这是我自己的Flink中文社区翻译稿存储仓库，用于提供给需要朋友进行二次创作。同时提供Flink一些课外的相关知识文档供大家学习

Elastic data processing with Apache Pulsar and Apache Flink

Flink Tutorial Project

Http Connector for Apache Flink. Provides sources and sinks for Datastream , Table and SQL APIs.

This repository provides Scotty, a framework for efficient window aggregations for out-of-order Stream Processing.

flink 流处理源码分析

🌟 Examples of use cases that utilize Decodable, as well as demos for related open-source projects such as Apache Flink, Debezium, and Postgres.

Apache Flink Guide

Examples of Flink on Azure

Collection of code examples for Amazon Managed Service for Apache Flink

基于Flink的批流处理实战案例

Different ways to process data into Cassandra in realtime with technologies such as Kafka, Spark, Akka, Flink

Streaming machine learning using PyTorch, Flink, and ONNX

Sample project for Apache Flink with Streaming Engine and JDBC Sink

Amazon Managed Service for Apache Flink Benchmarking Utility helps with capacity planning, integration testing, and benchmarking of Amazon Managed Service for Apache Flink applications.

A Flink applcation that demonstrates reading and writing to/from Apache Kafka with Apache Flink

🚀 Traffic Sentinel: A scalable IoT system using Fog nodes and Apache Flink to process 📷 IP camera streams, powered by YOLO for intelligent 🚗 traffic monitoring on highways. 🛣️

Kinesis Data Analytics Blueprints are a curated collection of Apache Flink applications. Each blueprint will walk you through how to solve a practical problem related to stream processing using Apache Flink. These blueprints can be leveraged to create more complex applications to solve your business challenges in Apache Flink.

A sample implementation of stream writes to an Iceberg table on GCS using Flink and reading it using Trino

Demo Flink and Kafka project to show how to react on tracking events in real-time and trigger offer for customer engagement based on campaign configurations. The project also utilizes the Broadcast State Pattern in order to update the rules (campaigns) at runtime without restarting the project, using a dedicated, low-frequency, Kafka topic.