This article reviews the origins, core characteristics, traditional and logical architectures of data warehouses, explores emerging trends such as massive real‑time data, and outlines Huawei Cloud GaussDB(DWS) evolution toward a cloud‑native, elastic, lake‑warehouse integrated solution.
This guide reviews eight popular ETL and data migration tools—including Kettle, DataX, DataPipeline, Talend, DataStage, Sqoop, FineDataLink, and Canal—detailing their core features, architectures, and use cases to help engineers choose the right solution for reliable data integration.
Explore a comprehensive overview of eight popular ETL and data migration tools—including Kettle, DataX, DataPipeline, Talend, DataStage, Sqoop, FineDataLink, and Canal—detailing their features, architectures, and use cases to help you choose the right solution for efficient data integration.
This article provides an overview of the most popular ETL tools—both open‑source and commercial—explaining their core features, use cases, and how they simplify data extraction, transformation, and loading for modern data‑driven applications.
This article introduces the concept of ETL, explains its importance for modern data‑driven applications, and provides a comprehensive comparison of the most popular open‑source and commercial ETL platforms—including their key features, supported data sources, and deployment options—helping readers choose the right tool for their data integration needs.
The article presents a comprehensive overview of the Wenjie low-code platform, detailing its motivation, front‑end React framework, back‑end Spring Cloud micro‑services architecture, PowerJob scheduler, custom ORM, various data‑modeling and data‑processing scenarios, dashboard visualizations, monitoring and alerting features, as well as future plans and a concluding summary.
This article provides a detailed overview of data middle platform concepts, describing a decoupled six‑subsystem architecture—including storage, collection, processing, governance, security, and operation frameworks—while illustrating typical enterprise implementations, industry‑specific solutions, and best‑practice considerations for building scalable, secure, and value‑driven data platforms.
The SQLE 2.2302.0 release introduces data source import from external platforms, operation‑record viewing, improved CloudBeaver integration, manual deployment support, numerous UI optimizations, and a long list of bug fixes, providing a more seamless and secure SQL auditing experience for both enterprise and community users.
This article provides a comprehensive overview of Apache SeaTunnel, covering its design objectives, current capabilities such as multi‑engine support and extensive connector ecosystem, detailed architecture including engine‑independent APIs and execution flows, and outlines the upcoming roadmap to expand connectors, launch a visual web UI, and introduce a dedicated SeaTunnel Engine.
This comprehensive guide walks you through the entire data governance ecosystem, covering metadata fundamentals, classification, maturity models, data standards, modeling, integration, lifecycle management, quality assurance, security, and ETL processes, all illustrated with clear diagrams and practical steps.
This article presents the SPI-based refactoring of Apache InLong Manager, describing the project's background, existing maintenance challenges, the concept of Java Service Provider Interface, the concrete implementation steps, code restructuring, and the resulting benefits such as higher code reuse, easier extension, and reduced DDL changes.
Enterprises must shift their perception of data assets and embed data‑value into every digital process, establishing governance, unified asset catalogs, operational metrics, security controls, integration, services, and visualization to transform raw data into strategic business outcomes.
This article introduces Alibaba DataX, explains its architecture and role in offline heterogeneous data synchronization, provides step‑by‑step Linux installation, demonstrates full‑load and incremental MySQL‑to‑MySQL sync with JSON job templates, and shares practical tips for handling large data volumes.
This article explains the challenges of a Lambda‑architecture data pipeline, introduces NetEase’s Arctic lakehouse built on Apache Iceberg, details its table‑store design, optimization cycles, consistency mechanisms, real‑time features, practical use cases, and future roadmap, highlighting its advantages over similar solutions.
The article details Zhihu's data empowerment team's design and implementation of a low‑cost, high‑response real‑time data platform built on Apache Doris, covering real‑time business metrics, algorithm features, and user profiling, and explains the challenges, architectural choices, tooling, performance gains, and future directions.
This article introduces the UData platform, explains its data‑integration architecture, details the StarRocks‑based query engine workflow from SQL parsing to distributed execution, and describes recent optimizations such as computation push‑down, support for JSF/HTTP/ClickHouse external tables, and a proxy‑based federated query framework.
