This article explains Flink's checkpoint mechanism, outlines key performance metrics, discusses interval configuration, external state storage choices, resource allocation, and task-local recovery strategies to improve checkpoint speed and reliability in large‑scale state scenarios.
This article surveys the 2019 GraphTech ecosystem, detailing the rapid growth of graph databases, market drivers, ecosystem layers, and the variety of native and multi‑model storage systems that support graph‑structured data.
This article explains the design of a data exploration service for large‑scale distributed storage systems, detailing metadata synchronization, index reconstruction, aggregation tables, node‑level statistics, a user console, and the transition from checkpoint‑based snapshots to delta updates using RocksDB WAL in Hadoop Ozone Recon Server.
This tutorial demonstrates how to create a VARBINARY test table in HBase using Phoenix, serialize its data with RoaringBitmap, implement a custom Spark aggregation function to merge bitmap values, and query the table via Spark SQL, showcasing a practical big-data processing workflow.
This article explains GrowingIO's event analysis data model, the challenges of metric‑dimension calculations on massive datasets, and how a BitMap‑based vertical storage and dimension‑combination numbering dramatically improve query efficiency and scalability.
This article explains how ZooKeeper functions as a distributed coordination service for HBase, detailing its role in master and RegionServer fault tolerance, log splitting, META table location tracking, and replication management, illustrating the underlying ZNode structures and failover mechanisms.
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 demonstrates how to create and register custom scalar UDFs, untyped and type‑safe aggregate functions (UDAF and Aggregator) in Spark SQL, and how to apply window functions such as ROW_NUMBER, providing complete Scala code examples and execution results.
This article explains how FunTouTiao builds a high‑performance real‑time analytics pipeline using Flink, Hive, and ClickHouse, covering business scenarios, hour‑level and second‑level Flink‑to‑Hive architectures, streaming file sink mechanics, multi‑user permissions, ClickHouse performance tricks, and future roadmap for unified stream‑batch storage.
The article outlines a three‑dimensional framework—technical depth, ecosystem breadth, and evolution capability—to help engineers decide which big‑data or stream‑processing technology (such as Hadoop, Spark, or Flink) is worth investing time in, and provides practical tips like using Google Trends and GitHub awesome lists.
This article analyzes Apache Spark's executor-side memory management model, focusing on the UnifiedMemoryManager in Spark 2.2.1, detailing on‑heap and off‑heap memory regions, dynamic execution/storage memory sharing, task memory allocation, and practical configuration examples.
Facing massive, multi‑system log volumes, China Minsheng Bank’s big‑data team designed a real‑time intelligent log platform by integrating Ctrip’s open‑source Apollo configuration center with Elasticsearch, enabling centralized, versioned, hot‑reloading configuration, role‑based parameter management, and high‑availability deployment across thousands of servers.
This article provides a comprehensive overview of Spark's memory management, covering executor memory architecture, the differences between on‑heap and off‑heap memory, static versus unified memory managers, storage and execution memory handling, and practical guidelines for optimizing Spark applications.
This article details Kuaishou's Flink‑based real‑time computing architecture, its massive cluster scale, and the comprehensive strategies—including overload protection, system stability, pressure testing, and resource guarantees—implemented to ensure reliable streaming for the 2020 Spring Festival Gala and its real‑time dashboard.
The data‑platform team introduced a self‑sustaining cost‑reduction framework that quantifies CPU, memory, and disk expenses using price‑per‑resource formulas, applies time‑weighted billing, generates multi‑level reports, and drives optimization through six actionable “swords” and incentive‑based operations, achieving roughly 17 % offline‑cluster savings within six months.
This article introduces KSQL as a lightweight streaming SQL engine for Apache Kafka, explains its architecture and core concepts of streams and tables, and provides step‑by‑step deployment instructions, command‑line examples for creating streams/tables, querying data, and managing persistent queries.
This article explains Spark SQL's Adaptive Execution engine, covering its background, dynamic plan adjustments, shuffle partition tuning, data skew mitigation techniques, and the key configuration parameters needed to enable and fine‑tune adaptive query execution for improved performance.
This article explains how HiveCube can replace traditional development for multi‑dimensional aggregation in a data‑warehouse, covering background, theory of cube, with‑cube/rollup/grouping‑sets syntax, grouping_id handling, practical implementation tips, performance tuning, and a comparison with conventional methods.
