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 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.
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.
This article explains the Lakehouse architecture, its required features, the evolution of big‑data stacks, and provides a detailed, hands‑on guide for constructing a Lakehouse using RocketMQ (Connector & Stream) and Apache Hudi, including configuration, deployment, and sample code.
This article explains the concepts of data warehouses, data marts, and data lakes, illustrates why the lakehouse model emerged to bridge storage and compute, and outlines its key benefits such as flexibility, scalability, reduced redundancy, and unified analytics for modern enterprises.
The article examines Amazon’s Intelligent Lakehouse architecture, tracing its evolution from early data‑lake‑warehouse integrations to a modern, serverless, secure, and AI‑enhanced platform that unifies data storage, governance, and analytics to lower big‑data costs and boost agility.
This article provides a comprehensive overview of the Lakehouse paradigm, tracing its origins from traditional data warehouses and data lakes, comparing architectures, detailing core components such as Delta Lake and Iceberg, and illustrating practical cloud implementations and future directions.
This comprehensive article explains what a data lake is, its core characteristics, reference architecture, major cloud vendor implementations, typical use cases such as advertising and gaming, step‑by‑step construction guidance, and future trends in cloud‑native big‑data platforms.
The article explains what a data lake is, its architecture and core capabilities, compares it with data warehouses, discusses its value and challenges, and reviews major open‑source platforms such as Delta Lake, Iceberg, and Hudi.
The article explains what a data lake is, compares various vendor definitions, outlines its four essential components, describes three evolutionary architecture stages from self‑hosted Hadoop to cloud‑native storage‑compute separation, and discusses the benefits and challenges of adopting data lake solutions in modern big‑data platforms.
This article examines the strengths and weaknesses of Apache Iceberg, explains why Tencent selected it over alternatives, details Tencent’s own enhancements and integration with Flink, Spark, and other engines, and shares multiple real‑world implementations for building enterprise‑grade real‑time data lakes.
This article explains how Kuaishou tackled late data scheduling, costly synchronization, and inefficient back‑fills by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, and step‑by‑step implementation to achieve fast, fresh, and scalable data processing.
This article explores the evolution of data lakes, compares major cloud providers' lake architectures, introduces the emerging lakehouse concept, and provides a step‑by‑step Flink‑Iceberg implementation—including dependencies, catalog setup, table creation, checkpointing, and Kafka ingestion—demonstrating practical big‑data streaming solutions.
This article explains the origins and purpose of data lakes, outlines four key construction goals, details common ingestion methods and storage technologies, and describes essential governance practices such as cataloging, data quality, and regulatory compliance.
This article details ByteDance's implementation of an exabyte‑scale data lake using Apache Hudi, covering scenario requirements, engine selection, functional support, schema management, extensive performance tuning, and future directions, while also noting recruitment opportunities within the team.
This article provides an in-depth overview of data lake concepts, definitions, and essential features, followed by detailed case studies of enterprise data lake implementations and comparative analysis of leading data lake table formats—Iceberg, Hudi, and Delta Lake—highlighting their architectures, capabilities, and trade‑offs.
This article examines JD's shift from a traditional Lambda‑based data warehouse to a Delta Lake‑powered real‑time data lake, detailing the challenges of legacy architectures, the evaluation of open‑source table formats, Delta Lake's core mechanisms, and the resulting simplified batch‑stream development workflow.
This article introduces OPPO's self‑developed data lake storage system CBFS, covering the fundamentals of data lake storage, the multi‑layer CBFS architecture, its core technologies such as metadata management and erasure coding, and future directions for large‑scale, low‑cost data analytics.
The article explains what a data lake is, its origins, key characteristics such as storing all raw data, flexible access, and low‑cost storage, compares it with traditional data warehouses, discusses advantages, common criticisms, and the types of users who can benefit from it.
This article shares Qunar's experience using Flink together with Apache Iceberg 0.11 to address real‑time data warehouse challenges, covering background pain points, Iceberg architecture, solutions for Kafka data loss and Hive latency, and optimization practices such as small‑file handling, sorting, and checkpoint management.
