Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
Learn the six typical scenarios that cause data loss when writing to Paimon—ranging from checkpoint failures and misconfigured partial‑update mode to incorrect sequence fields, snapshot retention issues, concurrent bucket writes, and outdated Spark versions—and how to prevent each problem.
A former communications engineer shares his step‑by‑step transition into big data, detailing study challenges, resume crafting, interview strategies, and the lessons learned from navigating job offers in both small towns and major tech firms.
This article explains what materialized views are, outlines their advantages such as query acceleration, lightweight ETL, and lake‑warehouse integration, classifies them by sync mode, table count, and refresh strategy, and highlights their limitations and best‑practice recommendations for production use.
This guide explains Apache Flink 2.1's new AI model DDL and ML_PREDICT function, showing step‑by‑step how to create, manage, and invoke AI models in streaming SQL, configure resources with Little's Law, and process risk‑assessment results in real time.
After conducting 16 one‑on‑one mock interviews and debriefs, we identified five recurring issues—from lacking a holistic project view and poor expression to sloppy resume formatting, underutilizing large‑language models, and neglecting regular self‑review—that candidates should address to improve their interview performance.
This article summarizes how enterprises adopt Apache Doris for lakehouse integration, near‑real‑time data warehouses, federated queries, and performance optimization, highlighting practical experiences, connector support, scheduling capabilities, and comparisons with Elasticsearch.
An interviewee recounts a challenging first interview that focused on Flink resource configuration, late data handling, and offline data warehouse design, and shares practical advice on attitude, thorough preparation, emphasizing real project storytelling, and post‑interview review to continuously improve performance.
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.
