Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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.
This article summarizes the major Apache Flink 2.0 updates released in the first half of 2025, covering architecture separation, cloud‑native deployment, AI‑driven agents, SQL enhancements, data integration, operational tools, and performance optimizations for real‑time intelligent computing.
This article reviews common data lake interview questions—covering problem definition, Flink-to-Hudi row_number deduplication, retract streams, pipeline architecture optimizations, and read/write best practices—providing concise explanations and practical insights for candidates.
This article reviews the most effective Flink optimization techniques since 2022, including operator‑level TTL, mini‑batch processing, two‑phase aggregation, multi‑dimensional DISTINCT with FILTER, lookup join caching strategies, and TopN implementations, each rated with recommendation stars for production use.
From monolithic Java applications to Dubbo‑powered microservices, this article traces the evolution of backend development, highlights key milestones, and explains why mastering server‑side skills remains essential for data professionals in 2025 today.
The article reviews key topics from the FFA2025 Singapore conference, highlighting Flink’s new AI‑focused Agents framework, the breakthrough Flink 2.0 disaggregated state architecture, emerging lake storage solutions like Paimon, and the Fluss streaming table store, illustrating how big‑data platforms are evolving for AI workloads.
Despite the hype around Flink and AI models, Spark 4.0’s release brings a lightweight Python client, Spark Connect GA, enhanced SQL optimization, vectorized execution, and AI integration, reaffirming its leading position in the big‑data ecosystem while hinting at future challenges and innovations.
