This article explains how the lakehouse integrated architecture combines data lake and data warehouse capabilities, outlines its key features, compares three implementation paths, and provides an in‑depth technical overview of Apache Hudi and Apache Iceberg for modern big‑data analytics.
This guide demonstrates how to configure Hive metastore, connect SparkSQL to Apache Hudi, create COW and MOR tables, perform insert, update, merge, delete, and insert‑overwrite operations, and illustrates each step with executable code snippets and sample results.
This article traces 20 years of big‑data evolution, compares data lakes and data warehouses, defines both concepts, examines their technical trade‑offs, and presents Alibaba Cloud’s lake‑warehouse (lakehouse) solution that unifies flexible storage with enterprise‑grade performance and governance.
Alibaba Cloud’s cloud‑native data lake analysis solution combines fully managed storage (OSS‑HDFS), a one‑stop lake management platform (Data Lake Formation), and multimodal compute capabilities, delivering high performance, massive scalability, and low cost for big‑data and AI workloads across offline, real‑time, and lake‑house scenarios.
This interview with NetEase’s data‑lake technology manager explores the distinction between data lakes and lakehouses, the evolution of table‑format technologies such as Iceberg, Hudi and Delta Lake, their maturity across key capabilities, and the practical adoption challenges faced by enterprises.
This article introduces the Iceberg table format, explains its core architecture and advantages such as transactionality, implicit partitioning and row‑level updates, details Xiaomi's practical deployments—including CDC pipelines, partition strategies, compaction services, and stream‑batch integration—and outlines future development directions.
This article details how NetEase Yanxuan transformed its data platform from a dual Lambda architecture to a unified batch‑stream solution built on Apache Iceberg, covering the original challenges, the evaluation of Iceberg versus Hudi and Delta Lake, implementation of stream‑batch pipelines, message ordering fixes, snapshot generation, and extensive table‑governance optimizations.
This article details T3 Travel’s exploration of the Modern Data Stack, describing its four‑point overview, business scenarios, the initial MDS implementation using Apache Hudi and Kyuubi, and the design of a feature platform that integrates Metricflow, Feast, and other components to support data processing, analytics, and machine‑learning workflows.
This article explains how Presto and Alluxio work together to query Iceberg tables, describes their architectures, deployment options, best‑practice recommendations such as using Iceberg native catalogs and local caches, and outlines future research directions for improving CPU usage and off‑heap caching.
This article explains the definition of data lakes (public‑cloud and non‑public‑cloud), outlines their key characteristics, presents three typical business scenarios—real‑time event analysis, change‑data analysis, and stream‑batch integration—summarizes required product features, evaluates open‑source lake formats, and details iQIYI's adoption of Apache Iceberg across multiple services to achieve low‑latency, large‑scale, cost‑effective analytics.
This article summarizes Alibaba Cloud senior product expert He Yuan's presentation on EMR 2.0, outlining the challenges of open‑source big data, the evolution of EMR, and the new features—including cloud‑native architecture, enhanced performance, diverse resource models, and expanded analysis scenarios—aimed at reducing cost and complexity.
This article presents an in‑depth overview of real‑time data‑warehouse scenarios, discusses challenges such as timeliness, update efficiency, and resource consumption, and details practical solutions using Apache Hudi, Flink, Presto, and related optimizations for ingestion, indexing, compaction, and query performance.
iQIYI’s data lake combines public‑cloud and private storage with Apache Iceberg’s snapshot‑based table format to enable near‑real‑time, unified batch‑and‑stream analytics, reducing costs, simplifying architecture, and improving data freshness across use cases such as log collection, audit, pingback, and member order processing.
This article explains how ByteDance designed and deployed a real‑time data warehouse on a data lake using Hudi, detailing three business scenarios, the challenges of latency, consistency and resource usage, and the engineering solutions—including upserts, compaction services, indexing, and future unified storage plans.
This article explores the ongoing debate between data lakes and data warehouses, clarifies their distinct purposes and technologies, discusses how they can coexist or complement each other, and introduces the concept of an integrated lakehouse architecture while promoting a comprehensive data intelligence knowledge map.
This article presents a comprehensive overview of Iceberg's adoption in Huawei Terminal Cloud, covering its architectural overview, key features such as Git‑style data management, real‑time processing, acceleration layers, and future development directions, along with a Q&A session addressing performance and implementation details.
