Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
An overview of Paimon 1.0’s milestone improvements, focusing on the optimized local Lookup performance, the new sort‑lookup‑store based PFile key‑value format, its four‑part structure, and detailed write and read procedures that enhance large‑scale dimension table joins.
The article discusses how large language model tools such as Cursor, DeepSeek, and Doubao are increasingly assisting code writing, SQL translation, job‑failure analysis, and documentation in data‑development workflows, while also reshaping job requirements and creating new opportunities for skilled developers.
This article presents a detailed overview of Douyin Group's Data Asset Management Platform, focusing on the evolution, architecture, modeling, metrics, and application scenarios of its large‑scale data lineage system, and outlines future directions for full‑coverage, fine‑grained lineage capabilities.
This article shares a data‑engineering student’s personal experience—from a misaligned operations role to mastering big‑data technologies, building a portfolio, crafting a targeted resume, and navigating multi‑stage interviews—offering concrete advice and a structured learning roadmap for aspiring data professionals.
This article explains Apache Paimon's snapshot expiration mechanism, comparing synchronous and asynchronous execution modes, their advantages and drawbacks, and how table properties control expiration to balance data consistency, performance, and back‑pressure in large‑scale data processing systems.
This article explains how Apache Paimon guarantees data timeliness and consistency through snapshot files, two‑phase commit, and configurable expiration policies, and it outlines practical optimization and cleanup techniques for maintaining efficient storage and query performance.
This article provides a comprehensive overview of Apache Paimon, covering its real‑time lake ingestion, unified stream‑batch processing, table types (primary‑key and append‑only), LSM‑tree storage, bucket mechanisms, merge‑engine options, compaction strategies, concurrency control, consumption methods, tag management, data cleanup, and system tables for big‑data workloads.
This article explains how Spark ≥ 3.3 introduces Storage Partitioned Join (SPJ) to avoid costly shuffle operations when joining partitioned V2 source tables such as Apache Iceberg, detailing the required conditions, configuration settings, practical code examples, and various join scenarios including mismatched partitions and data skew.
The article explains the three detailed-granularity fact table types—transaction, periodic snapshot, and cumulative snapshot—detailing their purposes, design principles, and comparative usage, and offers a simplified interpretation to help data engineers choose the appropriate fact table for data warehouse modeling.
The article reviews the major directions of Flink 2.0—including compute‑storage separation, a new Materialized Table for unified batch‑stream processing, and deeper integration with Paimon for streaming warehouses—while offering a cautious perspective on their practical impact and migration challenges.