The Tencent Advertising Rhinoceros Bird Special Research Program, launched in June 2020, invites global academia to collaborate on advertising technology challenges in AI, big data, and related fields, outlining the application process, evaluation criteria, and accompanying Wiztalk lecture series.
This article compares data warehouse, data lake, data platform, and data middle platform, explaining their definitions, architectures, strengths, limitations, and use‑case differences, and provides tables that highlight how each solution handles structured and unstructured data, governance, flexibility, and business value.
This article provides a comprehensive tutorial on installing a single‑node Elasticsearch 7.x cluster, configuring its key settings, setting up the Elasticsearch‑head web UI, and includes a reference 6.x configuration file for production environments.
The article analyzes the rapid shift from "Made in China" to "Intelligent Manufacturing" driven by industrial internet, 5G, AI and big data, highlighting policy evolution, case studies across light industry, liquor production and hazardous chemicals, and Tencent Cloud's strategic role in enabling digital transformation.
The article details Beike's Data Management Platform (DMP) built since May 2018, covering its overall architecture, data collection, processing, real-time profiling, storage solutions, application scenarios, achieved performance metrics, and future development directions.
The article explains what a data middle platform is, why it differs from a traditional big‑data platform, the efficiency, collaboration and talent challenges it addresses, its definition as a data‑driven innovation layer built on big data, cloud and AI, and outlines its logical architecture centered on data APIs.
Apache Spark 3.0, released after a 21‑month development cycle, introduces dynamic partition pruning, adaptive query execution, accelerator‑aware scheduling, DataSource V2, enhanced pandas UDFs, new join hints, richer monitoring, ANSI‑SQL compatibility, SparkR vectorization, Kafka header support, and numerous platform upgrades, all backed by over 3,400 resolved issues.
This article compares Apache Flink and Apache Spark in both standalone and YARN deployment modes, detailing their architecture, job scheduling differences, and specific configurations such as Flink’s yarn‑cluster and yarn‑session modes versus Spark’s yarn‑client and yarn‑cluster modes.
This article details the 2015 hybrid data‑warehouse design implemented at Guangdong Huaxing Bank, explaining its real‑time, historical, and archival layers, the data‑bus concept, and how mixing in‑memory, relational, and Hadoop technologies addressed modern banking data‑volume, latency, and unstructured‑data challenges.
QuTouTiao leverages Flink and ClickHouse to build a high‑performance real‑time analytics platform that supports hourly Hive pipelines and sub‑second ClickHouse queries, achieving sub‑second response for 80% of requests through streaming ingestion, exactly‑once semantics, multi‑cluster coordination, and optimized ClickHouse storage and connector designs.
The article details JD’s data product teams’ systematic preparation for the 618 shopping festival, covering pressure estimation, capacity expansion, stress testing, emergency downgrade strategies, dual‑data‑center isolation, high‑fidelity end‑to‑end testing, and continuous monitoring to ensure stable, real‑time data services during massive traffic spikes.
Facing billions of video entities and edges, Youku’s engineering team replaced traditional relational stores with a graph‑based knowledge platform, leveraging Alibaba’s Blink streaming engine and Lindorm to enable real‑time, incremental updates, unified UDF logic, and scalable feature computation for search and recommendation.
The article explains how to design a time‑based Elasticsearch cluster using a hot‑warm architecture with dedicated master, hot, and warm nodes, shows how to configure node attributes, allocate indices via settings or Curator, and discusses best‑practice compression and rollover strategies for large‑scale log data.
This article explains how Kafka’s design—its broker architecture, use of sequential disk I/O, PageCache, Sendfile, and a custom memory buffer pool—optimizes JVM garbage collection and achieves massive throughput in big‑data messaging scenarios.
This article provides a comprehensive guide to improving Hive query performance by covering column and partition pruning, predicate pushdown, replacing ORDER BY with SORT BY, using GROUP BY instead of DISTINCT, tuning MapReduce jobs, handling data skew in joins, and selecting appropriate storage formats for large‑scale data warehouses.
The article details JD.com’s Y Business Management Department’s data‑driven, algorithmic approaches to inventory forecasting, replenishment, allocation, and fulfillment during the 618 promotion, describing how big‑data predictions, dynamic programming, ADMM column generation, and cross‑department collaboration optimized costs, reduced stockouts, and enhanced customer experience amid pandemic challenges.
