The article analyzes the challenges of moving traditional on‑premise big‑data platforms to the cloud, outlines the cost‑saving opportunities of cloud‑native data lakes, presents three core architectural principles, and reviews Tencent Cloud's data lake product suite and its key use cases.
This article provides an extensive overview of data warehouse and data lake concepts, their architectures, differences, components, and implementation considerations, covering topics such as OLTP/OLAP, ETL processes, data quality, cloud solutions, and the role of data platforms in modern enterprises.
The article details Apache Iceberg 0.11.0's core enhancements—including partition changes, SortOrder, extensive Flink and Spark integrations, CDC/Upsert support, hash‑based write distribution to reduce small files, and upcoming 0.12.0 roadmap—while providing practical SQL and API examples for data‑lake practitioners.
This article provides a comprehensive overview of Apache Iceberg, covering its origins, key features, practical Spark and Flink code examples, notable deployments at Alibaba and Tencent, and its future role as a universal table format for big‑data analytics.
This article presents Tencent's experience of constructing a real‑time data warehouse by integrating Apache Flink with Apache Iceberg, covering background pain points, Iceberg's table format and capabilities, Flink‑Iceberg streaming and batch processing, practical implementations, and future roadmap for data‑lake acceleration.
This article provides a comprehensive overview of data lakes and data warehouses, explaining their definitions, architectures, differences, and practical use cases, while also covering related concepts such as OLTP/OLAP, ETL processes, data governance, and modern lakehouse solutions.
This article provides a comprehensive overview of data warehouses, data lakes, and data middle platforms, explaining their definitions, architectures, functions, differences, and the value they bring to enterprises, while also addressing common misconceptions and related concepts such as data marts and data swamps.
This article examines the 20‑year evolution of big data architectures, contrasts data lakes and data warehouses, explores their respective strengths and challenges, and details Alibaba Cloud’s lake‑warehouse (lakehouse) solution that unifies storage, metadata, and compute for enterprise‑grade analytics and AI workloads.
This article introduces JD's real‑time data warehouse evolution, outlines the limitations of traditional Lambda‑based warehouses, compares open‑source lake formats (Delta, Hudi, Iceberg), explains Delta Lake's transaction‑log architecture and read flow, and demonstrates how a unified batch‑stream development model simplifies data processing and improves reliability.
This article explains how Apache Flink integrates with data lake architectures, especially using Apache Iceberg as a table format, to enable real‑time streaming ingestion, CDC processing, near‑real‑time lambda architectures, and future enhancements like automatic file merging and row‑level deletes.
This article explains the concept and benefits of data lakes, outlines the storage and acceleration challenges they pose, presents an ideal checklist for selecting a data lake solution, and evaluates Alibaba Cloud's JindoFS against that checklist, highlighting its capabilities for big‑data and AI workloads.
This article examines the core requirements of data lakes and provides an in‑depth comparison of three major open‑source solutions—Apache Hudi, Apache CarbonData, and Delta Lake—highlighting their architectures, ACID support, query capabilities, and suitability for various real‑time and batch use cases.
Lyft transformed its legacy data pipeline by designing a cloud‑native, Flink‑based near real‑time analytics platform that ingests billions of events, writes Parquet files to S3, leverages Presto for interactive queries, and implements multi‑stage non‑blocking ETL, fault‑tolerant back‑fill, and extensive performance optimizations.
Alibaba Cloud’s Data Lake Analytics (DLA) tackles the growing complexity of data scenarios by offering cloud‑native, serverless solutions for data lake management, massive metadata construction, and high‑performance Spark and Presto engines, while addressing challenges such as high entry barriers, stability, and multi‑tenant isolation.
This article introduces Apache Hudi, explaining its core concepts, design principles, table architecture, write and compaction mechanisms, and the three query modes that enable efficient batch and incremental processing on modern data lakes.
Delta Lake adds ACID transaction support, schema enforcement, data versioning, and unified batch‑and‑stream processing to Apache Spark‑based data lakes, addressing reliability, quality, performance, and update challenges of traditional data lake architectures.
This article traces the 20‑year evolution of big‑data technologies, compares data lakes and data warehouses, explains their complementary strengths, and presents Alibaba Cloud’s lakehouse solution that unifies storage and compute to deliver flexible, performant, and cost‑effective analytics for enterprises.
This article examines the pain points of traditional data warehouse platforms, explains the core concepts and advantages of the Iceberg data lake table format, compares it with Metastore, reviews the current Iceberg community ecosystem, and details NetEase’s practical integration with Hive, Impala, and Flink to improve ETL efficiency and support unified batch‑stream processing.
Apache Hudi is an open‑source data‑lake framework that leverages Spark to ingest, manage, and incrementally query large analytical datasets on HDFS‑compatible storage, offering features such as timeline management, copy‑on‑write and merge‑on‑read tables, and support for snapshot, incremental, and read‑optimized queries across engines like Hive, Spark SQL and Presto.
This article introduces Apache Hudi, explaining its storage types, query views, timeline feature, typical use cases such as near‑real‑time ingestion and incremental pipelines, and provides a step‑by‑step Scala/Spark quick‑start guide with code examples for compiling, inserting, updating, querying, and syncing data to Hive.
This comprehensive article explains what a data lake is, outlines its core characteristics and reference architecture, compares major cloud providers' data‑lake offerings, presents typical advertising and gaming use cases, and proposes a practical, agile process for building and operating a data lake.
