This article compiles a comprehensive list of common open‑source tools for big data platforms—covering programming languages, data collection, ETL, storage, analysis, query, management, and monitoring—to help learners and practitioners quickly locate and understand the technologies they need.
The article describes how a large‑scale ETL process that previously used HBaseStorageHandler caused severe region server pressure, and how a new HBase‑to‑Hive task based on SnapshotScanMR was designed to bypass region servers, halve execution time, and double scanning performance.
This article explains how leveraging HBase's SnapshotScanMR feature to create a custom hbase2hiveBySnapshot task dramatically reduces region server pressure, halves ETL execution time, and improves cluster stability for large‑scale data back‑fill operations.
The article explains how data serves as a critical asset for recommendation systems, outlining the necessary steps from understanding business problems and data dimensions to collection, cleaning, integration, and analysis, while distinguishing explicit and implicit user feedback and emphasizing data quality, timeliness, and relevance.
This article presents a comprehensive case study of the Dada‑JD Daojia big data platform, detailing its evolution from a MySQL‑based warehouse to a multi‑layered One Data, One Platform, One Service, Many Apps architecture, the technical challenges faced, and the strategic approaches adopted to ensure coverage, accuracy, stability, and scalability.
Youzan replaced Sqoop with a customized DataX‑based platform that integrates with its offline scheduler to reliably sync MySQL, HBase, Elasticsearch and file sources to Hive, handling schema changes, sharding, rate‑limiting and logging, and has processed billions of rows daily with high stability.
This article details Zhihu's evolution of its real-time data warehouse, covering the 1.0 version built on Spark Streaming, the 2.0 upgrade using Flink Streaming SQL, architectural layers, ETL processes, and future directions such as streaming SQL platformization and automated result validation.
This article explains Zhihu's journey from ad‑hoc MySQL‑Hive sync using Oozie + Sqoop to a unified, platform‑based data synchronization service that now handles thousands of tables, over 10 TB daily, with load‑aware scheduling, incremental pulls, schema change handling, and tight integration with their offline job scheduler.
This article introduces the fundamentals of Business Intelligence, explains what ETL and data warehouses are, compares them with traditional databases, and outlines the main characteristics and popular tools such as Hive used in modern big‑data environments.
This article explains how Baidu Cloud's Noah intelligent operations product builds a unified operations knowledge base by categorizing metadata, status, and event data and applying three ETL approaches—Pull, Push, and Lazy—to handle offline, near‑line, and real‑time data integration.
The paper outlines the three‑generation evolution of a video‑platform operational backend—from Apollo to Eight—meeting cross‑business integration, low‑cost development, and user experience goals by employing a metadata‑driven ETL layer, a unified business‑object model, and a componentized UI within a micro‑kernel, plugin‑based architecture, delivering decoupling, rapid configuration, data safety, and dynamically generated pages, while future work expands UI components, source support, deep‑operation features, and PaaS/open‑source release.
This article details the evolution of the FunData esports data platform from a simple master‑slave ETL system to a cloud‑native, distributed architecture that leverages Google Cloud Pub/Sub, Dataflow, Bigtable, and a redesigned API layer to handle petabyte‑scale, real‑time DOTA2 match data.
This article provides a comprehensive overview of big data fundamentals, including the 4V characteristics, the Hadoop 2.0 layered architecture, a comparison between Hadoop and Spark, classification of common big‑data tools, and the typical offline and real‑time data processing workflows.
This article explains why BI is essential for big data platforms, outlines the value hierarchy of data, details the Hadoop‑based analysis workflow, and provides step‑by‑step guidance for constructing both pure Hadoop and hybrid Hadoop‑Spark analytics architectures.
Meitu’s data platform, serving dozens of apps with 500 million monthly active users and billions of daily events, combines the Arachnia log‑collection system, Kafka ingestion, multi‑layer storage (HDFS, MongoDB, HBase, Elasticsearch), offline Hive/MapReduce processing and real‑time Storm/Flink/Naix pipelines, supported by data‑workshop tools, staged evolution for scalability, and robust security and query‑validation mechanisms.
Apache Hive is an open‑source data‑warehouse system built on Hadoop that enables users to read, write, and manage large distributed datasets using SQL‑like queries, offering features such as ETL support, various file‑format connectors, extensible UDFs, and integration with tools like Tez, Spark, and MapReduce.
The article describes the evolution of 360's Titan big‑data processing platform through three architectural stages, details its functional modules, explains the DITTO component framework, context and rule‑engine abstractions, and shares practical case studies and personal insights on building a flexible, self‑service data platform.
This article introduces Yugong, an open‑source ETL framework for heterogeneous database migration, explains its core Extractor‑Translator‑Applier architecture, details key classes and interfaces, discusses limitations of the original version, and describes extensive refactoring and new features added to support SQL Server, MySQL, and Canal‑based incremental replication.
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.
This article details the challenges of moving from SQL Server to MySQL, compares offline and online migration approaches, presents a complete toolbox—including ETL utilities, consistency checkers, CDC configuration, and rollback mechanisms—and shares practical lessons from a large‑scale production migration.
This article details the background, design considerations, migration workflows, tooling choices, data consistency verification, rollback mechanisms, and practical experiences of moving a large‑scale production environment from Microsoft SQL Server to MySQL, covering both offline and online migration scenarios.
This article details the design, challenges, and implementation of Meituan’s Flow Compass—a data‑driven product that combines user, scene, and traffic source dimensions using a Kylin‑based warehouse to enable rapid, flexible traffic‑source analysis for hotel‑travel growth.