This article presents a comprehensive offline big‑data processing framework that leverages scalable object storage for PB‑level data, details storage and compute engine requirements, compares cost options, describes data pipeline design, and showcases an e‑commerce case study with Spark‑driven analytics.
This article describes how iQIYI evaluated various OLAP engines, selected Apache Druid for real‑time analytics, detailed its architecture, identified performance bottlene‑cks in Coordinator, Overlord and indexing, applied configuration and resource‑allocation optimizations, and built a user‑friendly RAP platform to democratize real‑time data analysis.
This article explains the three message delivery semantics in distributed stream processing, compares Kafka‑Spark Streaming integration methods (receiver vs direct stream), and details how to achieve exactly‑once guarantees through idempotent or transactional writes, including code examples.
This article describes the evolution of a real‑time computing platform from SQL 1.0 built on Spark Structured Streaming to SQL 2.0 powered by Flink‑SQL, covering dynamic tables, continuous queries, dimension‑table joins, cache optimization, DDL extensions, platformization, operational challenges and future roadmap.
VTrace is an automated diagnostic system that leverages big‑data processing to instantly reconstruct traffic paths and pinpoint the root causes of persistent packet loss in cloud‑scale overlay networks, dramatically simplifying network operations and cutting troubleshooting time from hours to minutes.
This article explains Apache Flink's distributed system architecture—including JobManager, ResourceManager, TaskManager, and Dispatcher—covers session and job deployment modes, data transfer mechanisms, event‑time handling with watermarks, various state types and backends, scaling strategies, and the checkpoint/savepoint recovery process.
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.
This article presents Kuaishou's extensive use of Apache Flink for real-time multi-dimensional analytics, detailing the platform's architecture, cluster scale, data processing pipelines, the design of a shared state storage engine called SlimBase, and performance improvements achieved through replacing RocksDB with a customized HBase‑based solution.
This article presents a data‑driven approach for allocating limited video exposure resources by building a PV‑click‑CTR (P2C) sensitivity model and a multi‑objective optimization framework that balances overall view volume and fairness across scenes, validated through offline metrics and online bucket tests.
This article details the evolution of 58 Tongcheng Bao's real‑time data warehouse, describing the initial Spark‑Streaming architecture, its limitations, and the redesign using Flink with a layered ODS‑DWD‑DWS‑APP model, data‑quality monitoring, join techniques, and the resulting improvements in latency and accuracy.
This article provides a detailed introduction to Spark Streaming, covering its architecture, DStream concepts, initialization, data sources, transformations, windowed aggregations, output operations, checkpointing, fault‑tolerance semantics, deployment, performance tuning, and monitoring for building reliable high‑throughput streaming applications.
The article provides a comprehensive overview of SQL‑on‑Hadoop query engines such as Hive, Impala, Presto and Spark SQL, comparing their runtime frameworks, core components, compilation steps, optimizer strategies, CPU/IO efficiency techniques, storage formats like ORC and Parquet, and resource management in a unified perspective.
This article explains how Kafka can be used for data ingestion and event distribution by illustrating production‑consumer and publish‑subscribe models, describing core concepts such as topics, partitions and consumer groups, and offering practical design options for handling different event scenarios.
This article presents a comprehensive case study of 58.com’s massive Hadoop‑based offline computing platform, detailing its architecture, scaling challenges, performance‑tuning measures, YARN and SparkSQL upgrades, and the systematic cross‑data‑center migration of thousands of nodes and petabytes of data.
The article reviews a Tencent Cloud closed‑door forum where industry experts dissect the banking sector’s digital transformation, outlining enterprise‑architecture‑driven strategies, key enabling technologies such as cloud, AI, big data and blockchain, and practical insights from Q&A sessions and ThoughtWorks’ models.
This article reviews how leading Chinese internet companies such as JD.com, Ctrip, Qunar, 58.com, and Didi have adopted Elasticsearch for large‑scale order search, log analysis, real‑time monitoring, and security, describing the evolution of cluster architectures, shard strategies, multi‑cluster pipelines, and performance optimizations.
This article explains Flink's StreamingFileSink in version 1.10.0, covering how files transition through In‑progress, Pending, and Finished states, the bucket assignment and rolling policies, and provides a complete Java example for writing string data to files.
