0 views collected around this technical thread.
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.
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 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 presents Didi's comprehensive big data cost governance approach, detailing the overall framework, data system architecture, asset management platform, Hadoop and Elasticsearch cost‑control practices, metadata‑driven optimization, and organizational insights for effective resource and budget management.
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.
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 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 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 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 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.
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.