moved to the Apache Attic after a unanimous community vote, marking the end of its development despite continued access to its website, source code, and JIRA.
This tutorial explains how to set up the Hue web UI on a CentOS 7 machine by installing required dependencies, compiling Hue, configuring HDFS, YARN and Hive integration files, starting Hive services, launching Hue, and accessing the interface, with all commands and configuration snippets provided.
This tutorial provides step‑by‑step instructions for installing Hadoop 3.1.1, Homebrew, Hive, and configuring MySQL as Hive's metastore on macOS, including environment variable setup, hive‑site.xml configuration, MySQL connector placement, schema initialization, and verification commands.
AutoKH is a cloud‑native mixed‑workload framework that integrates Kubernetes and Hadoop to dynamically schedule online and offline tasks, improve CPU and memory utilization, enforce priority classes, and ensure service stability through operators, CronHPA, and resource‑control components.
This article provides a detailed, step-by-step tutorial for installing Hadoop 2.9.2, configuring environment variables, editing XML configuration files, formatting the NameNode, starting HDFS and YARN services, testing the cluster, and setting up the MapReduce history server on a three‑node Linux environment.
This article presents a detailed interview-style walkthrough covering Hadoop cluster setup, HDFS components, MapReduce workflow, YARN advantages, Spark fundamentals, Kafka replication, Hive table types, and related big‑data concepts, providing concise explanations and practical insights for data engineers.
This article explains the four locations where java.lang.OutOfMemoryError can occur in Hadoop's MapReduce framework—client, ApplicationMaster, Map, and Reduce phases—and provides configuration adjustments and best‑practice solutions to mitigate each type of OOM issue.
This article details the background, challenges, and step‑by‑step solutions for migrating over a hundred petabytes of Hadoop HDFS data across data centers within a month, covering strategy selection, code modifications, balance optimization, and tool enhancements.
The article analyzes why HiveServer2 experiences JDBC connection failures and task execution stalls under high concurrency, reproduces the issues using GC monitoring and large join queries, and presents memory‑ and GC‑tuning solutions including server migration and JVM parameter adjustments to improve stability.
This article presents a detailed learning roadmap for aspiring big‑data experts, covering foundational programming languages, data structures, Linux basics, databases, distributed system theory, and essential frameworks such as Hadoop, Spark, Flink, Kafka, and provides curated B‑site video links and reference materials.
This article provides a comprehensive guide to designing and implementing a big‑data platform, covering architecture overview, data ingestion with Flume, storage on HDFS/Hive/HBase, processing engines such as Hive, Spark and Flink, scheduling solutions like Azkaban and Airflow, and the construction of self‑service analytics systems.
This article details Baicaowei's migration from an IDC‑hosted Hadoop cluster to a cloud‑native data lake on Alibaba Cloud, outlining the business drivers, pain points of the legacy platform, architectural goals, design principles, solution selection, implementation steps, and future outlook for the new big‑data ecosystem.
The article explains what a data lake is, compares various vendor definitions, outlines its four essential components, describes three evolutionary architecture stages from self‑hosted Hadoop to cloud‑native storage‑compute separation, and discusses the benefits and challenges of adopting data lake solutions in modern big‑data platforms.
This article details Tongcheng Travel’s month‑long, zero‑downtime migration of hundreds of petabytes of Hadoop HDFS and YARN clusters across data centers, describing the background, migration strategies, lessons learned, tool enhancements, and future plans to improve data locality, balance, and monitoring.
The article describes how Yixin Group’s product team created an in‑house intelligent data warehouse using Hadoop, Flink/Spark, and standardized data services to transform scattered automotive‑finance data into a secure, scalable platform that supports real‑time analytics and drives business growth.
LinkedIn’s engineers detail how they repeatedly doubled their Hadoop cluster to over 11,000 nodes, tackled YARN scheduling delays caused by workload imbalances, and created the DynoYARN simulation tool to predict performance impacts of massive scaling.
The "God of Big Data" project, launched in 2019, offers a detailed learning roadmap, systematic column resources covering Hadoop, Spark, Kafka, and more, and invites engineers transitioning from backend to big‑data development to follow curated articles, GitHub code, and CSDN tutorials.