This comprehensive guide explains the evolution from traditional data warehouses to modern data lakes, detailing concepts, architectures, differences, implementation steps, and real‑world case studies, while also comparing major cloud providers' solutions and highlighting how data platforms support digital transformation and analytics.
This article presents Glacier, OPPO's intelligent data lake solution that builds on Iceberg Table Format to provide real‑time data ingestion, low‑latency queries, advanced indexing, and robust multi‑version management for both structured and unstructured data, tightly integrating with machine‑learning workflows.
This article presents a comprehensive solution for multi‑stream joins in Apache Hudi, detailing the challenges of dimension and multi‑stream joins, the novel storage‑layer join approach, timeline‑based concurrency control, marker mechanisms, early conflict detection, payload customization, and practical usage with Flink and Spark, along with performance benefits and future directions.
The article details how Tencent Advertising re‑architected its massive log pipeline by consolidating heterogeneous real‑time and offline logs into an Iceberg‑based data lake, introducing multi‑level partitioning, Spark and Flink ingestion, and numerous performance and cost optimizations for scalable big‑data analytics.
This article explains why real‑time data warehouses are becoming essential, outlines their goals, compares them with traditional offline warehouses, and presents detailed design patterns, naming conventions, and case studies from Didi, Kuaishou, Tencent, Youzan and other enterprises, highlighting challenges and solutions for streaming, storage, and query layers.
This article presents a comprehensive overview of TikTok e-commerce's near‑real‑time data lake implementation, detailing data lake characteristics, architecture choices, practical use cases across analysis and operations, and for future challenges and plans.
This technical article presents a comprehensive overview of migrating Hive SQL to Flink SQL, covering the motivations behind the migration, key challenges such as compatibility, stability and performance, practical implementation steps, a detailed demo, future development directions, and a Q&A session addressing common concerns.
This article introduces NetEase’s Arctic, a real‑time lakehouse system built on Apache Iceberg that unifies streaming and batch processing, explains the challenges of Lambda architecture, details Arctic’s features such as change/base stores, hidden queue, transaction handling, and shares internal practice cases and future roadmap.
This article traces 20 years of big‑data evolution, defines data lakes and data warehouses, compares their trade‑offs, and explains how lakehouse solutions—exemplified by Alibaba Cloud MaxCompute—merge flexibility with enterprise‑grade performance to lower total ownership cost.
This article explains how Alibaba Cloud EMR StarRocks extends data lake analytics to support Hive, Iceberg, and Hudi, detailing its architecture, Iceberg integration, performance gains over Trino, IO merging, lazy materialization, intelligent caching, and elastic compute capabilities for faster, unified, and cost‑effective queries.
The article introduces the Lakehouse Analysis Service (LAS), explains its layered architecture that unifies data lake and warehouse capabilities, discusses challenges with Apache Hudi metadata and consistency, and details the design of the unified MetaServer, Table Management Service, concurrency control, async compaction, event bus, and future roadmap.
This article explains the fundamental concepts of data warehouses and data lakes, compares their architectures and use cases, discusses common misconceptions, highlights real‑world examples such as Facebook, and outlines the challenges and strategic considerations for organizations adopting both technologies.
This article introduces Iceberg’s table‑format fundamentals, details Xiaomi’s large‑scale deployment of Iceberg for CDC and log ingestion, explores their streaming‑batch integration experiments, outlines future roadmap items, and provides a comprehensive Q&A covering practical challenges and solutions.
This article explains ByteDance’s data lake technology, its Apache Hudi‑based features, near‑real‑time architecture, and practical e‑commerce use cases such as marketing promotion, traffic diagnosis, logistics monitoring, risk governance, and operational monitoring, while outlining future challenges and plans.
This article presents an overview of the Iceberg table format, its core architecture and advantages, details Xiaomi’s large‑scale deployment and use cases, explores stream‑batch integration with Spark and Flink, outlines data correction methods, future plans, and answers common technical questions.
This article presents ByteDance's evolution of data lake indexing built on Apache Hudi, detailing traditional update challenges, Hudi's index mechanisms, the introduction of bucket and extensible hash indexes, query optimizations, and upcoming multi‑modal and range index innovations.