DataOps, the data‑centric counterpart of DevOps, combines agile principles, standardized tools, and cross‑team collaboration to manage the full data lifecycle—from integration and development to storage, governance, and service—enabling organizations to handle massive, diverse datasets efficiently, reduce silos, and turn data into actionable value.
This article presents the architecture and practical experience of ZhongAn's hundred‑billion‑scale data integration service, covering common integration technologies, business support scenarios for offline and real‑time data, technical challenges, evolution from single‑machine to service‑oriented designs, and future directions using Flink and DataX.
This article outlines the essential requirements, architectural design, and five key capabilities of an enterprise‑level unified data platform, explaining how data integration, cloud‑native features, and layered design enable digital transformation and business innovation.
The 2022 Flink Forward Asia conference highlighted Apache Flink’s rapid growth, showcased major technical advances such as upgraded checkpointing, cloud‑native state storage, Hybrid Shuffle, Flink CDC 2.0, and Flink ML 2.0, and presented real‑world deployments from Alibaba, Midea, miHoYo, and Disney.
The article explores Apache Flink’s eight‑year journey to becoming a top‑level Apache project, Alibaba’s extensive contributions, the rise of stream‑batch unified computing, its impact on real‑time data integration, cloud‑native deployment, and the emerging Flink‑based data‑warehouse and serverless solutions.
This interview with Alibaba researcher Wang Feng (aka Mo Wen) explores Apache Flink’s eight‑year journey to top‑level Apache status, its unified stream‑batch architecture, the rise of Flink Table Store and CDC, and how cloud‑native deployments are reshaping real‑time big data processing.
BitSail, an open‑source data integration engine from ByteDance, provides a unified solution for batch, streaming, full‑load, and incremental data synchronization across heterogeneous sources, detailing its background, technical evolution, architecture, low‑cost co‑building features, compatibility strategies, and future roadmap.
BitSail, ByteDance’s open‑source data integration engine built on Flink, has evolved through three major versions to support batch, streaming and CDC modes, handling over 200,000 daily tasks across 20+ data sources, and aims to meet real‑time, cloud‑native integration demands.
This article introduces Debezium, an open‑source low‑latency change data capture platform that streams database row changes via Kafka, explains its architecture and common scenarios such as cache invalidation and CQRS, and provides step‑by‑step Docker commands to install ZooKeeper, Kafka, MySQL and the Debezium connector.
Shopee faced fragmented data‑ingestion pipelines, limited source support, and high maintenance overhead, so it evaluated open‑source tools and adopted Apache SeaTunnel to unify batch and streaming data transfers, simplify ETL workflows, and provide a scalable, extensible solution for its multi‑TB daily data processing needs.
This article explains the challenges of point‑to‑point data sync, introduces RocketMQ Connect as a cloud‑native solution that decouples upstream and downstream, details its architecture, connectors, REST API, metrics, deployment modes, and provides a step‑by‑step guide to building custom connectors for use cases such as CDC, data lakes, and system migration.
This article describes how AutoHome tackled the complexity of managing multiple relational, NoSQL, and Hive data stores by adopting openLooKeng for unified, cross‑source SQL queries, outlines its key features such as ANSI‑SQL support, diverse connectors, and query optimizations, and details the custom enhancements made to the Apache Kylin connector to better serve their commercial data analysis workloads.
This article describes how Baidu's commercial frontend team improved front‑end and back‑end collaboration efficiency by introducing a BFF layer, data‑direct capabilities, staged data tiering, and fragment‑based batch editing, ultimately reducing delivery time by over 50% while maintaining quality.
The article explains how Microsoft’s Common Data Model provides a shared metadata system and standardized, extensible data schemas that simplify integration across Power Apps, Power BI, Dynamics 365 and Azure, enabling consistent data semantics, easier app development, and scalable enterprise solutions.
The article describes how Baidu's commercial front‑end team introduced a data‑direct capability and BFF layer to streamline front‑end/back‑end cooperation, reduce environment‑maintenance overhead, enable parallel development, and improve overall delivery efficiency across multiple project phases.
Shopee tackled its data‑system bottlenecks by customizing Apache Hudi to provide unified stream‑batch integration, efficient state‑detail snapshots, and low‑latency wide‑table generation, using CDC‑based bootstrapping, COW/MOR tables, savepoints and partial updates, which cut latency to ten minutes, lowered resource use, and yielded several community‑backed enhancements.
