This article provides a comprehensive overview of Flink CDC 3.3, detailing its CDC fundamentals, new connectors, Transform module enhancements, asynchronous snapshot splitting, community adoption, and upcoming roadmap for broader ecosystem support and batch‑mode execution.
The author documents three StarRocks production issues: varchar(50) truncating MaxCompute strings, BE memory spikes caused by API gateway timeout mismatches, and a global dictionary error fixed by disabling low-cardinality optimization.
This guide explains how to integrate EMR Serverless Spark with the high‑performance Apache Doris analytical database, covering prerequisites, connector download, OSS upload, network configuration, table creation, and both SQL‑session and Notebook examples for reading and writing Doris tables.
This step‑by‑step guide explains how to use EMR Serverless Spark together with the StarRocks Spark Connector to create a workspace, upload the connector JAR, configure network connections, create databases and tables in StarRocks, and perform read/write operations via SQL sessions, Notebook sessions, or batch Spark jobs, complete with code examples and UI screenshots.
In today’s fast‑changing market, traditional product planning falls short, so this article explains how AI‑powered data integration, predictive analytics, and dynamic feedback loops can create a real‑time, data‑driven product roadmap, detailing three implementation phases—data unification, intelligent analysis, and continuous adjustment—with practical steps for product managers.
This article presents a comprehensive overview of Alibaba Cloud's modern data stack built on Flink CDC, detailing its core concepts, extended capabilities, typical application scenarios, performance optimizations, a live demo, and future development plans for large‑scale streaming data integration.
The article explains Anthropic’s Model Context Protocol (MCP), a universal interface that lets AI assistants like Claude query and manipulate data sources—including SQLite databases—using plain natural language, and compares it with existing tools such as Chat2DB.
This article summarizes an advanced Flink CDC presentation, covering Flink CDC fundamentals, real‑time Flink integration, CDC‑YAML core capabilities, supported sync links, Transform and Route modules, monitoring metrics, schema‑change strategies, typical use cases, performance optimizations, demo implementations, and future development plans.
Alibaba Cloud DataWorks’ Data Integration platform, built on Flink CDC, offers a comprehensive, serverless solution for real‑time and batch data lake ingestion, detailing its architecture, elastic scaling, productized use cases, and future roadmap, including AI‑driven diagnostics and expanded source support.
This article introduces Flink CDC YAML, outlines its core capabilities and application scenarios, compares it with SQL and DataStream jobs, showcases enterprise‑grade features of Alibaba Cloud Flink CDC, and provides a step‑by‑step tutorial to build a complete CDC YAML job in just ten minutes.
Bilibili migrated its massive user‑behavior, commercial AI training, and database synchronization pipelines from Hive and Kafka to an Iceberg‑based streaming‑batch architecture, using Flink and the Magnus optimizer to achieve minute‑level freshness, reduce CPU and memory usage by about 20‑22 %, save roughly 3.55 M CNY annually, and dramatically improve query latency and join performance.
This article outlines the motivations, objectives, and architectural design of a next‑generation data management platform, detailing its four‑layer “four‑ization” approach, core services such as data integration, modeling, API provisioning, componentization, as well as governance, security, and operational best practices.
This article reviews the evolution of data‑integration architectures toward EtLT, explains the core capabilities of Apache SeaTunnel, and details how a Chinese data‑platform vendor applied and extended SeaTunnel to simplify batch and streaming ingestion, unify multi‑engine processing, and reduce development and operational costs.
The article outlines how to obtain the DataFun Summit e‑book by following the public account instructions and provides concise English summaries of twelve technical sessions covering data lineage, integration, AI language models, multimodal content, game AI agents, lake‑warehouse governance, big‑data architecture, and cluster management.
This article describes Siemens' journey in applying generative AI and Retrieval‑Augmented Generation to create an internal knowledge chatbot, detailing the business challenges, technical architecture, data integration, multi‑modal capabilities, deployment outcomes, and strategic lessons for enterprise AI adoption.
This guide walks through the challenges of synchronizing massive datasets across heterogeneous databases, introduces Alibaba's open‑source DataX tool, explains its framework‑plugin architecture, and provides step‑by‑step instructions—including environment setup, installation, job configuration, and both full and incremental MySQL synchronization—complete with code examples and performance metrics.
This guide outlines a practical six‑step methodology—covering overall planning, data integration, model construction, data development, asset management, and data services—to help enterprises build a robust data middle platform that unlocks business value and supports agile digital transformation.
