This guide explains how to leverage Alibaba Cloud Log Service (SLS) SPL within the Flink SLS Connector to push down row filtering and column projection, reducing network traffic and compute load while enabling real‑time log analytics using Flink SQL.
The article reviews highlights from Flink Forward 2021, describing how real‑time computing is spreading across traditional industries, the unstoppable move toward Flink SQL, the emergence of Flink ML, and the vision of a streaming warehouse built on Flink Dynamic Table technology.
This article details NetEase Yanxuan's real-time computing platform development from 2017 to present, covering its architecture, Flink‑SQL development environment, service‑oriented deployment, resource optimization, cloud‑native migration, comprehensive data governance, and future plans for stream‑batch integration and intelligent job diagnostics.
This article details NetEase Yanxuan's evolution of a real‑time data platform from 2017 to present, covering background, current scale, layered architecture, Flink‑SQL development IDE, service‑oriented task execution, resource‑optimizing deployment modes, cloud‑native migration, comprehensive data governance, and future batch‑stream integration plans.
This article presents Car Home's experience building a real‑time materialized view system on Apache Flink, detailing system analysis, problem decomposition, a global‑version‑based CDC algorithm, its implementation as a Flink connector, practical deployment results, and remaining challenges such as clock dependency and state size.
This article introduces KSQL as a lightweight streaming SQL engine for Apache Kafka, explains its architecture and core concepts of streams and tables, and provides step‑by‑step deployment instructions, command‑line examples for creating streams/tables, querying data, and managing persistent queries.
This article introduces Apache Flink's Streaming SQL, explains its theoretical foundations such as the table‑stream relationship and watermark semantics, describes the platform's practical enhancements—including source/sink wrappers, built‑in functions, and native Retract Stream support—and showcases several real‑time computation examples.
This article details the technical evolution of 58.com’s real‑time computing platform, describing the shift from Storm and Spark Streaming to Apache Flink, the design of the one‑stop Wstream platform, its large‑scale deployment, stability measures, SQL streaming capabilities, task migration, diagnostics, optimizations, and future plans.
This article details Zhihu's evolution of its real-time data warehouse, covering the 1.0 version built on Spark Streaming, the 2.0 upgrade using Flink Streaming SQL, architectural layers, ETL processes, and future directions such as streaming SQL platformization and automated result validation.