This article explains DataOps and its role in modern ETL pipelines, outlines the benefits of DataOps for efficiency and reliability, and provides a detailed roadmap and best‑practice guidelines for planning, implementing, and optimizing DataOps‑driven ETL in cloud‑native environments.
This article provides a detailed overview of DataX, covering its purpose, framework design, core architecture, scheduling process, practical examples of MySQL-to-MySQL synchronization, step‑by‑step installation and configuration of DataX‑WEB, UI usage, routing strategies, task types, and advanced task building techniques.
This article provides a detailed overview of Spring Batch, covering its purpose, architecture, core concepts such as Job, Step, ItemReader/Writer/Processor, execution flow, chunk processing, skip/failed handling, and practical tips for building robust Java batch applications.
JD Logistics leverages StarRocks to create the Udata unified query engine, addressing data silos, low performance, and high maintenance costs by integrating data services and analytics, enabling low‑code data service generation, high‑speed federated queries, real‑time updates, and future data‑lake and resource isolation capabilities.
The presentation details the motivations, goals, and architectural redesign of Apache SeaTunnel (Incubating) to decouple its Source and Sink APIs from underlying engines, introducing unified APIs, version‑agnostic connectors, and enhanced support for Spark and Flink in both batch and streaming scenarios.
This article introduces Apache SeaTunnel's Examples module, compares SeaTunnel with DataX, explains its multi‑engine design, demonstrates Flink and Spark example implementations, and shares the speaker's experiences contributing to the open‑source community, providing practical guidance for big‑data integration projects.
From data ingestion to real‑time analytics, this guide breaks down the essential layers of a typical big‑data platform—covering collection methods, HDFS storage, Hive/Spark analysis, data sharing mechanisms, application use‑cases, streaming with Spark Streaming, and the need for robust scheduling and monitoring.
This article, based on a Flink CDC Meetup talk by OceanBase expert Wang He, introduces OceanBase’s architecture and core features, explains the implementation principles of the Flink CDC OceanBase connector, outlines its current capabilities and use cases, and discusses future enhancements and roadmap.
The article details JD International's challenges and solutions in building a unified, real‑time data platform for its multi‑regional 618 promotion, covering business characteristics, data distribution, team organization, dashboard architecture, integration strategies, and short‑ and long‑term technical plans.
This article details how a fast‑growing supply‑chain platform migrated from MySQL and Hive to Apache Doris for real‑time analytics, describing the architectural evolution, the advantages of the new design, practical implementation steps, encountered challenges, and the performance and cost benefits achieved.
This article introduces the concept and practice of Change Data Capture (CDC), explaining how it captures database changes to provide real‑time incremental data for analytics and reporting without impacting source performance, and outlines modern CDC methods, challenges, and production‑ready system requirements.
This article introduces the concept of Change Data Capture (CDC), explains why traditional batch reporting strains resources, describes how CDC captures only data changes to keep source databases performant, and outlines modern CDC architectures, production‑ready considerations, and best‑practice guidelines for building reliable data pipelines.
This article compares three standard internet data‑query APIs—OData, GraphQL, and ORDS—examining their REST foundations, query capabilities, metadata handling, versioning, and practical trade‑offs to help developers choose the most suitable API for analytics, integration, and data‑management applications.
This article introduces Apache SeaTunnel, a distributed, high‑performance data integration platform built on Spark and Flink, outlines its technical features, workflow, and plugin ecosystem, and details a concrete traffic‑management use case involving incremental Oracle‑to‑warehouse data synchronization with Spark resources and scheduled shell scripts.
This article introduces the concept and practice of Change Data Capture (CDC), explaining why CDC is needed for real‑time analytics, how it works by capturing DML changes, modern approaches using transaction logs, and key considerations for building a production‑ready CDC system.
This guide walks through Alibaba Cloud EventBridge’s event standardization, shows step‑by‑step how to integrate OSS events for automatic file unzipping, demonstrates custom event source filtering to store data in RDS, and explains using EventBridge event streams to route RocketMQ messages to MNS, complete with code snippets and configuration details.
