This guide explains how to perform incremental queries on Hudi tables by configuring Hive synchronization, using Spark SQL both programmatically and via pure SQL, and leveraging Flink SQL in batch and streaming modes, with detailed parameter settings and code examples.
The article explains how to archive Kafka real‑time data to Hive using either Flink SQL, which quickly creates partitioned ORC tables but requires timezone handling, or Flink DataStream for more complex pipelines, and offers best‑practice guidance on data quality, system complexity, security, and performance.
This article explains the challenges of early real‑time data pipelines, introduces a layered real‑time warehouse architecture, provides step‑by‑step Flink SQL code for building a demo warehouse, and covers comprehensive data governance, quality metrics, lifecycle management, and naming conventions for production‑grade big‑data systems.
RocketMQ-Streams targets massive data, high‑filtering, lightweight windowed computations with a lightweight, high‑performance design that runs on as little as 1 CPU core and 1 GB RAM, offering 2‑5× speed gains over traditional big‑data engines and supporting Flink‑compatible SQL, UDFs, and cloud‑native deployment.
This article examines the evolution of big‑data architectures toward cloud‑native, real‑time processing, and provides a detailed comparison of dimension‑table and result‑table options—including MySQL, Redis, and Alibaba Cloud Tablestore—along with their performance, cost, and scalability characteristics for Flink SQL workloads.
Tencent Cloud Oceanus, a computing service powering internal apps like WeChat and external partners such as Bilibili, scales to over 30,000 cores handling 5 PB daily and 500,000 jobs, and tackles Flink SQL’s syntax, function and operational limits with table‑valued functions, incremental and enhanced tumble windows, and caching‑based retraction optimization that cuts downstream data volume up to 30× and improves join performance by about 20 %.
This article provides an in‑depth overview of Flink SQL, covering its origins, the latest 1.7.0 and 1.8.0 enhancements, the underlying programming model, common operators and built‑in functions, and a complete end‑to‑end example that analyzes NBA scoring‑leader data using Flink SQL.