This article details Haijing Technology's challenges with real‑time telecom data analysis, explains why traditional Hadoop and ClickHouse solutions fell short, and shows how ByConity's unified engine, multi‑table joins, and elastic scaling enable efficient, low‑latency analytics across complex B‑O‑M domains.
This article examines OpenAI's recent ChatGPT API update that adds JSON‑formatted responses, detailing the technical background, implementation steps, example requests and responses, and the broader impact on developers, enterprises, and future AI applications.
This article explains the concept, design principles, and core components of a data middle platform, detailing its overall, functional, layered, logical, and data architectures, as well as the specific platforms for data collection, processing, organization, governance, quality, sharing, and visualization, illustrated with diagrams.
This guide provides a comprehensive, beginner‑to‑advanced reference for ODPS (MaxCompute) SQL, covering table creation, DDL/DML commands, query syntax, join hints, MySQL‑to‑ODPS synchronization, one‑click and custom imports into Hologres, and scheduling variables for automated data pipelines.
This article presents JD Logistics' one‑stop agile BI platform, detailing the complex data sources, rapid business demands, the UData solution architecture, performance and usability improvements, and future upgrade plans that together enable faster data integration, self‑service reporting, and enhanced decision‑making across the organization.
The article examines the evolution of data integration architectures—from traditional ETL through ELT to the emerging EtLT model—highlighting their advantages, disadvantages, industry trends, maturity stages, and practical guidance for enterprises and professionals navigating modern big‑data pipelines.
The article presents Ant Group's exploration of applying its data‑driven risk control and credit assessment capabilities to the traditional bulk commodity sector, detailing industry background, data pain points, core technical solutions, and the construction of a secure, explainable data‑model platform for digital transformation.
This article explores how conversational AI, powered by large language models, is reshaping enterprise operations across five key scenarios—from customer support assistants and AI‑driven data interfaces to HR bots, unstructured data processing, and multi‑agent digital assistants—highlighting benefits, implementation considerations, and privacy challenges.
Data middle platforms transform raw enterprise data into reusable assets by integrating collection, storage, processing, governance, and service layers, enabling faster deployment, consistent metrics, improved data quality, and business value across digital transformation, while addressing challenges like siloed data, low efficiency, and inconsistent standards.
This article presents Li Auto’s end‑to‑end data integration journey, detailing the evolution of its data platform, the challenges of heterogeneous sources, and how a unified Flink‑on‑K8s solution with cloud‑native architecture, operator management, monitoring, and checkpointing addresses batch‑stream convergence and future scalability.
This article explains the fundamentals of Data Fabric and data virtualization, highlights the limitations of traditional centralized data warehouses, describes the three‑layer virtualization architecture, and presents a detailed securities‑industry case study that demonstrates cost, efficiency, and compliance benefits.
The article analyzes how large‑model medical AI is rapidly adopted yet struggles with implementation due to doctor shortages, behavioral resistance, data silos, safety regulations, and the need for strategic alignment, while contrasting the more supportive innovation ecosystem in the United States.
This article introduces ByteDance's open‑source data integration engine BitSail, covering its background, layered architecture, recent feature enhancements, automated testing framework, CDC‑based full‑library synchronization solutions, and future development plans for connectors and real‑time data consistency.
This article presents JD Logistics' one‑stop agile BI platform, detailing the complex data sources, rapid requirement changes, and Chinese‑style reporting challenges it addresses, while outlining the UData solution, product methodology, performance enhancements, and real‑world case studies that demonstrate significant efficiency gains.
Ant Group shares its comprehensive knowledge graph initiatives, detailing the fundamentals, construction pipeline, fusion techniques, cognitive representations, diverse business applications, and the emerging synergy between knowledge graphs and large language models, illustrating how graph-based AI enhances accuracy, interpretability, and downstream services.
An ultra‑large hedge fund with over $10 billion AUM replaced ksqlDB and Flink with RisingWave, leveraging its PostgreSQL‑compatible streaming SQL to achieve sub‑10 ms latency, lower learning and operational costs, rich connectors, advanced operators, and comprehensive observability for real‑time trade data processing.
The article explains how the data team adopted the open‑source machine‑learning platform MindsDB to simplify data integration, enable SQL‑based model training and inference, manage model versions, and dramatically shorten recommendation system development cycles, achieving up to 30% efficiency gains.
