Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article summarizes the major Apache Flink 2.0 updates released in the first half of 2025, covering architecture separation, cloud‑native deployment, AI‑driven agents, SQL enhancements, data integration, operational tools, and performance optimizations for real‑time intelligent computing.
This article reviews common data lake interview questions—covering problem definition, Flink-to-Hudi row_number deduplication, retract streams, pipeline architecture optimizations, and read/write best practices—providing concise explanations and practical insights for candidates.
This article reviews the most effective Flink optimization techniques since 2022, including operator‑level TTL, mini‑batch processing, two‑phase aggregation, multi‑dimensional DISTINCT with FILTER, lookup join caching strategies, and TopN implementations, each rated with recommendation stars for production use.
From monolithic Java applications to Dubbo‑powered microservices, this article traces the evolution of backend development, highlights key milestones, and explains why mastering server‑side skills remains essential for data professionals in 2025 today.
The article reviews key topics from the FFA2025 Singapore conference, highlighting Flink’s new AI‑focused Agents framework, the breakthrough Flink 2.0 disaggregated state architecture, emerging lake storage solutions like Paimon, and the Fluss streaming table store, illustrating how big‑data platforms are evolving for AI workloads.
Despite the hype around Flink and AI models, Spark 4.0’s release brings a lightweight Python client, Spark Connect GA, enhanced SQL optimization, vectorized execution, and AI integration, reaffirming its leading position in the big‑data ecosystem while hinting at future challenges and innovations.
After a weekend dive into Apache Hive’s official Wiki and GitHub, this article highlights Hive’s declining visibility compared to Spark and Flink, examines its 4.0 release’s major features—including Iceberg integration, enhanced ACID, cost‑based optimizer upgrades, and Ozone support—while reflecting on its role in modern data ecosystems.
This article summarizes how major tech firms such as Vivo, Shopee, Alibaba, and TikTok adopt Apache Paimon to unify batch and streaming data pipelines, improve latency, reduce costs, and optimize storage, highlighting key challenges, architectural solutions, and real‑world performance gains.
This semi‑annual report analyzes over 120 interview experiences to reveal current hiring trends, required skills, and strategic advice for data professionals targeting mid‑to‑senior positions in large‑scale companies during the first half of 2025.
This article compiles frequent problems encountered when using Paimon with Flink—such as small‑file generation, write‑performance bottlenecks, OOM/GC issues, file‑deletion conflicts, dimension‑table join slowness, and snapshot expiration—and provides practical configuration and optimization solutions.