The talk outlines how AI training’s growing data and compute demands create storage bottlenecks across four evolutionary stages, identifies four core problems—massive data, data‑flow, resource scheduling, and compute acceleration—and proposes hardware, software (parallel file systems, caching), and cloud‑native orchestration (Fluid, Baidu Canghai) solutions that combine object‑storage lakes with high‑performance acceleration layers to achieve near‑full GPU utilization.
This article presents an in‑depth overview of Apache Iceberg as used at Tencent, covering its table format architecture, Spark read/write mechanisms, production challenges and optimizations such as schema evolution, file filtering, upsert strategies, and the surrounding data‑governance services.
Arctic, NetEase’s streaming lakehouse built on Apache Iceberg, unifies streaming and batch workloads with millisecond‑level latency, Hive compatibility, and built‑in message‑queue support, delivering CDC, upserts and OLAP without a Lambda architecture, as demonstrated by real‑time processing of 2 PB of Hive data for Cloud Music.
This article examines the challenges of using Apache Hudi for real‑time data lake writes, analyzes the COW and MOR write models, and presents a custom master‑worker architecture with index optimization and repartitioning that reduces write resource consumption by over 85% while boosting throughput up to 300‑fold.
This article examines the comprehensive challenges AI applications face from storage to compute, traces the evolution of AI training infrastructure, analyzes key bottlenecks such as compute acceleration, resource scheduling, massive data handling and data flow, and presents Baidu Cloud's storage acceleration solutions—including parallel file systems, caching, and the Fluid scheduler—to dramatically improve AI training performance.
This talk introduces the evolution of data lakes, outlines Apache Hudi’s core features, details the Flink‑Hudi integration architecture—including write pipelines, small‑file handling, and read strategies—covers real‑world use cases such as near‑real‑time DB ingestion, OLAP, and ETL, and previews upcoming Hudi roadmap items.
This article explains how an e‑commerce company designed and implemented a modern data warehouse—combining batch Spark jobs, real‑time Flink streams, and Hudi data‑lake storage—to handle terabytes of daily logs, ensure data quality, and provide fast, reliable analytics for business decision‑making.
This article presents a comprehensive overview of using Flink with Apache Hudi to build streaming data lake solutions, covering Hudi's background, core features, Flink‑Hudi integration design, practical use cases, recent roadmap updates, and a Q&A session.
This guide details how to execute Apache Hudi file clustering after a batch job and before streaming, using Spark commands to merge numerous small HDFS files into larger ones, configure clustering and cleaning policies, and verify the results with HDFS counts.
This article explains how Amazon EMR Serverless leverages serverless architecture to simplify, scale, and reduce the cost of big data analytics by providing managed Hadoop‑based services, flexible resource allocation, built‑in security, and seamless integration with the AWS data lake ecosystem.
The presentation by TikTok e‑commerce data‑warehouse engineer Ma Wenyuan explains data‑lake characteristics, near‑real‑time architecture, and practical e‑commerce use cases, highlighting Apache Hudi features, hybrid batch‑stream processing, and future challenges for scaling and integration.
GooseFS, Tencent Cloud’s Hadoop‑compatible storage accelerator, adds a local NVMe‑SSD cache layer to cloud‑native data lakes, letting users boost query speeds by up to 46 % and cut backend bandwidth by 200 Gbps without code changes, as demonstrated by a music‑industry customer’s 200‑node deployment caching ten million files.
This article explains the challenges of data lake adoption and details Alibaba Cloud’s metadata warehouse architecture, construction, search capabilities, asset analysis, fine‑grained profiling, and lifecycle management that together enable efficient, cloud‑native big data management.
This presentation details ByteDance's real‑time data‑warehouse implementations using Apache Hudi, covering scenario classifications, challenges of traditional offline warehouses, practical solutions for ingestion, upsert, validation, indexing, query optimization, and future plans for extensible indexing and unified batch‑stream processing.
This article reviews the evolution of data warehouse architectures, explains Amazon's serverless real-time data lake design and its key services, and details Amazon Redshift's cloud-native real-time data warehouse features, streaming ingestion, and integrated machine learning capabilities.
This article explains the architecture of Apache Doris, introduces Apache Hudi as a data‑lake format, compares Lambda and Kappa approaches, and details the design, implementation steps, and future roadmap for querying Hudi tables directly from Doris.
This article traces the history of data lakes from their 2010 inception with Hadoop through cloud‑native object storage, lakehouse formats like Delta Lake, and Alibaba Cloud's multi‑layer solution, outlining key architectural stages and practical construction challenges for enterprise‑grade implementations.