Learn how to set up a Python environment on an Ubuntu ECS, install the presto‑python‑client and PyHive libraries, configure Kerberos and SSL credentials, run sample queries against a Presto coordinator, and avoid typical errors such as NTP, SSL and authentication issues.
Facing growing order volumes and strict timeliness demands, Cainiao’s tech team overhauled its real‑time data warehouse by redesigning data models, adopting Flink for streaming computation, upgrading data services, and exploring innovative tools, sharing practical lessons and future directions for large‑scale logistics analytics.
QQ Music’s new real‑time OLAP platform, built on ClickHouse, Superset and Tencent Cloud EMR, ingests petabyte‑scale streaming and batch data with SSD‑backed ZooKeeper, load‑balanced writes, optimized partitions and read/write separation, delivering second‑level query responses that are several times faster than Hive, Presto or SparkSQL and enabling self‑service BI for thousands of users.
This article provides a comprehensive overview of Apache Zookeeper, detailing its purpose as a distributed coordination service, its key uses such as cluster management, configuration management, naming, distributed locking, and queue management, as well as its architecture, message types, Znode structures, read/write processes, Zab and Fast Paxos protocols, server states, and watcher mechanism.
This article examines Apache Flink’s programming model, comparing its batch DataSet API with the streaming DataStream API, detailing class hierarchies, key code examples such as groupBy and job submission, and explaining how both paradigms are unified into a common JobGraph representation.
Kafka is an Apache‑hosted distributed streaming platform that provides high‑throughput, durable, publish‑subscribe messaging, originally developed by LinkedIn; this article explains its core concepts, message system classifications, architecture components, APIs, replication, consumer groups, and guarantees, comparing it with other messaging solutions.
The article explains how a well‑defined technology roadmap, encompassing architecture, data governance, storage, analytics, and automation, is essential for aligning tools and techniques with business goals to achieve a successful, outcome‑driven enterprise data strategy.
iQiyi’s full‑link automated monitoring platform unifies tracing, metric and log collection with deep offline and real‑time analysis, delivering a DAG‑based call graph, near‑real‑time ingestion of tens of millions of logs, multi‑dimensional alerts and rapid root‑cause diagnosis that cut error‑lookup time by over 50 % and now serves as a core component of the company’s microservice reference architecture.
This article provides a comprehensive guide on detecting Hadoop system bottlenecks, analyzing resource constraints, and applying practical MapReduce performance tuning techniques—including baseline creation, counter analysis, combiner usage, compression, and proper Writable types—to achieve optimal big‑data processing efficiency.
This article provides a comprehensive tutorial on Kerberos fundamentals, its authentication workflow, and detailed procedures for installing, configuring, and enabling Kerberos security on a Cloudera (Hadoop) cluster running on CentOS, including code snippets, configuration files, and post‑deployment testing steps.
TCOS 2.0, the container operating system from Transwarp, expands compatibility to Windows, ARM, MIPS, and domestic platforms, adds GPU heterogeneous scheduling, HPA autoscaling, enhanced local storage management, and improved monitoring, providing a robust foundation for big data, AI, and cloud-native applications.
This article explains why Elasticsearch cross-index queries are essential, outlines their technical principles, showcases classic use cases such as business analytics, big‑data pipelines and log management, and provides practical methods, code examples, and performance considerations for effective implementation.
This article explains how ZooKeeper stores Kafka's coordination data by detailing the predefined ZK paths, the JSON structures for broker, topic, partition, controller, and consumer information, and the auxiliary nodes used for replica election and partition reassignment.
This article examines how to run several Spark jobs concurrently on a shared SparkContext, balancing full CPU‑vcore utilization with the need to generate fewer Parquet files, and presents practical experiments, scheduling strategies, and performance results.
This comprehensive guide covers Kafka's definition, publish/subscribe model, key components, storage mechanisms, producer and consumer strategies, and reliability features such as ACK levels, ISR, and exactly‑once semantics, providing a solid foundation for real‑time big‑data processing.
The article explains how Apache Hudi enables near‑real‑time data ingestion from various sources, supports low‑latency analytics, provides incremental processing pipelines, and simplifies data distribution on Hadoop, improving efficiency and reducing operational complexity.