This article presents a step‑by‑step tutorial on using the OpenMLDB Pulsar Connector to stream real‑time data from Apache Pulsar into OpenMLDB, covering connector architecture, key features, Docker‑based installation, sink configuration, schema registration, message production, verification queries, and future roadmap details.
The article introduces Flink CDC 2.2, highlighting its expanded support for twelve data sources—including OceanBase, PolarDB‑X, SqlServer, and TiDB—while detailing core features such as the incremental snapshot framework, multi‑version Flink compatibility, dynamic table addition, and numerous bug fixes and performance improvements.
Enterprise architects align IT strategy with business goals by managing application portfolios, technology risk, IT operations, security, data integration, and financial considerations, while balancing long‑term strategic planning with tactical execution in a rapidly changing environment.
NetEase Yanxuan replaced its cumbersome data‑warehouse with a flexible Delta‑Lake/Iceberg data lake, creating a unified metadata layer and real‑time ingestion pipelines that cut latency from nightly batches to seconds, slashed compute and storage costs, supported diverse business scenarios and machine‑learning feature engineering, and set the stage for broader future expansion.
This article provides a comprehensive, hands‑on tutorial for connecting a Hive data warehouse to ClickHouse via Seatunnel, covering environment setup, Hive and ClickHouse table creation, full and incremental data import scripts, execution examples, and practical troubleshooting tips.
SPL is an open‑source structured data processing language that extends full SQL‑92 functionality to a wide range of data sources—including CSV, Excel, JSON, NoSQL and Hadoop—allowing developers to perform complex queries, multi‑step calculations, and mixed‑source analytics without a traditional relational database.
The article explains how Kafka serves as the core of a real‑time data warehouse for search, detailing its advantages over traditional databases, integration with Flink for low‑latency stream processing, architectural patterns such as Lambda/Kappa, scaling challenges, and comprehensive monitoring using Kafka Eagle.
The article details Vipshop's multi‑dimensional use of SeaTunnel to integrate Hive and ClickHouse, describing data import/export challenges, tool selection among DataX, SeaTunnel and Spark, custom configurations, platform integration, and future improvements for high‑performance OLAP pipelines.
This article explains the concept, challenges, and key business goals of a real‑time Customer Data Platform, details the technology stack selection—including Nebula Graph, Apache Flink, Apache Beam, Kudu, and Doris—and describes the modular architecture, data model, identity service, streaming computation, storage layers, rule engine, operational results, and future directions.
This article examines the design and implementation of a tenant‑level real‑time Customer Data Platform, detailing CDP fundamentals, business and technical challenges, key architectural components, technology selections such as graph databases, stream processing, storage engines, and the operational practices that enable high‑throughput, low‑latency data integration and analytics.
This document outlines the logical and technical architecture of a government shared resource platform, describing application system upgrades, data collection and analysis, multi‑layer system design, standards compliance, interface management, and overall system integration for improved service quality and decision support.
This presentation details Alibaba Cloud DataWorks Data Integration (DataX), covering its architecture, core design principles, offline and real‑time synchronization mechanisms, deployment modes, product positioning, use‑case scenarios, and its role within the broader DataWorks ecosystem, highlighting its capabilities for large‑scale data movement and processing.
The article reflects on a CTO’s evolution through three stages, examines why architecture, data, servers and R&D management cannot be fully unified in large organizations, and offers practical guidance on innovation, team growth, and establishing a coherent R&D culture.
This article presents a comprehensive overview of medical knowledge graph development, covering global and domestic progress, domain characteristics, a detailed seven‑piece ontology and "Huizhi" graph construction process, platform support, and real‑world healthcare applications such as intelligent alerts, guideline recommendations, and data reporting.
SeaTunnel, the China‑originated data‑integration platform built on Spark and Flink, has been accepted into the Apache Incubator, and this article introduces its history, architecture, plugin ecosystem, deployment requirements, and numerous enterprise deployments across batch and streaming big‑data scenarios.
This article presents a comprehensive overview of medical knowledge graph development, covering global and domestic progress, domain characteristics, a six‑step construction workflow—including schema design, ontology term set creation, and graph building—and showcases practical applications such as intelligent alerts, guideline recommendations, and data direct reporting.