This article describes Qunar's end‑to‑end user behavior data pipeline, covering offline and real‑time ETL processes, system architecture, Dubbo service interfaces, monitoring, optimizations, and the numerous product applications that leverage the unified behavior dataset.
The article introduces data-ci, a SQL‑driven unit testing framework that lets engineers write, organize, and automate data validation tests for ETL pipelines, providing assertions, failure callbacks, coverage reporting, and CI integration to improve data quality and reliability.
etlpy is a lightweight Python framework that lets you define web‑crawling and data‑cleaning pipelines via XML, using generators for streaming, built‑in thread pools for parallelism, and a plug‑in architecture that handles everything from regex parsing to JSON conversion, all within a single 500‑line core file.
This talk details Baidu Waimai's end‑to‑end ETL design, covering demand sources, data flow patterns, multi‑stage system evolution, storage choices, scheduling architecture, configuration‑driven processing, quality monitoring, and how data lineage enables transparent, self‑service data delivery.
In this talk, a Baidu Waimai engineer explains the motivations, requirements, and architectural choices behind their open‑source ETL platform, covering data flow patterns, logical mappings, storage options, scheduling, metadata management, and quality monitoring to achieve scalable, transparent, and explainable data delivery.
This article details how Suning built a Hadoop‑based big data platform and leveraged Apache Spark to process terabytes of product price and inventory data, describing the system architecture, four key technical practices, performance results, and future data‑lake directions.
This article examines the principles, features, and implementation details of distributed scheduling for big‑data ETL pipelines, covering decentralised schedulers, host selection strategies, fault‑tolerance, operator abstraction, elasticity, trigger mechanisms, visual monitoring, alarm handling, data fan‑in/fan‑out, parameter consistency, real‑time quality checks, lineage tracking, and field‑level traceability.
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 details the architectural choices, component selection, performance tuning, and team organization behind Ctrip's big‑data platform, covering Kafka, Presto, Elasticsearch, Gobblin, Zeppelin, REST APIs, and job scheduling to achieve scalable, interactive data analysis and visualization.
This article summarizes Xu Peng's DAMS 2017 presentation on selecting big‑data platform components, designing ETL pipelines, choosing analysis engines, optimizing Elasticsearch, and building a data‑driven team at Ctrip.
The article introduces Transwarp Transporter, a near‑real‑time ETL tool for TDH 5.x, explains its architecture, visual dashboard, drag‑and‑drop data‑flow design, debugging features, parameter management, and highlights how it empowers business users to achieve fast, reliable data migration in big‑data environments.
This article provides an extensive overview of more than ninety open‑source and commercial big‑data tools—including ETL platforms, resource managers, storage systems, messaging queues, processing engines, and visualization libraries—detailing their core functions, typical use cases, and notable adopters.
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 shares practical experiences and step‑by‑step guidance for migrating databases from Oracle to MySQL, covering pre‑migration preparation, target selection, data‑object migration tools such as SQL LOAD, Python scripts, Oracle GoldenGate, MySQL Migration Toolkit and Kettle, handling of views, triggers, stored procedures, data validation techniques, and key MySQL performance parameters.
This article shares actionable ETL tuning strategies for Inceptor, including avoiding unnecessary transaction tables, shortening transaction windows, analyzing the most impactful cases first, and iteratively refining steps until optimal performance is achieved.
Effective ETL optimization is crucial for data warehouse construction, and this guide outlines three core strategies—ensuring proper resource configuration, leveraging data characteristics for optimal partitioning and bucketing, and monitoring task execution—providing practical principles, pitfalls, and case studies to maximize ETL efficiency.
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.
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.
This article examines the core technologies of big data—including data collection, storage, management, analysis, and mining—highlighting architectural challenges, analysis techniques, storage solutions, ETL processes, and the interplay between big data and cloud computing, while emphasizing practical implementation considerations.
This article provides a comprehensive overview of Oracle GoldenGate, detailing its supported databases, modular architecture, key components such as Extract, Data Pump, Replicat, Trails, Checkpoints, Manager and Collector, as well as processing types, group configuration, and commit sequence numbers for reliable data replication.
The talk shares hands‑on experiences and best‑practice methods for traditional data‑warehouse processing, public and behavioral data handling in big‑data environments, and practical guidance for migrating legacy warehouses to modern Hadoop‑based platforms, emphasizing data governance, security, and performance optimization.
This article describes how Meituan migrated from Hive‑SQL and MapReduce to Spark on YARN, built an interactive Zeppelin‑based development platform, created reusable ETL templates, constructed a Spark‑driven feature and data‑mining platform, and applied Spark to interactive user‑behavior analysis and large‑scale SEM services, highlighting performance gains and operational benefits.
This article details Asana's evolution from a simple Python‑MySQL setup to a robust, scalable data platform using Redshift, Hadoop, Luigi, and modern BI tools, highlighting challenges, solutions, and lessons learned for building reliable data pipelines in fast‑growing startups.
Facing rapid growth, Asana overhauled its data infrastructure—from a single‑machine MySQL setup to a Redshift‑backed warehouse, Hadoop‑based log processing, Luigi orchestration, and self‑service BI tools—highlighting the challenges, solutions, and future plans for scalable, reliable analytics.
This article chronicles the step‑by‑step evolution of Dazhong Dianping’s data platform from 2012 to 2014, detailing changes in data models, storage and compute architecture, scheduling, monitoring, and data‑driven applications, offering practical insights for teams building early‑stage big‑data infrastructures.
This article provides a concise, practical walkthrough for installing and configuring Apache Hive on a Hadoop cluster, covering prerequisite HDFS and MapReduce setup, downloading Hive, extracting files, setting environment variables, configuring XML files, starting Hive, and verifying the installation with simple commands.