The article examines how smart community initiatives, driven by AI, big data and cloud technologies, can overcome regional imbalances and information silos to create integrated, efficient, and resident‑friendly services that form a one‑hour lifestyle ecosystem.
The article examines how China’s push for public data sharing, led by Suning chairman Zhang Jindong, proposes a data governance committee, a unified sharing platform, security frameworks, and education initiatives to break information silos and accelerate the digital economy.
The May 2024 Huawei Cloud Community newsletter curates expert articles and tutorials on AI dark data handling, Apache CarbonData advances, agile retrospectives, IoT Greengrass on Raspberry Pi, Kubernetes networking, WAF anti‑scraping, openEuler contributions, Kunpeng cloud case studies, and ModelArts practical projects.
This article describes how advanced natural‑language‑processing, big‑data, and deep‑learning techniques are integrated into an end‑to‑end platform for financial asset management, covering large‑scale bid‑tender text analysis, few‑shot sentiment monitoring, model architectures, data‑enhancement methods, and practical deployment results.
Alibaba’s Fuxi DAG 2.0 redesign separates logical and physical graphs, introduces dynamic scheduling, unified offline and near‑real‑time execution, and a flexible bubble mode, enabling massive big‑data jobs to run up to five times faster while dramatically reducing resource waste.
The article explains Reddit's large‑scale page‑view counting system, detailing its real‑time requirements, the challenges of naive hash‑set storage, and how a hybrid approach using linear probability and HyperLogLog algorithms together with Kafka, Redis, and Cassandra achieves accurate, low‑memory, near‑real‑time analytics.
This article provides a comprehensive step‑by‑step guide for installing Apache Kylin on a single machine, covering required software versions, environment variable configuration, Spark dependency handling, main Kylin properties, verification steps, and detailed solutions to common errors such as Zookeeper host issues, HTTP 404, Jackson conflicts, MapReduce jobhistory problems, missing Spark classes, HiveConf errors, and YARN shuffle service configuration.
Effective data product development hinges on deep business understanding, clear metric decomposition, rigorous model evaluation, and translating technical performance into business impact, while algorithm engineers are best assessed by publication quality, problem significance, algorithmic contribution, and practical interview questions on model tuning and improvement.
This article shares Cainiao's practical experience in constructing a real-time data warehouse, covering the shortcomings of the previous architecture, the evolution of data models, the migration to Flink with advanced features like retraction and timer services, and the modernization of data services and tooling to support high‑throughput logistics scenarios.
This article explains the new unified TaskManager memory model introduced in Flink 1.10, detailing each memory component, its configuration parameters, how they map to JVM settings, and practical guidance for both standalone and containerized deployments, including a concrete YARN example.
This article provides a comprehensive introduction to Kafka, covering its definition, message queue concepts, architecture components, installation steps, configuration details, startup procedures, operational commands, producer and consumer mechanisms, reliability guarantees, partition strategies, offset management, and performance optimizations.
This article explains the underlying principles of Hadoop HDFS read and write operations, detailing how the client interacts with NameNode and DataNodes, the role of FsDataInputStream and FsDataOutputStream, block location retrieval, pipeline replication, and file closure steps.
This article details how Tongcheng‑eLong transitioned from Docker‑Host deployments to a Kubernetes‑based platform for hundreds of storage and compute services, covering network integration, IP management, service synchronization, storage strategies, operator development, monitoring, logging, and the challenges and future plans they encountered.
Apache Hudi is an open‑source data‑lake framework that leverages Spark and Hadoop‑compatible storage to provide efficient ingestion, incremental processing, and multiple query modes such as snapshot, incremental, and read‑optimized for large analytical datasets.
This article details Tencent's large‑scale Elasticsearch deployment, covering its massive usage scenarios, the availability, performance, cost and scalability challenges faced, and the comprehensive kernel‑level optimizations—including memory‑based throttling, storage‑model merging, off‑heap caching, rollup and metadata improvements—that enable PB‑level clusters with high reliability and low expense.
This article provides a comprehensive introduction to Apache Beam, covering its unified batch‑and‑stream processing architecture, programming model, workflow patterns, Lambda and Kappa architectures, the characteristics of PCollection, pipeline construction, core transforms, I/O handling, and includes practical code examples.