The article explains enterprise integration as the essential practice of connecting applications, data, and devices across distributed, cloud‑native environments, covering its evolution, key challenges, and core techniques such as messaging, application connectors, data flow platforms, integration patterns, and APIs.
This article outlines Ctrip Vacation's data standard management approach, covering why standards are needed, the three‑element framework of scope, tools, and policies, and detailed practices for data integration, production change handling, metadata governance, portal dashboard standardization, and self‑service query templating.
This article explains how to integrate Apache Hudi with Flink CDC to build a near‑real‑time data lake, covering Hudi’s storage model, streaming primitives, version compatibility, Maven setup, SQL table definitions, data flow from MySQL through Kafka, and practical troubleshooting tips.
This article introduces the cloud‑native LakeHouse solution from China Mobile Cloud, covering its lake‑warehouse integration concept, overall architecture, core functions such as storage‑compute separation, one‑click data ingestion, intelligent metadata discovery, serverless execution, JDBC support, incremental updates, and typical application scenarios in public and private clouds.
The article summarizes a GOPS conference presentation by Dingmao Technology on AIOps scenario‑driven construction, detailing challenges, definition of scenarios, technical methods, roadmap planning, and future prospects, while showcasing practical examples and supporting technologies for intelligent IT operations.
This article explains why ID mapping is essential for breaking data silos, describes traditional address cleaning and DSP scenarios, and details a graph‑based, six‑step process that builds a unified One‑ID dictionary to enable comprehensive user profiling and analytics in big‑data environments.
This article introduces Flink CDC 2.0, explains its distributed full‑load and incremental reading mechanisms, details the slice partitioning, snapshot correction, and binlog handling logic, and provides a complete Java example that demonstrates how to configure Flink SQL, MySQL source, and Kafka sink.
This article explains the background, challenges, overall architecture, core technology optimizations, edge‑computing integration, data modeling, serialization, and real‑world case studies of moving industrial IoT data to Alibaba Cloud, illustrating how cloud‑native solutions enable digital transformation in manufacturing.
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 the fundamentals of ETL, describes data‑warehouse architectures such as star and snowflake schemas, outlines a five‑step enterprise‑level ETL workflow, and discusses advanced techniques, tools, and algorithms for building robust data‑warehouse solutions.
This article provides an in‑depth technical overview of Flink CDC 2.0, covering its CDC fundamentals, comparison of query‑based and log‑based approaches, the new lock‑free chunk algorithm, FLIP‑27 based parallel snapshot reading, performance benchmarks, documentation improvements, and future roadmap for stability and ecosystem integration.
The article explains what a Customer Data Platform (CDP) is, why businesses need it to overcome fragmented multi‑channel data, enable fine‑grained operations and data‑driven growth, and outlines the key steps for building a CDP, including data collection, OneID unification, tagging, lifecycle management, and marketing execution.
This article explains the growing analytical demands in the big‑data era, the limitations of traditional OLAP, and how Kyligence’s distributed OLAP engine addresses data‑island issues, multi‑dimensional and many‑to‑many analysis, unified security, and performance optimization with MDX on Spark, delivering a seamless Excel‑like experience.
This article walks through the complete process of creating a high‑impact visual dashboard for large‑screen displays using FineReport, covering tool selection, data preparation, report creation, design, visual polishing, and adding dynamic effects.
This article provides a comprehensive guide to Spring Batch, covering its purpose, architecture, core components such as Job, Step, ItemReader/Writer/Processor, chunk processing, skip strategies, configuration tips, and common memory issues, all illustrated with code examples and diagrams.
This article explains what a big data platform is, outlines its seven‑component overall architecture, details the technical stack from data sources to applications, and describes the key subsystems such as catalog management, data integration, governance, storage, processing, sharing, development, and analysis.
The article details how NetEase Cloud Music co‑built a unified data service platform with NetEase YouShu, describing its architecture, phased development from internal use to online high‑concurrency services, feature enhancements such as API marketplace, multi‑source support, parameter conversion, and future roadmap for broader data products.
This article presents NetEase Game's Flink‑based streaming ETL system, detailing business background, log classifications, specialized and generic ETL services, Python UDF integration, runtime optimizations, HDFS write tuning, SLA metrics, fault‑tolerance mechanisms, and future roadmap for unified data lakes and PyFlink support.