The article explains how data lakes excel at ingesting massive, varied data, data warehouses optimize storage and query performance, and lake‑house architectures combine both strengths—offering scalable, low‑cost storage with high‑speed analytics—highlighting industry solutions from Snowflake, Databricks, and major cloud providers.
This article explains the background, architecture, design choices, and step‑by‑step implementation for enabling Apache Doris to query Hudi data lake tables, covering Doris features, Hudi formats, Lambda/Kappa architectures, solution alternatives, and future roadmap for real‑time analytics.
The DataFun Summit 2022 featured a Data Lake & Warehouse forum with expert talks on PALO, ByteDance LAS, Iceberg at Huawei, and Presto‑Alluxio acceleration, providing detailed technical outlines, speaker backgrounds, and audience takeaways for modern big‑data architectures.
This article details how NetEase Yanxuan upgraded its legacy Lambda data pipeline to a unified batch‑stream architecture built on Apache Iceberg, covering the original challenges, the evaluation of Iceberg versus Hudi and DeltaLake, implementation specifics, table‑governance techniques, and future roadmap.
The article examines the open‑source release of Delta 2.0, compares its features and benchmark results with Iceberg and Hudi, discusses the core capabilities required by enterprises for a lakehouse architecture, and introduces the Arctic project as a multi‑engine streaming lake service.
The article reviews recent developments in data‑lake table formats—Delta Lake 2.0, Iceberg, and Hudi—examining their features, benchmark results, and ecosystem impact, and then introduces Arctic, an open‑source streaming lakehouse service built on Iceberg that aims to bridge batch‑stream gaps for enterprises.
This article analyzes Baidu Intelligent Cloud's data‑lake acceleration strategy, covering the evolution of big‑data architectures, the advantages and challenges of compute‑storage separation, the native hierarchical namespace and RapidFS cache solutions, performance test results, and recommended deployment patterns.
This talk by eBay software engineer Zhu Feng explains the fundamentals of Delta Lake and Lakehouse architecture, outlines eBay’s migration from Teradata to a Spark‑based platform, and details the custom enhancements, performance optimizations, and operational improvements implemented to support large‑scale update and delete workloads.
The presentation details how Amazon Web Services’ intelligent data lake architecture integrates big data and machine learning to overcome marketing challenges, improve data governance, and provide scalable, real‑time analytics for personalized, data‑driven marketing across enterprises.
This article provides a comprehensive overview of data lakes, explaining their definition, key characteristics, architectural evolution, and detailed comparisons of major cloud providers' solutions, while also presenting typical use cases, construction processes, and future development directions for this emerging big‑data infrastructure.
MongoDB is evolving from a purely operational document store to a hybrid system that embeds native analytics, cloud‑native features, and SQL access, aiming to boost developer productivity, support real‑time insights, and complement rather than replace traditional data warehouses.
This article explains Baidu Intelligent Cloud’s data lake acceleration solution, covering the evolution of big‑data technologies, the benefits and challenges of compute‑storage separation, the architecture of BOS object storage, and the native hierarchical namespace and RapidFS cache mechanisms that boost performance and reduce costs.
This article provides a comprehensive tutorial on setting up Flink 1.11 with Iceberg 0.11.1, creating Hive catalogs, building databases and tables, inserting data, and exploring Iceberg components, file structures, partitioned tables, execution plans, and programmatic access via Scala.
This article explains Apache Doris’s architecture, introduces the Hudi data‑lake format, compares Lambda and Kappa approaches, and details the design and implementation of Doris’s Hudi external table support, including practical steps, code examples, and future roadmap.
The article compares Data Fabric and Data Mesh as modern data‑architecture approaches, explains their technical and organizational differences, discusses the ongoing debate between data lakes, warehouses, and lakehouses, and highlights how each option fits varying data‑type and usage scenarios.
This article explains the storage challenges of high‑performance computing—including traditional HPC, AI‑driven HPC, and HPDA—then details Baidu’s unified storage platform, object storage BOS, and runtime solutions PFS and RapidFS, illustrating their architecture, features, and a real‑world autonomous‑driving customer case.
This article introduces Apache Doris, an MPP analytical database, and explains how it integrates with the Hudi data lake format, covering architectural features, design choices, implementation steps including external table creation and query processing, and outlines future enhancements for supporting MOR snapshots and incremental queries.