This guide explains how to use Alibaba Cloud Message Queue Kafka's no‑code, fully managed, serverless dump feature to transfer data to OSS, covering its benefits, typical scenarios, required prerequisites, step‑by‑step configuration, testing, and verification of the resulting objects.
Li Auto built a cloud‑native data‑integration platform by deploying Flink on Kubernetes, unifying batch and streaming workloads with a storage layer (JuiceFS + BOS) and Flink Operator, enabling simple source‑sink pipelines, elastic scaling, automated checkpointing, and centralized monitoring while addressing earlier fragmentation and resource inefficiencies.
This article details Li Auto's end‑to‑end data integration practice using Flink on Kubernetes, covering the evolution of their integration platform, architectural design, cloud‑native deployment, operational challenges, and future roadmap, while highlighting unified batch‑stream processing and resource elasticity.
The article explains how Flink CDC 3.0 transforms traditional CDC pipelines into an end‑to‑end streaming ELT framework, offers practical guidance for describing such projects on resumes and in interviews, and outlines future challenges and development directions for large‑scale data integration.
This article examines the rapid growth of China's database market, the technical hurdles of moving databases to public cloud—including engine selection, lengthy migration processes, efficiency, disaster recovery, and data consistency—and explains how Baidu Intelligent Cloud's DTS platform offers a smooth, reliable, high‑availability, and high‑performance one‑stop solution with real‑world use cases.
Enterprises face significant hurdles in data governance and integration, from siloed systems and unclear responsibilities to poor data quality, but by establishing clear rules, fostering user department engagement, and aligning governance with business-driven data applications, they can create a cohesive data asset management framework that drives value.
This article explains how Alibaba Cloud's Serverless Function Compute combined with Database Change Data Capture (CDC) creates a complete, real‑time ETL pipeline, detailing the ETL model, DTS integration, architecture components, event‑driven processing, and practical use cases such as OLTP‑to‑OLAP data flow.
The presentation by Aloudata Vice President Yu Jun introduces his extensive background in large‑scale internet and big‑data platforms and outlines how Data Fabric and data virtualization can be applied to data integration, highlighting the differences from traditional solutions and the business value of logical data warehouses.
This article introduces the concept of Data Fabric, explains how Dipu Technology built the FastData real‑time intelligent lakehouse platform on top of it, describes its architecture, core advantages, practical use cases in energy and retail, and outlines the platform’s future roadmap.
This article outlines how a marketing‑focused data middle platform can be constructed by integrating online and offline behavior data, business data, and third‑party sources, then applying data integration, modeling, processing, and application capabilities to enable data‑driven user journeys and personalized marketing strategies.
Learn how to capture MySQL data changes using Debezium's CDC capabilities within a Spring Boot application, avoiding heavyweight message brokers by leveraging binlog monitoring, configuring connectors, handling snapshots, and processing change events for use cases like cache invalidation, data integration, and simplifying monolithic architectures.
The Common Data Model provides a shared, standardized data language and metadata system that simplifies cross‑application data integration, reduces custom development effort, and enables consistent, extensible data structures for business and analytics scenarios across Microsoft Power Platform and Azure services.
This article explains why lake‑warehouse fusion is needed, describes the challenges of integrating real‑time data warehouses with data lakes, introduces a new StarRocks‑based architecture that supports real‑time ingestion, cooling, offline loading, and adaptive hot‑cold query rewriting, and outlines future plans and Q&A.
Redis 7.2, the first Unified Redis Release, introduces extensive AI support, vector database capabilities, scalable search, server‑side Triggers and Functions, enhanced geospatial queries, performance‑boosted sorted sets, and the Redis Data Integration tool, while expanding client and protocol compatibility.
This guide reviews the most popular ETL and data integration tools—including Kettle, DataX, DataPipeline, Talend, DataStage, Sqoop, FineDataLink, and Canal—detailing their core features, architectures, and typical use cases to help you choose the right solution for data migration and synchronization.
The article details Kuaishou’s KwaiBI business intelligence platform evolution, covering its 1.0 tool‑based implementation, the 2.0 standardized architecture built on an indicator middle‑platform, core processes, data integration, self‑service features, and future directions for self‑service and intelligent analytics.
This article details Lenovo's implementation of ClickHouse in a manufacturing environment, covering the current data landscape, cluster architecture, integration challenges, performance enhancements, and solutions such as Seatunnel and query pre‑aggregation, illustrating how OLAP engines can address real‑time analytics and concurrency issues in production data pipelines.
