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This article explores OpenLake's evolution as a data lake platform for AI, covering the transition from Hive to modern lake formats like Iceberg and Paimon, performance benchmarks, metadata management advances, intelligent storage optimization, and the integration of multimodal support with the Lance file format.
Coupang’s AI platform replaces costly data‑copy steps with a distributed cache that automatically pulls data from a central lake, boosts GPU utilization across regions, cuts storage and operational expenses, and speeds up model training by up to 40% while simplifying deployment via Kubernetes.
DuckDB's DuckLake is an open‑standard, SQL‑driven data lake and catalog format that simplifies lakehouse architecture by managing metadata in a database while storing data in scalable Parquet files, offering multi‑user collaboration, time‑travel queries, and MIT licensing.
This article explains how Huawei's terminal cloud adopts the Apache Iceberg table format to efficiently manage large-scale datasets, detailing its architecture, feature engineering, merge operations, LSM-based storage, schema versioning, AB testing support, catalog enhancements, and future roadmap for full lifecycle data governance.
The Kuaishou‑hosted Apache Hudi Asia technical salon gathered over 230 attendees and featured seven experts from Kuaishou, Meituan, TikTok, Huawei, JD and others, who shared best practices, architecture designs, and performance optimizations for large‑scale data lake applications across AI, BI, and real‑time workloads.
This article presents Huolala’s end‑to‑end implementation of Flink CDC for real‑time data capture, detailing the business background, reasons for selecting Flink CDC over Canal, component comparisons, production‑level platform enhancements, data‑lake integration, validation methods, and future directions for unified data ingestion.
The Apache Hudi Asia technical salon held in Beijing on March 29 gathered over 230 on‑site participants and 16,000 online viewers, featuring expert talks from leading Chinese tech companies that showcased real‑world Hudi implementations, performance optimizations, and future roadmap for data‑lake technologies.
The first Apache Hudi Asia Summit in Beijing attracted over 230 attendees, featuring technical discussions on data lake optimization and case studies from companies like Fastly and Meituan.
This article presents a comprehensive exploration of using Apache Paimon and Flink to design lake tables that support minute‑level latency, low cost, and unified batch‑stream processing for advertising data, covering schema design, partitioning strategies, performance trade‑offs, cost analysis, and operational best practices.
Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.
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.
This article presents Douyin Group’s ByteLake, a heavily customized Apache Hudi‑based data lake table framework, detailing its core concepts, metadata services, write and read optimizations, operational challenges, a fully managed table management service, and its integration with the Amoro open‑source platform.
This article presents JD Advertising's engineering experience with Apache Doris, describing the evolution from a data‑lake cold‑data solution to a native cold‑hot tiering approach, detailing performance regressions after upgrading to Doris 2.0, and outlining a series of optimizations for query speed, CPU and memory usage, schema‑change efficiency, and automated data migration and restoration.
Tencent's advertising team replaced a traditional HDFS‑Hive warehouse with an Apache Iceberg‑based data lake, adding primary‑key tables, multi‑stream merging, adaptive compaction, and Spark SPJ optimizations to achieve minute‑level feature update latency, 10× back‑fill speed, and up to 60% storage savings.
The 8th Tongcheng Travel Big Data Technology Salon in Suzhou featured four expert talks covering Tencent Cloud’s Meson Spark engine, near‑line computing for travel itineraries, a Flink‑based real‑time risk control system, and Apache Paimon’s latest lake‑warehouse innovations, followed by a data‑driven business perspective session.
Bilibili migrated its massive user‑behavior, commercial AI training, and database synchronization pipelines from Hive and Kafka to an Iceberg‑based streaming‑batch architecture, using Flink and the Magnus optimizer to achieve minute‑level freshness, reduce CPU and memory usage by about 20‑22 %, save roughly 3.55 M CNY annually, and dramatically improve query latency and join performance.
This article presents Bilibili's end‑to‑end exploration of a streaming‑batch unified data pipeline built on Apache Iceberg, detailing the original and iterated architectures for massive user behavior transmission, online AI training, DB synchronization, and dimension‑join, along with performance gains, cost savings, and future plans.
iQIYI integrates Alluxio with its QBFS multi‑AZ unified scheduling system, automatically caching hot tables, applying table‑level policies, page‑level storage and AZ‑aware worker selection, which together cut cross‑zone traffic, halve query latency, achieve up to 20× I/O speedup and a three‑fold overall performance boost.
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.
This article surveys the evolution of data‑lake storage acceleration, compares different architectural stages, analyzes why acceleration is needed for AI and big‑data scenarios, and details the key techniques—metadata acceleration, read/write speedup, and end‑to‑end workflow optimization—used to overcome performance and cost challenges.