This article analytically compares three open table‑format projects—Paimon, Hudi, and Iceberg—examining how each addresses multimodal data lake challenges such as massive volume, sparse access patterns, and combined scalar‑vector retrieval, and provides concrete feature breakdowns and selection guidance.
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.
The Apache Paimon 0.9.0 release introduces production‑ready Branch support, native Iceberg compatibility, a caching catalog for faster OLAP queries, improved Bucketed Append tables with reduced small‑file issues, and full DELETE/UPDATE/MERGE‑INTO capabilities for Append tables, making the system more usable and efficient.
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 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 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 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 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 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.
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 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 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.
