Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article explains why vector databases are essential for AI-driven unstructured data, surveys the current landscape of professional and extended vector databases, and provides a detailed technical comparison between Milvus and OceanBase, covering architecture, features, performance, and product positioning.
The 2025 release of Apache Spark 4.0 brings a comprehensive overhaul—including default ANSI SQL mode, full SQL scripting support, a new Real‑Time streaming mode, adaptive query execution, dynamic memory management, and GPU‑accelerated MLlib—significantly boosting performance, reliability, and developer productivity across big‑data workloads.
The 2025 Apache Paimon release brings major performance boosts, AI‑centric multimodal storage, deeper streaming‑batch integration, and broader engine compatibility, detailing query and write optimizations, memory management tweaks, and a unified lake format for structured and unstructured data.
The article explains how the rule‑based NLQ component of ChatBI replaces large language models with a detailed dictionary‑driven architecture, using a custom Metrics Query Language (MQL) to transform natural‑language business questions into accurate SQL, highlighting its stability, low cost, transparency, and limitations compared to LLM solutions.
The 2025 Flink release introduces a disaggregated state management architecture for cloud‑native elasticity, AI‑driven Flink Agents with LLM, Memory and Tool support, Delta Join and VARIANT type for semi‑structured data, adaptive batch execution, incremental checkpoints, high‑speed network optimizations, and new SQL and Process Table Functions, reshaping real‑time analytics.
This article explains how small‑file issues in Paimon's streaming data lake architecture degrade system stability and query speed, and presents practical parameter‑tuning, table‑level settings, asynchronous compaction, and monitoring techniques to mitigate those problems.
Apache Kafka 4.1.0 introduces native queue semantics, a new Streams rebalancing protocol, multi‑version Connect plugins, a revamped consumer‑group protocol, enhanced transaction safety, and numerous client, monitoring, and security improvements, offering a comprehensive upgrade over the 4.0 release.
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.
