Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
The article explains why traditional physical‑table‑plus‑SQL pipelines no longer meet AI‑driven data needs and proposes a semantic‑engineered approach—semantic assets, a governed semantic layer, logical tables, and agents—to make data meaning executable, verifiable, and governable.
The article examines advanced data‑warehouse RAG challenges, explains why pure vector retrieval falls short, details a BM25‑plus‑vector hybrid approach, showcases AI‑driven metric platforms and data‑agent design, and outlines practical factors for building a robust data‑agent foundation.
This article (part 1) presents twelve high‑level interview questions from top tech firms covering the shift from AI‑Ready to Data‑Agent‑Ready data warehouses, AI‑centric metadata and lineage, lakehouse formats like Paimon, and key Iceberg features that empower AI‑driven analytics.
The article examines Apache Ossie's role as an incubating Apache project that defines a vendor‑neutral, AI‑ready semantic metadata standard, addressing semantic islands, improving AI Agent reliability, and enabling reusable, exchangeable business semantics across analytics, BI and data platforms.
The article outlines a practical framework for assessing data‑centric AI agents, highlighting challenges such as nondeterminism, black‑box behavior, and error amplification, and proposes concrete dimensions—result correctness, semantic consistency, query quality, security, and explainability—to ensure zero‑tolerance accuracy and reproducibility.
The article analyzes the May 2026 releases of Apache Iceberg V3 and Spark 4.0, explaining how Deletion Vectors, the native VARIANT type, and Spark Connect together transform semi‑structured data handling and real‑time upserts in lakehouse architectures.
This article analytically compares three open table‑format projects—Paimon, Hudi, and Iceberg—examining how each addresses multimodal data lake challenges such as massive volume, sparse access patterns, and combined scalar‑vector retrieval, and provides concrete feature breakdowns and selection guidance.
The article analyzes how AI coding has moved from assistance to partial automation, outlines production‑ready AI capabilities for data development, discusses rapidly advancing areas like cross‑table understanding and agent auto‑debugging, and examines the resulting blurring of job roles and the heightened importance of AI skills in interviews.
This article enumerates ten uncommon, compaction‑related problems encountered in large‑scale Paimon deployments, explains their root causes—such as RPC timeouts, snapshot expiration, file corruption, and write conflicts—and provides concrete configuration tweaks and operational steps to resolve each issue.
Apache Doris 4.0 introduces native vector indexes, built‑in AI functions, and hybrid search, turning the OLAP engine into an AI‑centric analytics hub; the article details the technical design, performance optimizations, and real‑world deployments at ByteDance, Squirrel AI, NetEase and a security vendor, highlighting storage savings, query speedups and reduced operational complexity.