This article examines the challenges of using Kafka, Flink, and Hive for real‑time data warehousing, introduces Apache Iceberg 0.11 as a solution, details its architecture, query planning, Flink integration, code examples, optimization techniques, and summarizes the benefits for large‑scale data processing.
This article explains the fundamental differences between databases, data warehouses, and data lakes, describes how they complement each other, and introduces the Lake House concept that integrates transactional and analytical workloads using cloud services such as Amazon S3, Redshift Spectrum, and Athena.
This article reviews the advantages of Apache Iceberg for data lake storage, details Tencent’s custom optimizations and integration with Flink and Spark, and shares multiple real‑world implementations that demonstrate how Iceberg improves data consistency, reduces small‑file overhead, and enables near‑real‑time analytics in large‑scale big‑data environments.
This article explains the concept of data lakes, outlines a four‑layer open‑source architecture, presents several classic Flink‑Iceberg use cases, details why Iceberg was chosen, and describes the design of Flink’s streaming sink and upcoming community roadmap.
At the Techo TVP Developer Summit, leaders discussed how big‑data tools are evolving beyond perceived bottlenecks toward cloud‑native, specialized platforms and data lakes, emphasized open‑source collaboration, highlighted China’s capacity to spawn a Snowflake‑like service, and offered guidance on emerging real‑time, GPU‑accelerated analytics and multidisciplinary data‑career paths.
The talk outlines a next‑generation cloud‑native data lake that leverages elastic Kubernetes compute, object‑storage, and Apache Iceberg to cut costs 3‑10× while boosting performance, and presents Tencent’s Data Lake Compute and Data Lake Fabric solutions that address scalability, reliability, and operational challenges through serverless, unified, multi‑engine architecture.
This article explains the concept of a data lake—its origin in 2011, how it differs from traditional databases and data warehouses, its core characteristics such as raw data storage, on‑demand computing, and schema‑on‑read, as well as its advantages, challenges, architectural components, and future outlook within the big‑data ecosystem.
This article describes how Linkflow migrated mutable customer data from MySQL to an Apache Hudi data lake using Debezium‑in‑Flink CDC, addressing challenges such as snapshot resumability, partial updates, row‑key merging, schema evolution, indexing, and concurrent writes to achieve minute‑level data freshness and improved offline processing performance.
This article explains the motivations behind Apache Iceberg, its design principles such as snapshot and MVCC, compares it with Hive, and describes how NetEase Cloud Music adopted Iceberg to improve metadata handling, query performance, and operational stability for massive daily log data.
The article explains the concept and advantages of data lakes, outlines the major storage and acceleration challenges they face, provides a checklist for ideal data‑lake solutions, and details how Alibaba Cloud's JindoFS addresses those challenges with object‑storage‑based, high‑performance, scalable features.
This article walks through downloading the required Flink and Hudi components, building Hudi for Scala 2.12, and demonstrates step‑by‑step how to create, populate, query, and update Hudi tables in both batch and streaming modes using Flink SQL, complete with code snippets and result screenshots.
This article describes how Soul's data engineering team replaced nightly batch ETL with real-time Delta Lake ingestion on EMR, detailing the motivations, comparative analysis of Delta, Hudi, Iceberg, the implementation architecture, encountered issues such as data skew and schema evolution, and the solutions adopted to improve performance and reliability.
This article examines the evolution of data lakes, outlines the challenges enterprises face with massive, heterogeneous data, and details Tencent Cloud’s native data lake architecture and its serverless Data Lake Compute service, highlighting performance, cost‑efficiency, and future development directions.
This article presents a comprehensive overview of data lake implementations, detailing Huawei's production‑scene platform, a real‑time financial data lake architecture using Kafka, Flink and Iceberg, and Soul's Delta Lake practice with Spark, Hive, and custom ETL tools, highlighting design choices, processing flows, and operational considerations.
This article explains what a data lake is, outlines its key characteristics, and compares three major data lake frameworks—Iceberg, Hudi, and Delta Lake—highlighting their architectures, features, and trade‑offs for large‑scale data storage and processing.
