Fanatics unified its fragmented data stack by building a StarRocks‑powered Lakehouse on Apache Iceberg, replacing Redshift, Snowflake, Athena, and Druid, which cut costs by up to 95%, delivered sub‑second dashboard queries on petabyte‑scale data, and enabled real‑time and historical analytics on a single platform.
The article examines how SRE and infrastructure teams are converging, why major infra vendors are acquiring observability assets, the rising cost pressures, and how OpenTelemetry combined with Apache Iceberg forms a new standard stack that AI‑driven incident response will rely on in the coming years.
This article collection showcases seven advanced data engineering solutions—from Tencent Cloud's Iceberg batch‑stream integration and Apache Gravitino metadata lineage to Xiaohongshu's Lakehouse evolution and multimodal AI data lake implementations—highlighting architectural innovations, performance optimizations, and real‑world deployment insights for modern big‑data platforms.
This article explains how Baidu’s next‑generation data platform Turing 3.0 integrates Apache Iceberg to solve the inefficiencies of the legacy MEG stack, detailing ecosystem components, migration strategies from Hive, table‑level optimizations, and future roadmap for high‑frequency, low‑latency analytics.
BiFang is a lake‑stream integrated storage engine that merges Apache Pulsar message‑queue capabilities with Iceberg data‑lake features, providing a single unified data store with full‑incremental queries, sub‑second visibility, exactly‑once semantics, and seamless integration with Flink, Spark, and StarRocks for both real‑time analytics and batch processing.
This article examines how Apache Iceberg’s snapshot‑based ACID transactions, logical‑physical partition evolution, and COW/MOR update modes enable efficient real‑time data lake ingestion, and demonstrates AutoMQ’s Kafka‑to‑Iceberg Table Topic solution that simplifies schema management, reduces latency, and cuts operational costs.
In a panel discussion, experts explain how data‑lake‑warehouse integration, columnar formats like Apache Iceberg, and emerging variant types enable efficient feature engineering, support large‑language‑model workloads, and provide flexible vector storage, thereby driving the evolution of AI from traditional ML to the GenAI era.
In a panel discussion, experts from Kuaishou, Ping An, and Datastrato explain how data lake architectures, columnar storage formats like Apache Iceberg, and vector‑enabled lake formats are enhancing feature management, supporting generative AI workloads, and accelerating machine‑learning pipelines.
This tutorial walks you through the fundamentals of Apache Iceberg, its architecture and key features, explains why it’s advantageous for lakehouse workloads, and provides a step‑by‑step Docker‑Compose setup to integrate Iceberg with StarRocks for fast, ACID‑compliant analytics on real‑world taxi data.
This article explores the evolution of data lakes for AI, discusses the challenges of AI-era data management, introduces Apache Iceberg and its architecture, demonstrates PyIceberg-based AI training and inference pipelines, and presents vector table designs with LSH indexing and performance optimizations.
The article examines the rapid rise of lakehouse architecture, its market momentum, core components—including storage, metadata, table formats, and compute layers—compares Iceberg, Hudi, and Delta Lake, discusses the shift from HDFS to object storage, and outlines the strategic importance of lakehouses for AI-driven data management and future data infrastructure trends.
This article presents a comprehensive overview of modern Data+AI data lake challenges and solutions, covering the evolution of data lakes, an introduction to Apache Iceberg, practical use of PyIceberg for AI training and inference pipelines, and advanced vector table and indexing techniques for efficient similarity search.
Databricks announced the acquisition of Tabular, the company founded by the original creators of Apache Iceberg, aiming to integrate Delta Lake and Iceberg into a unified, open lakehouse architecture that enhances format compatibility, reduces data silos, and supports AI workloads.
Apache Iceberg offers a modern, ACID‑compliant table format for data lakes with features like hidden partitions and schema evolution, while StarRocks provides high‑performance query acceleration, metadata caching, and distributed planning to address Iceberg’s latency challenges, enabling seamless lake‑warehouse integration and real‑time analytics.
Xiaohongshu’s data‑warehouse team integrated Apache Iceberg‑based data‑lake techniques into its existing warehouse, replacing the legacy Hive/Spark stack with global sorting, Z‑order, and upsert‑enabled tables, which cut query latency by up to 90 %, boosted data freshness by 50 %, slashed storage costs by 83 % and saved tens of thousands of GB‑hours of compute daily.
This article examines the design of lakehouse storage systems by comparing Delta Lake, Apache Hudi, and Apache Iceberg, focusing on metadata management, Merge‑On‑Read mechanisms, and a series of query and write performance optimizations with real‑world EMR case studies.
This article introduces the background, challenges, and cloud‑native solutions of lakehouse architecture, explains Apache Iceberg’s open table format and its cloud‑native features, details Amoro’s management and self‑optimizing capabilities, showcases three real‑world cloud migration cases, and outlines future development plans.
This article introduces Amoro, a lakehouse management platform built on Apache Iceberg, explains why Webex adopted it to overcome Hive limitations, details its AWS GlueCatalog and S3 integration with DynamoDB lock management, and provides step‑by‑step Helm‑based deployment instructions on Kubernetes.
This article describes how Shanghai Steel Union leveraged Amoro Mixed Iceberg on top of Apache Iceberg to create a unified streaming‑batch lakehouse, addressing small‑file and upsert challenges, simplifying architecture, improving data freshness, and providing a scalable solution for real‑time and batch analytics.
iQIYI’s data‑middle‑platform team built a four‑zone data lake—raw, product, work, and sensitive—integrated with unified ODS/DWD/MID layers, a metadata catalog, and self‑service tools, leveraging HDFS, Hive/Iceberg, Spark/Trino, and Flink, migrated to Apache Iceberg for real‑time freshness, and now aims to further streamline modules and adopt new technologies.
