This article provides an extensive introduction to data warehouses, covering their origins, development, definition, advantages, components, comparisons with databases, ODS and data marts, architectural approaches, modeling techniques, and dimensional modeling processes for enterprise‑level analytics.
Ctrip’s hotel data team tackled a 3 TB daily data load by building a ClickHouse cluster on VMware, creating custom sync and execution tools, applying query optimizations, and handling merge and memory errors, ultimately achieving over 400% performance gains across multiple reporting themes.
Lyft transformed its legacy data pipeline by designing a cloud‑native, Flink‑based near real‑time analytics platform that ingests billions of events, writes Parquet files to S3, leverages Presto for interactive queries, and implements multi‑stage non‑blocking ETL, fault‑tolerant back‑fill, and extensive performance optimizations.
This article introduces Apache Hudi, explaining its core concepts, design principles, table architecture, write and compaction mechanisms, and the three query modes that enable efficient batch and incremental processing on modern data lakes.
This article examines the pain points of traditional data warehouse platforms, explains the core concepts and advantages of the Iceberg data lake table format, compares it with Metastore, reviews the current Iceberg community ecosystem, and details NetEase’s practical integration with Hive, Impala, and Flink to improve ETL efficiency and support unified batch‑stream processing.
The talk explores the current pain points of AI testing, outlines data‑quality analysis methods, highlights critical ETL and model‑testing considerations, and shares practical case studies and platform designs to improve machine‑learning quality assurance.
The article details Meituan Waimai's offline data warehouse evolution from its initial V1.0 design through V2.0 improvements to the V3.0 modeling‑tool driven architecture, covering the four‑layer framework, Spark‑based ETL, data governance processes, resource optimization, security measures, and future development plans.
This article outlines the data development lifecycle, distinguishes it from application development, details the responsibilities and focus areas for data testers, and presents a comprehensive end‑to‑end quality monitoring and alert system for big‑data pipelines.
This article explains why ETL is a critical step in building data warehouses, introduces eight core ETL algorithms—including full delete/insert, upsert, append, and various link‑table models—describes their ideal use cases, and provides ready‑to‑run SQL code examples for each.
DiDi’s Data Channel Service evolved from a fragmented component system into a unified, SLA‑driven platform with a UI‑based Sync Center and Flink‑powered StreamSQL engine, dramatically improving task creation speed, resource utilization, and reliability while automating issue diagnosis for company‑wide real‑time and offline data synchronization.
This article walks through a Spark and Kudu advertising project, explaining the refactoring approach, Scala trait usage, implementation of ETL and province‑city statistics processors, and shows the complete Spark application entry point with full code examples.
The article explains the fundamentals of ETL, compares Talend and Pentaho in terms of openness, connectivity, support, performance, GUI usability, deployment flexibility, and cost, and concludes with guidance on choosing the appropriate tool based on specific business and technical requirements.
This tutorial describes how to place advertising JSON data on HDFS, use Spark for ETL and analysis, enrich logs with IP lookup, and persist the results into Kudu with daily scheduling, including code examples and schema definitions.
This article provides an extensive summary of data middle platform concepts, covering data aggregation, collection tools, offline and real‑time development, data governance, service layers, warehouse construction, and operational practices, illustrating how enterprises build and manage a unified data ecosystem.
This article provides a comprehensive overview of data middle platforms, covering data aggregation, collection tools, offline and real‑time development, job scheduling, data governance, multi‑layer architecture, ETL processes, and various industry use cases, illustrating how enterprises build and manage unified data assets.
This article presents a comprehensive guide to constructing real-time data warehouses using Apache Flink, covering the motivations, design principles, application scenarios, layer-by-layer architecture, metadata and lineage management, quality assurance, and the supporting toolchain for reliable streaming analytics.
This article explains how enterprises can leverage massive raw and business data to build detailed user profiles, covering tag types, data architecture, development modules, project phases, key deliverables, and a real-world e‑commerce case study.
