Apache Flink

flink learning blog. http://www.54tianzhisheng.cn/ 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例，还有 Flink 落地应用的大型项目案例（PVUV、日志存储、百亿数据实时去重、监控告警）分享。欢迎大家支持我的专栏《大数据实时计算引擎 Flink 实战与性能优化》

专注大数据学习面试，大数据成神之路开启。Flink/Spark/Hadoop/Hbase/Hive...

SQL stream processing, analytics, and management. PostgreSQL simplicity, unrivaled performance, and seamless elasticity. 🚀 10x more productive. 🚀 10x more cost-efficient.

Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.

Flink CDC is a streaming data integration tool

Flink 中文视频课程（持续更新...）

CSDN博客专家程序员欣宸的github，这里有六百多篇原创文章的详细分类和汇总，以及对应的源码，内容涉及Java、Docker、Kubernetes、DevOPS等方面

🚁🚀基于Flink实现的商品实时推荐系统。flink统计商品热度，放入redis缓存，分析日志信息，将画像标签和实时记录放入Hbase。在用户发起推荐请求后，根据用户画像重排序热度榜，并结合协同过滤和标签两个推荐模块为新生成的榜单的每一个产品添加关联产品，最后返回新的用户列表。

A data integration framework

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

DataSphereStudio is a one stop data application development& management portal, covering scenarios including data exchange, desensitization/cleansing, analysis/mining, quality measurement, visualization, and task scheduling.

Dinky is a real-time data development platform based on Apache Flink, enabling agile data development, deployment and operation.

大数据学习，从零开始学习大数据，包含大数据学习各阶段学习视频、面试资料

LakeSoul is an end-to-end, realtime and cloud native Lakehouse framework with fast data ingestion, concurrent update and incremental data analytics on cloud storages for both BI and AI applications.

🔥🔥 AllData大数据产品是可定义数据中台，以数据平台为底座，以数据中台为桥梁，以机器学习平台为中层框架，以大模型应用为上游产品，提供全链路数字化解决方案。全新会员商业版 X 微信群：https://docs.qq.com/doc/DVHlkSEtvVXVCdEFo

深圳地铁大数据客流分析系统🚇🚄🌟

A Flexible, Fast, Federated(3F) SQL Analysis Middleware for Multiple Data Sources

基于开源的flink，对其实时sql进行扩展；主要实现了流与维表的join，支持原生flink SQL所有的语法

Apache Paimon is a lake format that enables building a Realtime Lakehouse Architecture with Flink and Spark for both streaming and batch operations.