The article provides an in‑depth overview of Apache Hudi 0.11.0, covering its new multi‑level index design, Spark SQL enhancements, Flink integration improvements, and additional performance and usability features aimed at boosting read/write efficiency in large‑scale data lake environments.
Bilibili’s lakehouse architecture merges Iceberg‑based data lake flexibility with data‑warehouse efficiency, using Kafka‑Flink real‑time ingestion, Spark offline loads, Trino queries, Alluxio caching, Z‑Order/Hilbert sorting, and enhanced BloomFilter and bitmap indexes to boost query speed up to tenfold while drastically cutting file reads.
This article details why Microvision adopted Apache Iceberg, how it replaces parts of their Lambda‑architecture data pipeline, the real‑time and offline use cases, table‑maintenance practices such as snapshot cleanup and small‑file merging, and lessons learned from the implementation.
This article explains how Spark reads data from Apache Iceberg tables by parsing snapshots and manifest files into DataFile objects, creates Batch and InputPartition objects, uses readers to materialize InternalRows, and then demonstrates how Iceberg's RewriteDataFilesAction can merge tiny Parquet files into larger ones through Spark‑driven tasks.
This article presents a comprehensive overview of how Amazon EMR Serverless leverages serverless technology to simplify, scale, and cost‑optimize big data analytics, covering the evolution of serverless services, the intelligent lakehouse architecture, core concepts, key benefits, common use cases, and available documentation.
This article explains Apache Iceberg’s architecture, core capabilities such as time‑travel, fast scans, delete handling, and schema evolution, and provides a step‑by‑step guide for configuring Flink to use Iceberg with Hive and Hadoop catalogs, including DDL commands and streaming queries.
This article explains the Apache Iceberg file storage format, its metadata hierarchy, and demonstrates how Spark and Flink write data to Iceberg tables, including detailed code examples, manifest handling, snapshot management, and commit processes for efficient data lake operations.
This article traces the evolution of enterprise data platforms—from early data warehouses to modern data lakes and the emerging lakehouse—detailing key technologies, challenges, and best practices for storage, compute engines, metadata, and integration, while highlighting how cloud-native object storage reshapes scalability and cost.
ByteDance tackled massive feature‑storage challenges by replacing row‑based HDFS files with columnar Parquet and the Iceberg table format, enabling schema evolution, selective reads, efficient backfill, and training optimizations that cut storage costs by over 40% and reduced CPU and network I/O dramatically.
This article explains the concept and challenges of data lakes, describes the “impossible triangle” of storage, compute, and cost, and introduces the open‑source SPL engine that provides multi‑source, file‑based, high‑performance computing to overcome those limitations.
This article explains Bilibili's lake‑warehouse integrated architecture, describing how Iceberg, Z‑Order sorting, and advanced indexing techniques such as BloomFilter and BitMap are used to accelerate queries and improve data organization in large‑scale analytics workloads.
The article examines the inherent trade‑offs of data lakes—maintaining raw data, enabling efficient computation, and keeping costs low—explains why traditional data‑warehouse approaches fall short, and introduces the open‑source SPL engine that provides multi‑source, file‑based, high‑performance analytics to overcome these limitations.
This article explains how Apache Flink integrates with Apache Hudi to enable real‑time data lake ingestion, covering the evolution from traditional data warehouses to data lakes, Hudi’s core concepts such as timeline and file grouping, copy‑on‑write vs merge‑on‑read modes, and Flink’s CDC‑based ETL pipeline.
This article explains how Delta Lake adds reliability to data lakes by offering ACID transactions, scalable metadata, and unified batch‑and‑stream processing, outlines the challenges it solves, details its implementation principles, and demonstrates a practical demo for building an integrated data warehouse.
This article provides a comprehensive guide to Apache Hudi, covering its basic concepts, timeline architecture, storage types (Copy‑On‑Write and Merge‑On‑Read), write operations, DeltaStreamer usage, Hive/Spark/Presto query integration, data management, indexing, compaction, and best‑practice recommendations for big‑data lake workloads.
This presentation provides a comprehensive overview of JuiceFS, an open‑source cloud‑native distributed file system, detailing its role in modern data lake and lakehouse architectures, comparing it with HDFS and object storage, and highlighting its performance, integration, and community ecosystem.
