Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article examines how Xiaohongshu replaced its costly Lambda architecture with a real‑time lakehouse built on Iceberg, Paimon, Spark, and StarRocks, achieving minute‑level latency, higher data quality, lower resource consumption, and dramatically faster query performance.
The article examines how emerging data‑infrastructure trends, multi‑modal databases like Neon, Supabase, and ClickHouse, and the convergence of OLTP, OLAP, and vector search are reshaping AI workloads, introducing the Data Warebase concept that unifies warehouse and database capabilities to meet modern AI workflow demands.
This guide outlines a step‑by‑step three‑month preparation plan—including resume building, project showcase, interview mindset, mock sessions, and offer negotiation—to help candidates secure high‑paying big‑data positions at top companies.
This article explains the key monitoring items of the Flink Web UI, details task topology, operator and system metrics, checkpoint and log inspection, and provides two practical solutions—custom metrics and distributed tracing—to measure and visualize full‑chain latency in Flink jobs.
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.
