Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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.
This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.
This article, based on a meetup presentation, details Alibaba Cloud's MaxCompute platform—its evolution, serverless architecture, AI integration, distributed Python framework, Object Table, near‑real‑time processing, and intelligent warehouse features—addressing the challenges of data warehouses in the Data+AI era.
This article summarizes how the Doris‑Paimon lakehouse architecture leverages Doris' high‑performance OLAP engine to accelerate lake queries, provides a unified data analysis gateway, supports unified data integration, and enables open, layered data modeling for modern big‑data workloads.
This article introduces the concept of AI-powered Data Agents, outlines their key features and use cases across enterprise analytics, data governance, and intelligent customer service, and reviews recent implementations from Google, Alibaba Cloud, and ByteDance, highlighting their impact on modern data-driven workflows.
This article explains the Model Context Protocol (MCP) as a standard for LLM‑data integration, describes Retrieval‑Augmented Generation (RAG) techniques to reduce hallucinations, and introduces vector databases like Milvus that store high‑dimensional embeddings for efficient AI retrieval tasks.
This article analyzes why Elasticsearch struggles with large‑scale, complex real‑time analytics and demonstrates how Apache Doris’s MPP, columnar storage, and native SQL support provide a cost‑effective, high‑performance alternative, illustrated with detailed enterprise case studies.
Apache Kafka 4.0 introduces a groundbreaking KRaft architecture that removes ZooKeeper, a revamped consumer‑group protocol that dramatically speeds up rebalancing, a new queue mode for point‑to‑point messaging, upgraded Java requirements, streamlined APIs, and numerous performance and security enhancements, reshaping both development and operations for large‑scale streaming workloads.
This article reviews how major tech firms such as Alibaba, Tencent, ByteDance, and Kuaishou tackled lakehouse challenges—including architecture fragmentation, cost, scalability, and complex multimodal data—by adopting real‑time lakehouse solutions like Flink + Paimon, Iceberg + StarRocks, Hudi + LAS, and Doris + Alluxio, and outlines the resulting performance and cost gains.
The article analyzes how the rapid rise of open‑source large‑model AI in 2025 is reshaping the data development profession, urging developers to transition from specialized data‑engineer roles to full‑stack AI data engineering skills such as distributed computing, lake‑house architectures, and model tuning.