This guide explains the cloud‑native lakehouse concept, outlines its advantages and challenges, compares lake‑table projects such as Iceberg, and provides a step‑by‑step AWS deployment of Apache Iceberg and Amoro—including environment setup, AMS installation, catalog configuration, optimizer launch, data ingestion with Flink, and query verification with Spark.
This article examines the architecture and core design of lakehouse storage systems, compares the metadata handling and Merge‑On‑Read mechanisms of Delta Lake, Apache Hudi, and Apache Iceberg, and presents practical performance‑optimization techniques and real‑world case studies on Alibaba Cloud EMR.
This article explains the evolution of data lakes, compares public‑cloud and private‑cloud implementations, outlines key technical features, presents three real‑world scenarios, details the selection and inner workings of Apache Iceberg versus Hive, and showcases multiple production use cases at iQIYI.
This article describes how ByteDance tackled petabyte‑scale feature storage by adopting Apache Iceberg, detailing the problem background, design choices, implementation of COW and MOR back‑fill strategies, performance optimizations, and future plans such as lake‑cold‑layering and materialized views.
This article introduces the Arctic lakehouse management system built on Apache Iceberg, explains Iceberg’s core principles, format versions, and real‑world implementations at NetEase, and details Arctic’s automated table optimization, governance workflows, and future development plans.
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.
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 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 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 details Xiaohongshu's data platform architecture and three real‑time lake initiatives—log ingestion, CDC ingestion, and lake analysis—showcasing how Apache Iceberg, Flink, and custom shuffling algorithms solve small‑file and cross‑cloud challenges while enabling schema evolution and future multi‑cloud optimizations.
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 examines the technical differences and feature sets of Apache Hudi, Delta Lake, and Apache Iceberg, highlighting incremental pipelines, concurrency control, merge‑on‑read storage, partition evolution, multi‑mode indexing, and real‑world use cases to help practitioners choose the most suitable lakehouse solution for their workloads.
This article details Xiaohongshu's data platform engineering, describing how Apache Iceberg is leveraged for real‑time data lake ingestion, CDC pipelines, multi‑cloud storage, small‑file mitigation, schema evolution, and future plans across storage, compute, and management within a big‑data ecosystem.
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 explains how Apache Iceberg’s DataSkipping technique can lose efficiency when many filter columns are used, and presents a data‑organization optimization using space‑filling curves and Z‑Order to improve query I/O, details the OPTIMIZE implementation, and shares performance benchmark results and future plans.
This talk explains how Apache Iceberg’s DataSkipping can lose efficiency with many filter columns, and presents a data‑organization redesign using space‑filling curves and Z‑Order to improve query I/O, detailing the OPTIMIZE syntax, implementation steps, performance benchmarks, and future roadmap.
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 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.
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 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 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 presents Tencent's practical experience of constructing a real‑time data warehouse by integrating Apache Flink with Apache Iceberg, covering background pain points of traditional Lambda architectures, Iceberg's table format and capabilities, Flink‑Iceberg sink design, small‑file handling, and future roadmap for a unified streaming‑batch data lake.
This article explains how to construct a lake‑house architecture using Apache Iceberg, detailing the migration from Hive, Flink‑SQL integration, proxy user support, CDC handling, copy‑on‑write sinks, and the resulting benefits for near‑real‑time data visibility and unified batch‑stream processing.
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 Apache Iceberg’s table‑format design, compares it with Hive’s limitations, details its snapshot‑based architecture and metadata handling, and describes how NetEase Cloud Music leveraged Iceberg to dramatically improve large‑scale log processing performance and stability.
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.
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.
This article compares Apache Hudi, Apache Iceberg, and Delta Lake, examining their storage formats, platform compatibility, update performance, concurrency guarantees, and integration with lakeFS to help readers choose the most suitable solution for their data lake use case.
This article introduces Apache Iceberg as an open table format for massive analytic datasets, explains its underlying concepts such as schema, partitioning, statistics, and read/write APIs, compares it with Hive Metastore, outlines its ACID commit process, highlights the performance and operational advantages for big‑data workloads, and previews upcoming community features.
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 talk presents NetEase's practical experience with Impala, covering its core architecture, new features in version 3.x, integration with Apache Iceberg, a custom management platform, profiling and statistics enhancements, as well as future plans involving Kubernetes, Alluxio caching and pre‑computation strategies.
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.
At Youzan’s Big Data Technology Salon, over 100 attendees heard leaders from Youzan, NetEase Yishu, and Didi discuss cost governance, Apache Iceberg data lakes, large‑scale Flink real‑time computing, and data‑driven growth strategies, highlighting practical implementations, savings of millions and tools for merchant empowerment.
The Youzan Big Data Technology Salon brought together Youzan, NetEase and Didi to share practical approaches for cutting data‑infrastructure costs, building an Apache Iceberg‑based data lake, scaling Flink real‑time workloads, and creating a data‑driven growth platform that leverages tracking, A/B testing and analytics.
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 how Apache Flink combines with Apache Iceberg to build unified stream‑batch data lake solutions, covering data lake fundamentals, architectural layers, classic business scenarios, reasons for choosing Iceberg, streaming ingestion design, and upcoming community enhancements.
This article examines the three leading open‑source data‑lake projects—Delta Lake, Apache Iceberg, and Apache Hudi—by outlining their origins, core problems they address, key features, and a detailed seven‑dimension comparison to help practitioners choose the most suitable solution for their scenarios.
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.
