The article explains how Kuaishou partnered with Apache Hudi to overhaul its ODS‑based data lake, addressing latency, storage cost, and complexity for AI and BI workloads, detailing the evolution from mysql‑to‑hive to mysql‑to‑hudi 1.0 and 2.0, the resulting performance gains, cost savings, and future roadmap.
This article presents a complete guide to Hudi-based real-time data lake ingestion, covering overall data integration architecture, batch and streaming ingestion strategies, advanced table design, and practical recommendations for handling challenges such as deduplication, latency, partitioning, and performance optimization.
This article presents a comprehensive overview of a unified data lake architecture, evaluates three ingestion solutions, details real‑time ingestion optimizations for Flink‑Hudi pipelines, and describes how Kyuubi enables unified query access across multiple engines, offering practical guidance for large‑scale data processing.
Discover how GaussDB(DWS) HStore tables combine columnar storage with Delta capabilities to support high‑concurrency real‑time data ingestion, upsert operations, and ultra‑fast analytical queries, while offering full ACID consistency, strong compression, and practical configuration tips for developers.
ByteHouse evolved its real‑time import pipeline from a community ClickHouse architecture to a custom HaKafka engine and a cloud‑native design, addressing node failures, read‑write conflicts, scaling costs, and latency by introducing two‑level concurrency, memory tables, exactly‑once semantics, and robust fault‑tolerance.
This article details the motivation, architectural evolution, and technical implementations of ByteHouse's real‑time ingestion pipeline, covering internal business requirements, distributed‑system challenges, the custom HaKafka engine, memory‑table optimizations, and the transition to a cloud‑native design that delivers high availability, low‑latency, and exactly‑once semantics.
The article details Xiaomi's growth analytics platform evolution from a Lambda architecture using SparkSQL, Kudu, and HDFS to a streamlined MPP solution with Apache Doris, covering performance gains, real‑time data ingestion, query tuning, and operational improvements for large‑scale analytics.
The article explains how Apache Hudi enables near‑real‑time data ingestion from various sources, supports low‑latency analytics, provides incremental processing pipelines, and simplifies data distribution on Hadoop, improving efficiency and reducing operational complexity.
