Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article reviews how Paimon and Doris combine to solve unified storage, data visibility, and performance challenges in modern lakehouse architectures, detailing their complementary features, integration capabilities, and real‑world use cases from leading companies.
The Apache Doris 3.1 release strengthens lake‑house capabilities with major upgrades to the VARIANT data type, vertical compaction, inverted index storage, new tokenizers, enhanced materialized view support for Iceberg/Paimon/Hudi, and numerous query‑performance optimizations such as faster partition pruning and dynamic partition clipping, offering smoother handling of thousands of columns and large‑scale semi‑structured data.
This article introduces several noteworthy open‑source AI projects—including Google’s Gemini CLI, the AI‑Engineering‑Hub learning repository, and OpenAI’s GPT‑5 coding examples—providing URLs, key features, and visual previews to help developers quickly explore and adopt cutting‑edge AI tools.
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partial‑update mode to incorrect sequence fields, snapshot retention issues, concurrent bucket writes, and outdated Spark versions—and how to prevent each problem.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.
