Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article explains how to improve Paimon's query performance by optimizing table schemas, storage settings, query parameters, and index designs, covering table mode choices, partitioning, file formats, parallelism, batch reads, and various index types such as Bloom filters and clustering indexes.
The article announces the Spring AI 1.0 release, provides the Maven BOM dependency, compares Spring AI with LangChain4j for Java AI development, and outlines upcoming integration, performance, and observability improvements for both frameworks amid rapid large‑model advancements.
This article shares an interviewee's experience discussing core Flink interview questions, including typical resource allocation for large online tasks, common problems such as data, performance, stability, and resource issues, and the monitoring practices for clusters and tasks, while also containing a brief self‑promotion.
Apache Gravitino is an open‑source metadata service platform that provides a unified, high‑performance, geographically distributed metadata lake, enabling end‑to‑end data governance, multi‑engine access, and direct management of both structured and unstructured data assets across diverse systems.
This article reviews common interview questions for data development roles, covering Spark stage partitioning and optimization, criteria for evaluating data warehouses, Flink's handling of late data, and provides practical answers and resources to help candidates deliver standout responses.
Redis 8.0 GA introduces a suite of new data structures, AGPLv3 licensing, multithreaded query execution, horizontal scaling, and significant performance gains, positioning Redis as a more powerful in‑memory database for big‑data, AI, and real‑time applications.
The article reports Xiaohongshu’s cancellation of the big‑small week schedule and non‑compete clause, then provides a collection of big‑data interview questions—including data governance, Iceberg metadata management, and lakehouse production best practices—along with concise answers and resources for candidates.
This article shares three high‑quality interview questions from a JD big‑data interview, covering practical Spark tuning, Flink's exactly‑once guarantees in production, and Paimon's asynchronous merge mechanism, and explains how to answer them with real‑world scenarios.
This article introduces Spring AI, a Java‑based AI framework inspired by LangChain, explains its core capabilities such as model management, inference, extension and Spring ecosystem integration, and provides step‑by‑step installation, configuration, sample code, and guidance for creating MCP services.
This article presents a selection of challenging interview questions frequently asked of big‑data candidates—covering long‑tail task identification, the role of Apache Paimon, handling underperforming cluster nodes, data scale limits, and large‑model fundamentals—along with concise explanations and resources for further study.