This article breaks down a typical big data platform architecture into its four layers—data collection, storage and analysis, sharing, and real‑time computation—detailing the essential tools such as Flume, HDFS, Hive, Spark, DataX, and task scheduling systems that enable scalable, low‑latency data processing and delivery.
This article explains the typical architecture of a big‑data platform, detailing its four core layers—data collection, storage & analysis, data sharing, and application—and describing the key technologies such as Flume, DataX, HDFS, Hive, Spark, Spark Streaming, and task scheduling components.
This article provides a comprehensive guide to optimizing Hadoop HDFS storage through erasure coding and heterogeneous storage policies, explains fault‑tolerance techniques such as safe mode and slow‑disk monitoring, and shares practical MapReduce performance tuning and enterprise‑level configuration examples for large‑scale clusters.
This article explains how a 10 GB gzip file is stored and processed on HDFS, details the MapReduce split calculation using GzipCodec, and discusses why Spark reads such non‑splittable files with a single task, recommending file splitting or format conversion for better performance.
The interview with Kuaishou senior architect Zhao Jianbo details the three‑phase evolution of its trillion‑scale big data platform, covering foundational Hadoop services, real‑time and OLAP extensions, deep customizations, Spring Festival Gala challenges, scheduling innovations, Hadoop usage, and the relationship between big data and cloud architectures.
This article explains how Hadoop revolutionized big data by providing a distributed architecture with HDFS for storage and MapReduce for processing, outlines its ecosystem components, describes the inner workings of HDFS and MapReduce, and discusses the strengths and limitations of this approach.
This article explains how Hadoop's MapReduce shuffle uses a circular buffer data structure to store serialized key/value pairs and their metadata in memory, describes its initialization, write path, spill handling, and the underlying algorithms that ensure efficient in‑memory sorting and disk spilling.
This article outlines a typical big data platform architecture, detailing its core layers—data acquisition, storage and analysis, sharing, application, real‑time computation, and task scheduling—while introducing key technologies such as Flume, HDFS, Hive, Spark, DataX, and monitoring considerations.
The article outlines a typical big data platform architecture, detailing its core layers—data collection, storage and analysis, sharing, application, real-time computation, and task scheduling—while describing key technologies such as Flume, DataX, HDFS, Hive, Spark, Spark Streaming, and Redis.
This article explains Hadoop's data splitting concepts, the distinction between HDFS blocks and logical InputSplits, details the source code of various InputFormats such as TextInputFormat, CombineTextInputFormat, KeyValueTextInputFormat, NLineInputFormat, and custom InputFormats, and provides complete Java examples for Mapper, Reducer, and driver classes.
This article provides a comprehensive introduction to Apache Parquet, covering its origins, columnar storage advantages, nested schema support, internal architecture, storage model components, comparison with ORC, and practical tools for inspecting Parquet files.
Kudu is an open‑source columnar storage engine from Cloudera that combines high‑throughput batch processing with low‑latency random reads, offering features such as C++/Java APIs, Raft‑based replication, flexible consistency, partitioning, and integration with Hadoop, Spark, Impala, and other ecosystem components.
This guide walks through installing Hadoop on Windows, configuring environment variables and XML files, adding the required winutils binaries, verifying the setup with HDFS shell commands, and then building a Maven project that uses the Java API to list and inspect files in HDFS.
This guide explains how to use the HDFS shell (preferred via hdfs dfs) to list, copy, move, delete, and snapshot files in a Hadoop cluster, detailing command syntax, URI handling, generic options, and practical examples for each operation.
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.
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 provides a comprehensive overview of Hadoop’s core components—including MapReduce programming model, HDFS storage architecture, and YARN resource management—while discussing common challenges like data skew and small files, and offering learning resources for aspiring big‑data engineers.
This article walks you through the complete installation and configuration of UCloud's free USDP (UCloud Data Platform) on a three‑node CentOS 7.2‑7.6 cluster, covering environment preparation, package download, repair scripts, MySQL setup, service startup, web UI activation, monitoring, and a quick Hive query example.