This article explains the fundamentals of data lakes and data warehouses, compares their architectures, outlines the challenges of data lakes, and then dives deep into Delta Lake's core features, storage model, ACID guarantees, concurrency handling, and provides step‑by‑step Spark code examples for practical use.
This article explains StarRocks' streamlined architecture, cost‑based optimizer, massively parallel processing and vectorized engine, and how they enable high‑performance queries over data stored in Hive, Iceberg, Hudi and other lake formats, backed by benchmark results and future roadmap details.
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 explains how NetEase Yanxuan evolved from a traditional data‑warehouse pipeline to a cloud‑native data‑lake architecture, detailing the business challenges, design choices, technology stack (Delta, Iceberg, Hudi), implementation steps, and the resulting gains in real‑time data access, cost reduction, and feature‑engineering support.
This article introduces the FlinkX framework for multi‑source heterogeneous data synchronization, detailing its background, core functions such as checkpoint‑based resume, metric monitoring, rate limiting, plugin architecture, cloud‑native K8s deployment, Hudi integration, and future roadmap, while also addressing common Q&A topics.
This talk by Tencent senior engineer Chen Liang covers Iceberg table format fundamentals, data lake ingestion, query processing, hidden partitioning, time‑travel, major features, optimization techniques such as compaction, bin‑packing, sorting and Z‑ordering, and outlines a future roadmap for improving performance and governance in big‑data environments.
This article explains why data lakes are essential for today’s analytics, outlines the three main user demands, defines data lakes, compares rule‑based and cost‑based optimizers, explores record‑oriented versus block‑oriented processing, and details StarRocks’ frontend‑backend architecture and benchmark results.
This article presents Tencent's data lake metadata governance practice, covering data lake fundamentals, the 3+2 architecture of storage, compute and unified metadata, multi‑tenant design, the re‑implemented Hive Metastore for online catalog, performance optimizations, and offline data‑governance capabilities.
This article explains the fundamentals of data lake analytics, compares optimization strategies such as rule‑based vs cost‑based and record‑oriented vs block‑oriented processing, describes StarRocks' lightweight frontend/backend architecture, and presents benchmark results that demonstrate its performance advantages over competing engines.
The article explains how Apache Hudi automatically manages file sizes to avoid small‑file issues, details key configuration parameters, provides a step‑by‑step example of file merging, and offers practical Spark tuning recommendations for optimal performance in data‑lake workloads.
The presentation outlines Alibaba Cloud's native data lake solution built on Apache Iceberg, covering data lake fundamentals, cloud migration challenges, Iceberg's architecture and features, real‑time ingestion with Flink, unified metadata management, security guarantees, and testing practices to ensure reliable, scalable big‑data analytics.
This article provides a step‑by‑step guide on integrating Apache Hudi with Hive and Presto, demonstrates core Hudi operations such as insert, upsert, delete, query, and Hive synchronization using Scala code, and shows how to manage Hudi tables through Spark SQL DDL/DML commands.
This article explains how Tencent leverages Apache Iceberg together with Flink to build a real‑time data lake pipeline, covering data ingestion, Iceberg's snapshot‑based read/write model, compaction and governance services, Z‑order based query optimization, performance results, and future roadmap.
Bilibili replaced its Hive‑Spark‑Presto ETL pipeline with a lakehouse built on Iceberg, using Magnus, Trino and Alluxio to unify a PB‑scale data lake and warehouse, adding Z‑Order sorting and indexing for fast multi‑dimensional queries while planning further schema and pre‑computation optimizations.
This article introduces NetEase's internally incubated data lake project Arctic, explains the concept of data lakes, outlines NetEase's specific requirements for a unified streaming‑batch platform, details Arctic's core architecture, storage strategy, data‑merge mechanisms, current achievements, and future development plans.
This article introduces object storage as a cost‑effective solution for massive unstructured data and reviews five open‑source platforms—LakeFS, Ceph, MinIO, OpenIO, and Apache Ozone—highlighting their features, scalability, and suitability for modern data‑lake and cloud‑native environments.
This article provides a comprehensive overview of Apache Hudi, covering its storage reliance on HDFS, core design principles, table architecture, timeline management, file and index structures, as well as detailed read and write workflows for both Copy‑On‑Write and Merge‑On‑Read table types.
This article explains how Kuashou tackled latency and efficiency problems in large‑scale data pipelines by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, its architecture, model design, handling of bursty updates, back‑fill scenarios, and operational safeguards.