This article compares data warehouse, data lake, data platform, and data middle platform, explaining their definitions, architectures, strengths, limitations, and use‑case differences, and provides tables that highlight how each solution handles structured and unstructured data, governance, flexibility, and business value.
Apache Hudi is an open‑source data‑lake framework that leverages Spark and Hadoop‑compatible storage to provide efficient ingestion, incremental processing, and multiple query modes such as snapshot, incremental, and read‑optimized for large analytical datasets.
This article explains the origins and market growth of data lakes, compares them with traditional data warehouses, showcases major implementations like Amazon Galaxy and Club Factory, and provides practical guidance on choosing open‑source or commercial cloud solutions to construct a data lake efficiently while minimizing risk.
An in‑depth comparison of the three leading open‑source data lake platforms—Delta Lake, Apache Iceberg, and Apache Hudi—examines their origins, core challenges they address, key features, and performance across seven evaluation dimensions to guide practitioners in selecting the optimal solution for their workloads.
The article explains what a Data Lake is, its origins, key characteristics, cost advantages, potential pitfalls such as becoming a data swamp, and compares it with traditional data warehouses, highlighting when each approach is most appropriate.
This article compares the design goals, data‑lake table formats—Hudi, Iceberg, and Delta—highlighting their common reliance on meta files and their distinct strengths for upserts, analytics, and unified streaming‑batch processing in modern big‑data environments.
The article explains Alibaba's data middle platform—its definition, methodology, organizational structure, key tools, and how it differs from traditional data warehouses and data lakes—while highlighting its role in supporting scalable, business‑centric data services and digital transformation.
This article explains the origins and motivations of Delta Lake, details its ACID transaction support, schema enforcement, metadata handling, versioning, and unified batch‑and‑stream processing, and provides a step‑by‑step Maven and Spark code tutorial for creating, updating, and querying Delta tables.
Despite recent market turbulence and negative headlines, Hadoop's revenue continues to grow, driven by cloud migration, evolving storage solutions, and increasing adoption of related projects like Spark and Kafka, positioning it as a leading data‑lake technology.
The article explains how Alluxio, a memory‑speed virtual distributed file system, acts as a virtual data lake to unify access to structured and unstructured big‑data across heterogeneous storage systems, offering on‑demand fast local access, intelligent caching, reduced storage costs, and enterprise‑grade security and fault tolerance.
This article explores Hadoop’s role in the big‑data era, detailing its architecture, core components such as HDFS, YARN, MapReduce, Ozone and Submarine, the challenges of trillion‑scale data, and why its scalability, cost efficiency, and a mature ecosystem give it a competitive edge.
This article explains the core differences between data lakes and data warehouses, their respective strengths, and how they complement each other to support both exploratory analytics and routine business reporting.
The article explains how Alluxio provides a virtual distributed file system that acts as a "virtual data lake," enabling unified, high‑performance access to structured and unstructured data across heterogeneous storage back‑ends while reducing storage costs through intelligent caching and eliminating the need for permanent data copies.
The article reviews recent big-data trends—from Hadoop’s evolution and Spark’s in-memory advances to emerging storage like Ozone—while detailing data-warehouse models, query-optimizer techniques, and cloud-native architectures that integrate diverse data sources, enabling scalable, AI-ready analytics and modern data-lake capabilities.
This article explains why BI is essential for big data platforms, outlines the value hierarchy of data, details the Hadoop‑based analysis workflow, and provides step‑by‑step guidance for constructing both pure Hadoop and hybrid Hadoop‑Spark analytics architectures.
This article explores the evolution of storage technology, the rise of software‑defined distributed unified storage like UMStor, and the Hadapter solution that enables high‑performance, compute‑storage separation for big‑data and cloud environments, highlighting real‑world deployments and performance insights.
The article reports on UCloud's subsidiary presenting at ArchSummit 2018 in Shenzhen, detailing the evolution to the digital era, challenges of PB‑scale data storage, and their solution using NFS‑Ganesha, Hadapter, and UMStor to achieve efficient big‑data‑on‑cloud performance and a data‑lake model.
This article explains how enterprises can build a scalable data analytics platform on Hadoop by outlining the multi‑layer architecture, storage options, data synchronization methods, and ETL/offline computation techniques, while highlighting practical component choices such as Hive, HBase, Spark, and Oozie.
This article explains the definition of big data, its characteristic 3Vs, common data sources, supporting IT infrastructure, key technologies such as Hadoop and Spark, specialized databases, required skills, and several practical business use cases.
This article examines the evolution of data lake architectures, comparing the compute‑storage fusion model of HDFS, the compute‑storage separation approach of S3A on Ceph, and a new UMStor Hadapter plugin that aims to unite their strengths while addressing performance bottlenecks.
The article explains how Ctrip's big‑data platform introduced Alluxio to isolate real‑time Spark Streaming jobs from HDFS NameNode maintenance, reduce NameNode pressure, improve Spark SQL performance, and provide a unified storage layer across multiple HDFS clusters.
This article explains the origins, core features, logical architecture, and advantages of data lakes, contrasts them with traditional data warehouses, outlines a modern data architecture that combines lakes and warehouses, and introduces the DCE intelligent data lake platform with practical Q&A.