This article explains the fundamentals of user profiling in the big data era, covering tag types, data architecture, development modules, a step‑by‑step implementation process, a practical e‑commerce case study, table design strategies, and both quantitative and qualitative profiling methods.
This article introduces Kafka Connect, a framework for building scalable data pipelines between Kafka and other systems, covering its architecture, key concepts like connectors and tasks, and practical deployment examples.
This article presents a comprehensive design and implementation guide for a financial data warehouse, covering background needs, modeling methodology choices, a layered architecture, data quality monitoring, metadata management, naming and coding standards, and future development directions.
The article analyzes data‑warehouse workflow scenarios, explains core concepts such as OLAP, multidimensional modeling and layer architecture, reviews existing workflow engines like Azkaban, Oozie and Airflow, and proposes a task‑and‑instance layered optimization that simplifies dependency configuration, improves collaboration, and supports complex scheduling in modern big‑data environments.
This article compiles a comprehensive set of 32 interview questions covering data warehouse fundamentals, data platform construction, modeling approaches, real‑time architectures, data quality, governance, Hive optimization, and related analytical techniques to help candidates prepare for data engineering roles.
This article analyzes workflow scenarios in data warehouse construction, proposes an optimization scheme that abstracts workflow nodes into task and instance layers, and demonstrates how task attributes and generation rules can improve configurability, dependency management, and collaborative development for large‑scale data warehouse projects.
The article analyzes workflow scenarios in data‑warehouse projects, proposes a two‑level model that abstracts workflow nodes into tasks and instances, defines period and dependency attributes, and presents generation rules that simplify configuration, improve collaboration, and support complex data‑processing schedules in modern big‑data environments.
This article describes the design and implementation of a data warehouse, covering storage engine choices, dimensional modeling techniques, ETL processes using Python scripts, and workflow management with Apache Airflow to address data integration, scalability, and maintenance challenges.
This article describes AutoHome's data warehouse architecture, detailing its background, business pain points, layered model design (RDM, ADM, GDM, FDM, Stage/BDM, DIM, TMP), advantages in performance, cost, efficiency, quality, and various application scenarios including BI, analytics, and decision support.
This article details the evolution of Ctrip's flight ticket data warehouse, describing its historical tech stack, current architecture—including Hive, Presto, ClickHouse, CrateDB, and Flink—data synchronization methods, layer design, quality monitoring, and a real‑time price‑monitoring use case.
The article traces Youzan’s data warehouse from its chaotic early days lacking structure, through a 2016 Airflow‑driven construction phase that introduced layered ODS/DW/Data Mart architecture and naming standards, to a mature stage focused on efficiency, security, SparkSQL, dimensional modeling, metadata, and ongoing real‑time and governance challenges.
This article outlines Ctrip's flight ticket data warehouse evolution, current big‑data technology stack, data synchronization methods, layered architecture, quality monitoring system, and a real‑time price anomaly detection case, providing practical insights for building scalable, reliable data warehousing solutions.
This article explains the purpose of data as a strategic asset, compares traditional databases with data warehouses, outlines key characteristics and related concepts of data warehouses, and introduces the Hadoop ecosystem components that support large‑scale data storage and analysis.
This article systematically describes the challenges, design principles, modeling methods, layered architecture, implementation steps, and standards used in building a comprehensive user behavior data warehouse for 58.com, highlighting practical experiences and future improvement directions.
This article details{32-64 words} OPPO's evolution from an offline data warehouse to a real‑time platform, describing the business scale, data‑mid platform architecture, migration strategy using Flink SQL, extensions like AthenaX, and practical use cases such as real‑time ETL, CTR calculation, and tag import.
This article provides a thorough introduction to data warehousing, covering warehouse creation, the ETL process, OLAP/BI tools, data cube concepts, common OLAP operations, and the three main OLAP architectural models (MOLAP, ROLAP, HOLAP).