This article details Zhihu’s transition from a Sqoop‑driven data integration system to a Flink‑centric platform, covering business scenarios, historical architecture, design goals, technology choices, performance optimizations, and future plans for unified streaming‑batch processing across diverse storage systems.
The article explains how Apache Kylin and MLSQL complement each other, detailing Kylin's OLAP strengths, MLSQL's data‑processing and AI capabilities, and demonstrates a low‑code integration that enables users to perform ETL directly within Kylin’s interface while outlining future deep‑link scenarios.
This article explains how Flink SQL can be used to construct a unified stream‑batch ETL pipeline for data warehouses and data lakes, covering data integration, CDC support, streaming writes to Hive and Iceberg, and various join techniques such as regular, interval, and temporal joins.
This article introduces Kafka Connect, explaining its purpose as a scalable and reliable tool for moving data between Apache Kafka and external systems, detailing its core concepts, architecture, deployment modes, configuration files, and a step‑by‑step example that streams data from a file source to a file sink.
The article reviews Flink 1.12's major enhancements, including comprehensive SQL capabilities, deep integration with Hive via catalog and streaming support, and a practical code example that demonstrates how to write streaming data into Hive tables while handling partition commits and small‑file merging.
The article explains how traditional point‑to‑point data integration leads to data quality, consistency and cost issues, introduces the concept of data services as a unified, reusable way to provide data, outlines their benefits, lifecycle stages, classification into data‑set and API services, and presents Huawei’s practical construction strategy and the “Three‑1s” supply‑chain goals.
This article introduces Ballerina’s unique language features, including native XML/JSON data types, datatable handling, inline definitions, parallel processing with workers and fork‑join, and the comprehensive development toolset such as Composer, Testerina, connectors, and editor plugins, illustrating code examples throughout.
This article explains how Spark can be combined with MongoDB for large‑scale data processing, covering Spark fundamentals, comparisons with HDFS, practical integration patterns, performance benefits, real‑world case studies, and detailed code examples for deployment and analytics.
This article provides an in-depth overview of ETL concepts, including extraction, transformation, loading, data warehouse architecture, and detailed discussion of Kettle (Pentaho Data Integration) features, design principles, components, transformations, jobs, database connections, metadata management, and practical examples for building robust data integration pipelines.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.
Flink SQL, the high‑level SQL interface for Apache Flink, offers language‑independent, dependency‑free, easy‑to‑use stream processing with advanced features such as DDL, UDFs, time semantics, windowing, pattern matching, and built‑in connectors, supporting data synchronization, batch‑stream fusion, Hive integration, and various product enhancements.
This article provides a comprehensive guide on integrating Hive with Spark SQL, covering Hive‑on‑Spark and Spark‑on‑Hive setups, spark‑shell and spark‑sql usage, HiveServer2 with Beeline, Scala scripts for reading and writing Hive tables, and partition handling for aggregated results.
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.
The article outlines Qunar’s challenges in aggregating international hotel data, analyzes issues such as localized address formats and limited text similarity parsing, and presents a pattern‑matching and weighted scoring approach that improves aggregation accuracy across multiple countries.
The article details Meituan Delivery’s A/B testing platform and evaluation system, explaining its closed‑loop design, multi‑strategy traffic allocation with AA grouping, comprehensive metric hierarchy, statistical rigor, data integration, and implementation architecture, and outlines future tools for traffic‑volume recommendation.
This article presents a comprehensive technical overview of the Real-time Data Platform (RTDP), detailing its overall architecture, component selection—including DBus, Kafka, Wormhole, Moonbox, and Davinci—design philosophies, functional features, and various deployment patterns such as synchronous, stream-processing, rotation, and intelligent modes.
This article explains the differences between Spark on Hive and Hive on Spark, then provides step‑by‑step instructions for configuring Hive MetaStore, setting up SparkSQL to use Hive, and demonstrates a complete Scala program that creates a Hive table, loads data, and queries it.
The article examines China's push to develop data factor markets and showcases how Suning’s integration of online and offline data, big‑data analytics, and AI is revitalizing traditional retail, illustrating the broader impact of digital transformation on the post‑pandemic economy.