The article surveys how leading Chinese tech firms such as JD Daojia, Ctrip, Qunar, 58.com, and Didi have adopted Elasticsearch for large‑scale search, real‑time analytics, and security, detailing their evolving cluster architectures, shard strategies, data volumes, and supporting services.
This article surveys how major Chinese internet companies such as JD.com, Ctrip, Qunar, 58.com and Didi have adopted Elasticsearch and the Elastic Stack for high‑volume order queries, log analysis, real‑time monitoring, security analytics, and large‑scale distributed search, describing their architecture evolution, shard strategies, and operational practices.
This article explains how to build user profiles by distinguishing persona from profile, describing the evolution of ID‑mapping techniques, designing a multi‑layer tag system, implementing statistical, interest, and model tags, storing the data in Hive, HBase, Codis and Elasticsearch, and finally evaluating profile timeliness, coverage and accuracy.
This article provides a comprehensive, hands‑on tutorial for preparing three VMs, installing JDK and Hadoop, configuring core‑site.xml, hdfs‑site.xml, mapred‑site.xml, yarn‑site.xml, setting environment variables, distributing the package, starting HDFS and YARN, and verifying the cluster via web UI and jps commands.
This article provides a comprehensive introduction to HBase, covering its origins, key characteristics, architecture components, installation steps, basic shell commands for table management, data structures, read/write processes, and high‑availability configuration within the Hadoop ecosystem.
This article provides a comprehensive introduction to Kafka, covering its definition, message‑queue models, architecture components, installation steps, configuration details, producer and consumer mechanisms, reliability guarantees, partition assignment strategies, offset management, and high‑performance read/write techniques.
Apache Zookeeper is an open‑source coordination service that provides reliable distributed synchronization, configuration management, and naming for big‑data components such as Hadoop, HBase, and Kafka, offering features like hierarchical znode structures, watches, master election, and distributed locks to maintain cluster health.
The article outlines a three‑dimensional framework—technical depth, ecosystem breadth, and evolution capability—to help professionals evaluate whether a technology is worth investing time in, illustrated with real‑world examples from Hadoop, Spark, and Flink.
Didi has unified its real‑time computing on Apache Flink, creating an enhanced StreamSQL service with extended DDL, built‑in parsers and UDX, supporting thousands of nodes, millions of jobs, and trillions of daily records, while addressing state management, high availability, multi‑language UDFs, and pursuing real‑time ML and data‑warehouse integration.
This article describes the background, requirement‑driven prototype design, Elasticsearch‑based query‑DSL selection, and extensive performance tuning—including hardware configuration, indexing parameters, JVM and garbage‑collector adjustments—that enabled real‑time ingestion of hundreds of thousands of records and sub‑second search responses for a city‑wide data platform.
Carrefour’s new AI‑driven fast‑pick warehouse system, built by Suning, cuts picking time by half and enables over 95% of more than 1,000 daily orders to be delivered within an hour, illustrating the power of big‑data logistics in modern retail.
This article explains how Alibaba's search, recommendation, and advertising platforms handle the unique quality challenges of big‑data AI applications, detailing six major testing problems and the comprehensive strategies—including functional, real‑time, performance, and stability testing—used to ensure reliable online services.
This article presents a series of hands‑on big‑data exercises, including Spark Scala data analysis, Kafka topic creation and custom partitioning, and MySQL table design with Scala‑based streaming calculations, providing complete source code and step‑by‑step solutions for each task.
This tutorial explains several techniques for adding new columns to PySpark DataFrames—including native Spark functions, user‑defined functions, RDD transformations, Pandas UDFs, and SQL queries—while demonstrating data loading, schema handling, and code examples for each method.
A comprehensive survey by Suning Retail Technology Institute and the Asia‑Pacific Smart Retail Industry Alliance reveals how twenty retail technology firms faced COVID‑19 disruptions, optimized operations, accelerated digital transformation, and identified emerging growth points in AI, big data, and omnichannel retail.
This talk reviews China's big‑data evolution from early enterprise data warehouses to modern data middle platforms, outlines core architectural components, technology selections, data development practices, lifecycle and quality management, and shares practical Q&A insights for building scalable, cost‑effective data infrastructures.
Amid the rapid rise of the digital economy, China now treats data as a new production factor, driving innovations such as 5G, AI, and big-data-enabled retail, where companies like Suning integrate online and offline channels, leverage C2M models, and boost efficiency through data-driven operations.