This article explains the historical background of databases and data warehouses, outlines why data warehouses are essential for modern enterprises, and provides a step‑by‑step guide to building a data warehouse using Kimball’s dimensional modeling approach.
This article explains the origins and challenges of scattered enterprise data, defines the data warehouse concept, details its four core characteristics, compares entity, normalization, and dimensional modeling methods, and illustrates Maoyan’s three‑stage data‑warehouse evolution with practical examples and diagrams.
This article systematically outlines the architecture and technical strategy of big‑data product design, detailing a five‑step process from front‑end data collection and ETL to data warehousing, modeling, algorithm design, and personalized user‑centric delivery, while highlighting common platform challenges and future deep‑learning enhancements.
The article surveys modern big‑data architecture, contrasting Lambda and Kappa models, highlights common governance and integration pain points, and proposes an end‑to‑end platform featuring unified metadata, stream‑batch processing, one‑click ingestion, standardized modeling, intelligent query abstraction, and a comprehensive development IDE.
This article traces the evolution of Youzu's data platform ingestion, comparing early HTTP/script methods with modern DTS and real‑time ETL solutions, evaluating middleware choices, detailing core system architectures, and outlining future improvements for reliable, scalable data access.
This article explains how to overcome Spark SQL's inability to handle certain Oracle data types, such as Timestamp with local timezone and FLOAT(126), by creating and registering a custom JdbcDialect that remaps unsupported types to compatible Spark types.
This article details the AutoHome community's migration from a monolithic SQL Server database to the distributed TiDB platform, covering the performance bottlenecks that prompted the change, the evaluation of candidate databases, extensive OLTP/OLAP testing, the full‑ and incremental‑sync migration strategy, rollback mechanisms, and the resulting operational improvements.
The article demonstrates how to enrich Flink streaming ETL jobs with slowly changing dimension data by periodically loading MySQL tables into an in‑memory cache and performing a simple map‑side join within a custom RichMapFunction implementation.
This article provides a thorough introduction to data warehouses, traces their evolution, explains construction methodologies, compares offline, Lambda, and Kappa architectures, and presents real‑time warehouse case studies from Alibaba, Meituan, Xiaomi, Netflix, and OPPO, highlighting practical implementation details and challenges.
This article describes the challenges of handling billions of travel records in Qunar's MySQL databases, compares open‑source data sync solutions like Databus and Canal, outlines the legacy system’s issues, and presents a refactored architecture that introduces Otter, ES gateway, and improved aggregation to achieve low‑latency, reliable, and scalable data synchronization.
This article presents a structured, repeatable approach for Talend data‑integration jobs that guides readers through pinpointing performance bottlenecks, testing individual pipeline stages, and applying targeted optimizations to sources, targets, and transformations to achieve higher throughput and more reliable ETL processes.
This article details Mafengwo’s journey in constructing a data warehouse and data middle platform, covering the core three‑layer architecture, hybrid modeling approaches, the supporting toolchain for data synchronization, scheduling, and metadata management, and the design of an indicator platform for business analytics.
This article provides a comprehensive introduction to data warehouses, covering their definition, architectural layers, characteristics, modeling approaches such as Inmon and Kimball, fact and dimension table design, star and snowflake schemas, and best‑practice principles for building scalable, maintainable warehouse solutions in the big‑data ecosystem.
This article reviews the evolution and key components of big data platforms at leading Chinese internet companies—Taobao, Didi, and Meituan—detailing their data sources, synchronization tools, storage layers, processing engines, and scheduling systems to provide practical guidance for building robust big data infrastructures.
The article details WMDA's end‑to‑end data analytics architecture, covering zero‑event data collection, real‑time and offline processing pipelines built on Spark Streaming, Druid, Hadoop, Kettle, and TaskServer, and explains how these components collaborate to deliver comprehensive user behavior analysis.
This article explains how to build a data warehouse from scratch, covering its definition, system and collaboration layers, ETL requirements, data layering design, modeling steps, common challenges, and governance practices such as temporary table management and coding standards.
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.