This article explains why fragmented data hampers business insight in large enterprises and provides a comprehensive overview of master data concepts, governance structures, standards, processes, and step‑by‑step implementation practices to achieve consistent, high‑quality enterprise data.
The article explains data fabric architecture as a promising approach for enabling data exchange across distributed systems, outlines its three design patterns, describes key technical components such as data virtualization, data catalog, and knowledge graphs, and discusses the trade‑offs, costs, and limitations that organizations must consider.
Didi’s data serviceization converts raw business data into consumable services through a four‑stage pipeline—integration, development, production, and back‑flow—while the Data Dream Factory and Shu‑Chain platform automate synchronization, provide a unified access gateway for thousands of APIs, and introduce a standard metric service that abstracts storage complexities and ensures high‑performance, secure data delivery.
This article outlines the challenges of scaling data integration platforms, proposes a comprehensive solution using Apache Seatunnel and Dinky, details the implementation of Trino and Kyuubi JDBC support, and describes the platform's architecture, task publishing workflow, logging, monitoring, resource management, and future enhancements.
Enterprise architects align IT strategy with business goals by overseeing application portfolio management, technology and risk, IT operations, security and privacy, integration and data, and finance, defining roadmaps for 1‑3‑5 year plans while balancing strategic and tactical responsibilities in a rapidly changing environment.
ETL extracts, transforms, then loads data, while ELT extracts, loads, and transforms later, each offering distinct advantages; the article compares their processes, key differences, and factors such as data volume, complexity, latency, and cost to help businesses choose the optimal integration approach.
This article explains how Huawei Cloud’s IoT platform connects devices, leverages edge computing, stores data in RDS, and visualizes it on an Astro real-time dashboard, offering a step-by-step guide for developers to build end-to-end IoT solutions.
This article explains data integration fundamentals, compares data integration tools such as Stitch, Fivetran, and Airbyte, describes the concepts of data warehouses and data lakes, outlines ETL vs ELT processes, and explores building modern data stacks with Flink CDC and cloud services.
The article outlines how a unified, big‑data‑based user profiling platform addresses traditional data silos, high costs, and limited functionality by standardizing tags, integrating Spark and RHadoop processing, and enabling a closed‑loop marketing workflow that improves accuracy and operational efficiency.
This article explains SeaTunnel’s architecture and its StarRocks connector, detailing source and sink features such as field projection, predicate push‑down, parallel reading, state recovery, data type mapping, Stream Load writes, CDC support, configuration examples, and future roadmap for exactly‑once semantics.
The article explains the concept, benefits, and detailed modular architecture of a data middle platform, covering data storage, acquisition, processing, governance, security, and operation frameworks, and illustrates how enterprises can build and evolve such platforms to turn data into valuable services.
This article explains the concept of Change Data Capture (CDC), outlines its common use cases, compares the main technical approaches—including timestamps, data diff, triggers, and log‑based methods—and reviews popular open‑source CDC solutions and their database‑specific configuration requirements.
This article reviews eight widely used ETL and data‑migration tools—including Kettle, DataX, DataPipeline, Talend, DataStage, Sqoop, FineDataLink, and Canal—detailing their core features, architectures, supported data sources, and typical usage scenarios to help practitioners choose the right solution.
This article presents a comprehensive interview with several big‑data platform experts, outlining the core components such as data integration, storage and computation, distributed scheduling, and query analysis, while also highlighting current challenges, best‑practice tools, and future trends in big‑data architecture.
This article details how Hangzhou Bank Consumer Finance modernized its big‑data platform by introducing Apache Doris 1.2, replacing the original Greenplum + CDH architecture, unifying data sources via Multi‑Catalog, achieving second‑level query latency, reducing storage and compute costs, and outlining the integration workflow with DolphinScheduler, SeaTunnel, and Spark.
The article describes how a comprehensive digital platform combines structured, semi‑structured, and panoramic data with machine‑learning valuation models, natural‑language processing, and VR technology to make used‑car condition information transparent, improve estimation accuracy, and enhance user decision‑making in the Chinese second‑hand car market.
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.
The interview with Alibaba researcher Wang Feng reviews Flink's eight‑year journey to a top Apache project, its massive scale at Double 11, the push toward unified stream‑batch computing, emerging storage challenges, and the roadmap for cloud‑native, real‑time data warehousing.
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.
